The Sensitivity of Bank Net Interest Margins and Profitability to 

Credit, Interest-Rate, and Term-Structure Shocks 

Across Bank Product Specializations 

Gerald Hanweck 

Professor of Finance 

School of Management 

George Mason University 

Fairfax, VA 22030 

ghanweck@gmu.edu 

and 

Visiting Scholar 

Division of Insurance and Research 

FDIC 

Lisa Ryu 

Senior Financial Economist 

Division of Insurance and Research 

FDIC 

lryu@fdic.gov 

January 2005 

Working Paper 2005-02 

The authors wish to thank participants at the FDIC’s Analyst/Economists Conference, October 

7–9, 2003, and at the Research Seminar at the School of Management, George Mason 

University, for helpful comments and suggestions. The authors would also like to thank Richard 

Austin, Mark Flannery, and FDIC Working Paper Series reviewers for their comments and 

suggestions. All errors and omissions remain the responsibility of the authors. The opinions 

expressed in this paper are those of the authors and do not necessarily reflect those of the FDIC 

or its staff. 
 

The Sensitivity of Bank Net Interest Margins and Profitability to 

Credit, Interest-Rate, and Term-Structure Shocks 

Across Bank Product Specializations 

Abstract 

This paper presents a dynamic model of bank behavior that explains net interest margin 

changes for different groups of banks in response to credit, interest-rate, and term-structure 

shocks. Using quarterly data from 1986 to 2003, we find that banks with different product-line 

specializations and asset sizes respond in predictable yet fundamentally dissimilar ways to these 

shocks. Banks in most bank groups are sensitive in varying degrees to credit, interest-rate, and 

term-structure shocks. Large and more diversified banks seem to be less sensitive to interest-rate 

and term-structure shocks, but more sensitive to credit shocks. We also find that the composition 

of assets and liabilities, in terms of their repricing frequencies, helps amplify or moderate the 

effects of changes and volatility in short-term interest rates on bank net interest margins, 

depending on the direction of the repricing mismatch. We also analyze subsample periods that 

represent different legislative, regulatory, and economic environments and find that most banks 

continue to be sensitive to credit, interest-rate, and term-structure shocks. However, the 

sensitivity to term-structure shocks seems to have lessened over time for certain groups of banks, 

although the results are not universal. In addition, our results show that banks in general are not 

able to hedge fully against interest-rate volatility. The sensitivity of net interest margins to 

interest-rate volatility for different groups of banks varies across subsample periods; this varying 

sensitivity could reflect interest-rate regime shifts as well as the degree of hedging activities and 

market competition. Finally, by investigating the sensitivity of ROA to interest-rate and credit 

shocks, we have some evidence that banks of different specializations were able to price actual 

1 
 

and expected changes in credit risk more efficiently in the recent period than in previous periods. 

These results also demonstrate that banks of all specializations try to offset adverse changes in 

net interest margins so as to mute their effect on reported after-tax earnings. 

2 
 

 

1. Introduction 

The banking industry has undergone considerable structural change since the early 1980s 

as the legislative and regulatory landscape governing the industry has evolved. The structural 

changes, in turn, have had significant effects on the degree of market competition and the scope 

of products and services provided by banks as well as significant effects on the sources of bank 

earnings. Despite these developments, credit and interest-rate risks still largely account for the 

fundamental risks to bank earnings and equity valuation as well as to the contingent liability 

borne by the FDIC insurance funds. The relative importance of credit and interest-rate risks for 

bank earnings and the FDIC’s contingent liability has varied over time in response to changes in 

the macroeconomic, regulatory, and competitive environments.1 

Despite the rising importance of fee-based income as a proportion of total income for 

many banks, net interest margins (NIM) remain one of the principal elements of bank net cash 

flows and after-tax earnings.2 As shown in figure 1, except for very large institutions and credit 

card specialists, noninterest income still remains a relatively small and usually more stable 

component of bank earnings. As a result, despite earnings diversification, variations in net 

interest income remain a key determinant of changes in profitability for a majority of banks. 

However, research in the area of bank interest-rate risk and the behavior of NIM has been largely 

limited since the late 1980s, when the savings and loan crisis brought the issue of interest-rate 

risk to the fore. Understanding the systematic effects of changes in interest-rate and credit risks 

on bank NIM will likely help the FDIC better prepare for variations in its contingent liability 

associated with adverse developments in the macroeconomic and financial market environment. 

1

 For example, Duan et al. (1995) posit that interest-rate risk dominated the volatility of the FDIC’s contingent 

liabilities in the early 1980s—the time of high interest-rate volatility—whereas credit risk became the leading factor 

in the late 1980s and early 1990s, as interest-rate volatility subsided. 

2

 Throughout this paper, net interest margins are defined as annualized quarterly net interest income (interest income 

less interest expense) as a ratio of average earning assets. 

3 
 

 

The objective of this paper is twofold. First, this paper develops a new dynamic model of 

bank NIM that reflects the managerial decision process in response to credit, interest, and termstructure shocks. We focus our analysis primarily on variations in net interest margins, although 

bank managers adjust their portfolios in order to manage reported after-tax profit rather than net 

interest margins. However, given that the variation in net interest income is the key determinant 

of earnings volatility for many banks, understanding the degree to which these shocks affect the 

bank’s net interest income would help us identify the channels through which they could affect 

overall bank profitability and the responses bankers make to manage reported profitability. The 

degree to which the bank can change the portfolio mix and/or hedge in the short term would 

determine the magnitude of the effect of interest-rate changes and other shocks on bank 

profitability. 

Our second objective is to use a large set of data, consisting of quarterly bank and 

financial market data from first quarter 1986 to second quarter 2003, to evaluate the model. In 

addition, we investigate whether the sensitivity to shocks varies across diverse bank groups on 

the basis of their product-line specializations as well as different regulatory regimes. We focus 

on the effects of three key legislative changes on bank NIM during the sample period: the 

Depository Institutions Deregulation and Monetary Control Act (DIDMCA) of 1980, which set 

in motion the phasing out of the Regulation Q ceilings on deposits; the Federal Deposit 

Insurance Corporation Improvement Act (FDICIA) of 1991; and the Riegle-Neal Interstate 

Banking and Branching Efficiency Act (Riegle-Neal) of 1994, which became effective in July 

1997.3

 These pieces of legislation have likely changed the sensitivity of bank NIM to credit, 

interest-rate, and term-structure shocks, for they spurred price competition for deposits that 

3

 See FDIC (1997) for a detailed discussion of the legislative and regulatory history of the banking crisis of the 

1980s and early 1990s. 

4 
 

reduced volatility in bank lending, improved the capital positions of banks, allowed geographic 

and earnings diversification, and changed the general competitive landscape. No empirical study 

to date has investigated the effects of these legislative changes on the behavior of bank NIM. 

Empirical evidence and casual observation reinforce the view that banks with different 

product-line specializations tend to have distinctive business models and corresponding riskmanagement practices and characteristics. In addition, banks with different product-line 

specializations also face different competitive landscapes, with some bank groups experiencing 

progressively more intense competition than others. To maximize profitability and enhance bank 

value, bankers attempt to choose a product mix that best fits their perceived markets and 

managerial expertise, thus gaining a competitive advantage for lending, investing, and raising 

funds through deposits. For most banks, the choice of market means some degree of 

specialization in particular product lines and geographic locations. The bank portfolios 

associated with these various product lines are likely to exhibit different degrees of sensitivity to 

interest-rate and credit-risk changes. The extent to which bankers can offset adverse interest-rate 

changes and hedge adverse credit-risk changes will depend on the principal product line of the 

bank, the flexibility of the portfolio in responding to change, and the cost and availability of 

hedges for a particular portfolio. 

Our empirical results show that net interest margins associated with some bank portfolios 

derived from specializing in certain product lines are considerably more sensitive to interest-rate 

changes than others. The magnitude of these effects depends on the repricing composition of 

existing assets and liabilities: banks that have a higher proportion of net short-term assets in their 

portfolio experience a greater boost in their NIM as interest rates rise. We find that changes in 

bank net interest margins are typically negatively related to interest-rate volatility but positively 

5 
 

related to increases in the slope of the yield curve. Changes in the yield spread have significant 

and lingering effects on NIM for many bank groups, but the effects are particularly notable for 

mortgage specialists and small community banks. We find that, for most bank groups, after-tax 

earnings are less sensitive to interest-rate changes than NIM are, but the degree of sensitivity 

differs among banks with different product-line specialties. 

We find that bank NIM are negatively related to an increase in realized and expected 

credit losses, particularly among banks specializing in commercial-type loans (i.e., commercial 

and industrial loans and commercial real estate loans). We posit that this inverse relationship 

between realized credit risk, as indicated by an increase in nonperforming loans, and net interest 

margins exists because, in the short run, risk-averse bank managers reallocate their funds to less 

default-risky, lower-yielding assets in response to an increase in the credit risk of their portfolios. 

This response is reinforced by bank examiners, who encourage banks to reduce their exposure to 

risky credits when loan quality is observed to be deteriorating. Banks’ net interest margins are 

positively related to a size-preserving increase in high-yielding, and presumably higher-risk, 

loans. We generally find that the estimated parameters of the models differ by subperiod for 

banks with different product-line specialties in ways that are statistically and economically 

meaningful. 

This paper extends the existing literature on NIM in three important respects. First, we 

develop a dynamic behavioral model of variations in NIM in response to market shocks that 

more closely resembles the actual decision-making process of bank managers than existing 

models. Second, by treating the banking industry as inherently heterogeneous (which we do by 

dividing banks into groups based on their product-line specializations), we are able to proxy 

broad differences in business models and managerial practices within the banking industry, and 

6 
 

identify groups of banks that are most sensitive to credit, interest-rate, and/or term-structure 

shocks. Finally, we are able to test the importance of shifts in regulatory regime in behavioral 

differences across subperiods for the same group of banks. 

The rest of the paper is organized as follows: section 2 reviews the literature relating to 

interest effects on bank net interest margins; section 3 presents a theoretical model of bank 

behavior in response to interest-rate shocks; section 4 discusses the data, the empirical variables, 

and the empirical specifications for the model; section 5 presents the results of both the full 

sample period and the subsample periods; and section 6 concludes the paper. 

2. Literature Review 

Despite significant regulatory concern paid to the interest-rate risk that banks face (OCC 

[2004]; Basel Committee on Banking Supervision [2004]), research on a key component of 

earnings that may be most sensitive to interest shocks—namely, bank net interest margins—has 

been limited thus far, particularly for U.S. banks. With a few exceptions discussed in this 

section, there has been little published research on the effects of interest-rate risk on bank 

performance since the late 1980s. Theoretical models of net interest margins have typically 

derived an optimal margin for a bank, given the uncertainty, the competitive structure of the 

market in which it operates, and the degree of its management’s risk aversion. The fundamental 

assumption of bank behavior in these models is that the net interest margin is an objective to be 

maximized. In the dealer model developed by Ho and Saunders (1981), bank uncertainty results 

from an asynchronous and random arrival of loans and deposits. A banking firm that maximizes 

the utility of shareholder wealth selects an optimal markup (markdown) for loans (deposits) that 

minimizes the risks of surplus in the demand for deposits or in the supply of loans. Ho and 

7 
 

Saunders control for idiosyncratic factors that influence the net interest margins of an individual 

bank, and derive a “pure interest margin,” which is assumed to be universal across banks. They 

find that this “pure interest margin” depends on the degree of management risk aversion, the size 

of bank transactions, the banking market structure, and interest-rate volatility, with the rate 

volatility dominating the change in the pure interest margin over time. 

Allen (1988) extends the single-product model of Ho and Saunders to include 

heterogeneous loans and deposits, and posits that pure interest spreads may be reduced as a result 

of product diversification. Saunders and Schumacher (2000) apply the dealer model to six 

European countries and the United States, using data for 614 banks for the period from 1988 to 

1995, and find that regulatory requirements and interest-rate volatility have significant effects on 

bank interest-rate margins across these countries. 

Angbazo (1997) develops an empirical model, using Call Report data for different size 

classes of banks for the period between 1989 and 1993, incorporating credit risk into the basic 

NIM model, and finds that the net interest margins of commercial banks reflect both default and 

interest-rate risk premia and that banks of different sizes are sensitive to different types of risk. 

Angbazo finds that among commercial banks with assets greater than $1 billion, net interest 

margins of money-center banks are sensitive to credit risk but not to interest-rate risk, whereas 

the NIM of regional banks are sensitive to interest-rate risk but not to credit risk. In addition, 

Angbazo finds that off-balance-sheet items do affect net interest margins for all bank types 

except regional banks. Individual off-balance-sheet items such as loan commitments, letters of 

credit, net securities lent, net acceptances acquired, swaps, and options have varying degrees of 

statistical significance across bank types. 

8 
 

 

 

Zarruk (1989) presents an alternative theoretical model of net interest margins for a 

banking firm that maximizes an expected utility of profits that relies on the “cost of goods sold” 

approach. Uncertainty is introduced to the model through the deposit supply function that 

contains a random element.4

 Zarruk posits that under a reasonable assumption of decreasing 

absolute risk aversion, the bank’s spread increases with the amount of equity capital and 

decreases with deposit variability. Risk-averse firms lower the risk of profit variability by 

increasing the deposit rate. Zarruk and Madura (1992) show that when uncertainty arises from 

loan losses, deposit insurance, and capital regulations, a higher uncertainty of loan losses will 

have a negative effect on net interest margins. Madura and Zarruk (1995) find that bank interestrate risk varies among countries, a finding that supports the need to capture interest-rate risk 

differentials in the risk-based capital requirements. However, Wong (1997) introduces multiple 

sources of uncertainty to the model and finds that size-preserving increases in the bank’s market 

power, an increase in the marginal administrative cost of loans, and mean-preserving increases in 

credit risk and interest-rate risk have positive effects on the bank spread. 

Both the dealer and cost-of-goods models of net interest margins have two important 

limitations. First, these models are single-horizon, static models in which homogenous assets 

and liabilities are priced at prevailing loan and deposit rates on the basis of the same reference 

rate. In reality, bank portfolios are characterized by heterogeneous assets and liabilities that have 

different security, maturity, and repricing structures that often extend far beyond a single 

horizon. As a result, assuming that bankers do not have perfect foresight, decisions regarding 

loans and deposits made in one period affect net interest margins in subsequent periods as banks 

face changes in interest-rate volatility, the yield curve, and credit risk. Banks’ ability to respond 

4

 Uncertainty in the bank’s deposit supply function is modeled as D* = D(RD ) + μ where RD is the interest rate on 

deposits and μ is a random term with a known probability density function. 

9 
 

to these shocks in the period t is constrained by the ex ante composition of their assets and 

liabilities and their capacity to price changes in risks effectively. In addition, the credit cycle and 

the strength of new loan demand determine the magnitude of the effect of interest-rate shocks on 

banks’ earnings. In this regard, Hasan and Sarkar (2002) show that banks with a larger lending 

slack, or a greater amount of “loans-in-process,” are less vulnerable to interest-rate risk than 

banks with a smaller amount of loans in process. Empirical evidence, using aggregate bank loan 

and time deposit (CD) data from 1985 to 1996, indicates that low-slack banks indeed have 

significantly more interest-rate risk than high-slack banks. The model also makes predictions 

regarding the effect of deposit and lending rate parameters on bank credit availability that were 

not empirically tested with aggregate data. 

The second important limitation of both the dealer and cost-of-goods models of net 

interest margins is that they treat the banking industry either as being homogenous or as having 

limited heterogeneous traits based only on their asset size. However, banks with distinct 

production-line specializations usually differ in terms of their business models, pricing power, 

and funding structure, all of which likely affect net interest margin sensitivity to interest-rate and 

other shocks. For instance, in the 1980s and early 1990s, credit card interest rates were typically 

viewed as “sticky” or insensitive to market rates, a view suggesting imperfect market 

competition (Ausubel [1991]; Calem and Mester [1995]). This view would imply that net 

interest margins of credit card banks, as a group, would be significantly less sensitive to interestrate shocks than other banks. Furletti (2003) documents notable changes in credit card pricing 

due to intense competition over the past decade; however, it is not clear how these changes have 

affected credit card specialists’ sensitivity to interest-rate and other shocks. In comparison, 

mortgage lenders, as a group, have a balance sheet with a significant mismatch in the maturity of 

10 
 

their assets and liabilities, and they are therefore more likely to be sensitive to changes in the 

yield curve. 

3. A Model of Bank Behavior 

Discussed in this section is a model of the effects of interest-rate and credit risk changes 

using the mismatching of asset and liability repricing frequencies. The model is a standard 

approach to evaluating changes in NIM due to changes in interest rates and credit quality as 

loans that are passed-due or charged off are essentially repriced in the current period. 

3.1 Interest-Rate Changes 

The model of bank behavior relating to net interest margins used in this paper assumes 

that at each period a bank can significantly but not completely choose the amount of its 

investment in assets and liabilities of different repricing frequencies, given past choices that are 

immutable. Admittedly, this is a fuzzy statement as to the choices available to a bank, but banks 

have a moderate degree of control over their asset mix in the short run (from quarter to quarter) 

by purchasing or selling assets of different repricing frequencies. As suggested above, banks’ 

choices of principal product-line specializations will determine the market conditions they face 

that may limit their ability to make rapid asset portfolio adjustments. The same is true for bank 

liabilities. Bankers can pay them early, deposits can be received and withdrawn at random, and 

some of them, like federal funds and repurchase agreements, are under the control of the bank 

and can be changed overnight. 

In contrast to banks’ ability to make portfolio adjustments, banks have little control over 

market interest-rate changes and interest-rate volatility. When contracts on assets or liabilities 

11 
 

 

are negotiated, banks may, through market power, be able to set levels or markups (markdowns) 

over index rates such as LIBOR, but are unable to control index rate changes. In addition, we 

assume that markups are contractually fixed in the short run. Furthermore, banks are unable to 

change their chosen product-line specializations in the short run, so such changes are strategic 

options only. 

In our modeling of bank responses to credit and interest-rate risks, we assume that banks 

are most interested in achieving the best after-tax profit performance they can in order to provide 

shareholders with maximum value. Maximizing shareholder value in a dynamic context, 

however, is a daunting problem and requires considerable judgment. Not only do bank managers 

have to choose the optimal financial service product mix (product-line specialization, in this 

study) and geographic diversification, but they also need to set the lending rate and fees, hedge 

credit quality and volatility changes, manage their liability structure, and gauge the moods of the 

equity and debt markets to favorable or unfavorable news so as to increase or protect shareholder 

value. Given these underlying conditions regarding banks’ motivations and their ability to 

change their portfolios and their positions as interest-rate takers, we assume that banks operate 

such that they will change their portfolio mix only to increase profits and maximize shareholder 

value over a 12-month horizon. As discussed above, the net interest margin is the major source 

of net income for most banks, and therefore a strategy of maximizing its value in the short run 

may be a reasonable proximate goal for achieving maximum bank profits in the short run. If 

risk-neutral pricing were prevalent in financial markets, banks would all price loans in a similar 

way, and short-run maximization of the expected value of net interest margins would be a proper 

bank objective.5 However, banks can do better. They can make decisions as to the timing of 

5

 As pointed out in the introduction, banks in general have been increasing fee income as a way to achieve greater 

long-run profitability. Fee income is difficult to adjust in the short run in response to interest-rate changes because 

12 
 

 

 

credit charge-offs, changing portfolios for credit risk purposes, and changing asset structure by 

buying or selling liquid assets (U.S. government and agency debt). 

To best consider the interest-rate sensitivity of net interest margin, we consider the net 

interest margin as a function of interest rates on assets and liabilities and the shares of each as a 

ratio to earning assets at each repricing frequency. Throughout the development of the model, 

we are assuming that the bank has chosen its product-line specialization and that the assets and 

liabilities reflect this choice for each bank. This relationship can be formally stated as 

EA 

⎛

⎜

⎝

⎞

⎟

⎠

m 

k 1 

where p refers to product line p, NIMpt is net interest margin in t, NIIt is net interest income 

(interest income less interest expense) in t, EApt is the amount of interest-earning assets in the 

∑=

portfolio in t, yk is the interest rate on assets of repricing frequency k, EAk is the amount of 

earning assets in repricing frequency k, rk is the interest rate on liabilities for repricing frequency 

k, and Lk is the amount of liabilities for repricing frequency k. Operationally, the first repricing 

frequency, for example, would be overnight. 

Since NIM will be subject to changes in interest rates on earning assets and interestbearing liabilities, changes in individual investments in earning assets, funding from interestbearing liabilities and changes in the overall investment in earning assets, the continuous change 

in NIM, dNIM, is a function of these bank management portfolio decisions and of time. In 

general, this can be expressed more formally, assuming continuous time and using (1) for any 

product line, as follows: 

y EA kt kt kt kt − r L p NII pt 

EA NIM = = (1) pt 

pt pt 

of its longer-term contractual basis. One exception is for credit card banks, where fees can be modified at the will of 

the lender, as can interest rates on outstanding balances of accumulated interest and original principal. 

13 
 

 

∂NIM ∂NIM dNII NII t t t t dNIM = dNII + dEA = − dEA (2) t t t 2 t ∂NII t ∂EA t EAt EAt 

where the changes in NII and EA, dNII and dEA, are the result of changes in the interest rates, dyk 

and drk and bank management decisions on investments in EA. The product-line index is 

dropped to simplify the notation. 

Noting that the total derivative of NII can be expanded in terms of interest-rate, earning 

asset, and liability changes: 

m m ∂NIIt ∂NIIt dNIIt = ∑ dyk − drk = ∑EAk dyk + yk dEAk − Lk drk − rk dLk (3) 

k =1 ∂yk ∂rk k =1 

In this formulation, we assume that interest-rate changes are independent of each other, which is 

not usually the case. We can change this assumption by substituting a term-structure and creditrisk spread factor model for each interest-rate change. For the NIM modeling, we will use a 

more simplified approach that can accommodate the term-structure and credit-risk spread effects 

on NIM. 

Expressing the interest change effects on NIM, we substitute (3) into (2) for dNII 

resulting in 

⎛ m ⎞ ⎜∑EA dyk + y dEA − L dr − r dL ⎟ k k k k k k k ⎝ k =1 ⎠ dEAt dNIM t = − NIM t (4) EAt EAt 

Note that the final term in (4) is the proportional change in EA over the preceding period times 

the current period NIM. This term is negatively related to the change in NIM, implying that if all 

other factors are held constant, increases in earning assets will tend to decrease the net interest 

margin. With respect to the first term in (4), constant interest rates mean that all dyk and drk are 

zero such that the proportion of each asset and liability component relative to EAt would have no 

effect on the change in NIM. Under these ceteris paribus conditions, this term is the ratio of the 

14 
 

change in NII resulting from a change in each asset and liability component, with each 

component’s proportion to EA held constant. If dEAt is positive and each dEAk and dLk grows at 

the same positive rate as earning assets, the effect would be to increase NII such that dNII was 

positive as long as NIMt was positive. The net effect on NIM under these conditions is zero. 

The implication of this result is important for interpreting the effect of the growth in 

earning assets on banks' net interest margins. Without advantageous changes in interest rates or 

changes in the composition of assets and liabilities relative to earning assets, a growth in earning 

assets will have little effect on NIM. Banks should experience an increase in NII by practically 

the same proportion as EA. Therefore, management cannot rely solely on growth to increase 

NIM or profitability but must manage the composition of assets and liabilities to achieve greater 

NIM and ROA, given management’s expectation of changes in interest rates and term structure. 

To complete the model for estimation, changes in interest rates are assumed to be outside 

the control of management and each is subject to a continuous time, stochastic diffusion process 

as follows: 

dyk = f ( yk ,t)dt +σ y dzk (5) k 

where σyk is the standard deviation of changes in yk, f(yk,t) is a drift term or mean for dyk, and dzk 

is a Weiner process of interest-rate changes with repricing frequency k. We assume, for 

simplicity, that each yk and rk follows the same stochastic processes so that dz depends only on 

the repricing frequency, k. Furthermore, the drift term requires a hypothesis for its value. If it is 

hypothesized that there is a tendency of regression toward a mean (e.g., Vasicek and HeathJarrow-Morton models), the sign of the term will depend on whether interest rates are above or 

below the mean. Another hypothesis is that the drift term is zero because interest rates follow a 

15 
 

 

 

 

 

 

random walk once regime shifts are complete (see Ingersoll [1987], 403).6 Since we do not wish 

to impose an interest-rate adjustment hypothesis or a term-structure hypothesis on bankers’ 

adjustment to interest-rate changes, we will allow the data to provide estimates of the effect of 

interest-rate and term-structure changes.7

 These interest-rate diffusion processes can be 

substituted into (4) for the final model: 

⎛ m ⎞ ⎜∑EAk f (yk ,t) + EAkσ y dz k + yk dEAk − Lk f (rk ,t) − Lkσ r dz k − rk dLk ⎟ ⎝ k =1 k k ⎠ dEAt dNIM t = − NIM t EAt EAt 

(6) 

The drift terms, f(yk,t) and f(rk,t), pose an interesting way of viewing the sign of any estimation of 

the coefficient on EAk or Lk. If these terms are zero and E(dzk) is zero, the effect of changes in 

earning assets is strictly conditioned by interest rate changes. 

If interest rates increase for assets and liabilities with repricing frequencies of less than 

one year, the change in NIM, all other factors held constant, depends on the relative shares of 

earning assets and liabilities repricing within one year. If short-term liabilities have a greater 

proportion of EAt than assets, dNIM will be negative and NIM will fall in the next period. Note 

also that the effect of interest-rate volatility on NIM, σyk and σrk, will be in the same direction as 

respective interest-rate changes, meaning that higher interest volatility has the same relationship 

as an increase in interest rates depending on the sign of the repricing gap, the difference between 

assets and liabilities in the same repricing frequency, or cumulative repricing frequencies. 

6

 The hypothesis of a random walk is perhaps most appropriate for the period under analysis. From 1984 to the 

present, there have been several regime shifts in interest-rate levels due to the substantial and sustained decline of 

inflation and shifts in monetary policy. The purpose of our study is not to explain these shifts but to allow the data 

to provide parameter estimates of bankers’ responses to interest-rate changes. 7

 In dealing with data on a quarterly frequency, we considered the imposition of the unbiased expectations 

hypothesis on interest-rate changes and the conjoint assumption of risk-neutral pricing to be a second-order 

constraint for the purposes of this study. The focus of this study is to estimate bankers’ reactions to prior interestrate, term-structure, and volatility changes and not to impose a particular model. The unbiased expectations 

hypothesis will be used to help interpret the estimated coefficients, since the pricing that results is risk neutral. 

16 
 

Furthermore, the change in NIM is inversely related to the level of prior-period NIM and, since 

NIMt is always positive, to the rate of change in EA, ceteris paribus. Since the rate of change in 

EA can be positive or negative, its sign must be accounted for in estimations. 

By way of comparison, another approach to modeling changes in NIM is to use Ito’s 

lemma by assuming that the change in NIM follows a diffusion process as below: 

m m m m 2 ⎛ ∂NIM t ∂NII t ∂NIM t t ∂NII ⎞ ∂NIM t 1 ∂ NIMt dNIM t = ⎜∑ dyk −∑ drk ⎟

⎟ + dt + ∑∑σ ij (7) ⎜

⎝ k =1 ∂NIIt ∂yk k =1 ∂NIIt ∂rk ⎠ ∂t 2 i=1 j=1 ∂xi ∂x j 

where all variables are as described above, xi and xj are interest rates composed of y and r and 

stated this way in (7) for simplicity, and σij is the covariance among all interest-rate changes of 

assets, dy, and liabilities, dr. To expand (7), note that the terms in parentheses are equivalent to 

equation (4), where earning assets are allowed to change. The middle term in (7) is the drift of 

NIM over time and can be thought of as a trend in NIM. When the diffusion process for interest 

rates is substituted from (5) into (7), the term in parentheses is equivalent to (6). This approach 

adds the drift and the second-order stochastic term within the double sum in (7). This final term 

can be interpreted as the portfolio effect on dNIMt due to interest-rate volatility and correlation— 

a portfolio risk effect. If interest rates are positively correlated within most interest-rate regimes 

(see Hanweck and Hanweck [1995]; Hanweck and Shull [1996]), the σij are positive and the sign 

of the double-sum term will depend on the sign of the second derivative of NIM with respect to 

interest rates. This term could be positive or negative depending on whether the interest rates are 

only for assets or only for liabilities. For asset terms the sign is negative; for asset and liability 

terms the sign depends on the weight of assets and liabilities at each repricing period and is 

likely to be negative for one-year repricing items; and for all liabilities the sign is likely to be 

positive. With positive correlations of interest-rate changes, we expect the weight of the terms to 

17 
 

be such that changes in volatility will be negatively related to the change in NIM for most banks 

regardless of product-line specialization. This result is consistent with the hypothesis expressed 

in equation (6), but with the correlations of interest-rate changes added. Thus, this approach 

reinforces the role of interest volatility for changes in NIM. 

This form of a model of NIM change is much less theoretically appealing because it 

assumes that earning assets and liabilities are almost exclusively stochastic, similar to the 

assumption of Ho and Saunders (1981), when it is well known that banks can and do change the 

distribution of assets and liabilities among their repricing buckets substantially from quarter to 

quarter for strategic purposes, presumably to take advantage of expected future interest-rate 

changes (see Saunders and Cornett [2003], chap. 9, for this evidence). Thus, we focus our 

empirical work using the model represented by (6) while taking advantage of the insights of the 

second model regarding interest-rate volatility and correlation by maturity and risk class. 

3.2 Credit Risk 

Some important factors influencing changes in NIM have been left out of the models 

above in order to achieve simplicity in focusing on interest-rate change effects on NIM. One 

important factor, as pointed out by Zarruk and Madura (1992), Angbazo (1997), and Wong 

(1997), is the effect of credit risk or risk of loan losses on NIM. Angbazo and Wong 

hypothesized that NIM should be positively related to loan losses, arguing that greater credit risk 

would mean that banks would charge higher premiums. An implication of this hypothesis is that 

expected increases in credit risk would prompt banks to raise interest-rate markups on the basis 

of these perceived future loan losses. Although it may be the case in the long run that greater 

credit risk will lead to higher NIM through the pricing of risk, quarterly or short-run changes in 

18 
 

 

 

 

the NIM are more likely to respond inversely to increases in credit risk. Like Zarruk and 

Madura, we argue that when faced with higher uncertainty of loan losses—that is, an increase in 

credit risk of their portfolios—risk-averse bank managers will shift funds to less default-risky, 

lower-yielding assets over the short-term horizon. In addition, bank examiners will put pressure 

on banks to reduce their exposure to risky credits when loan quality starts to deteriorate. These 

supervisory actions imply that a deterioration in loan quality, indicated by rising loan losses or 

nonperforming loans relative to earning assets, causes banks to lose interest income from these 

loans and move funds to less default-risky, lower-yielding assets. Both effects tend to decrease 

NIM in the short run, so that decreases in credit quality tend to decrease NIM. 

We can integrate these concepts directly into the above model by using equation (6). The 

total change in NIM, dNIMt, now becomes a function of interest-rate changes and credit-quality 

changes. We can incorporate credit quality by defining the value of an earning asset as 

composed of two components: the promised value, less the value of an option held by the bank 

(the lender) to take over the assets of the borrower if the loan is not paid off on time and in full.8 

An increase in the value of this option means that the credit quality of the borrower has 

decreased and the bank’s credit risk has increased. This relationship is shown more formally as 

EA = BEA − P (A , BEA ,T, Rf ) (8) k k t b k k 

where EAk is the market value of the earning asset of repricing frequency k, BEAk is the 

promised value of the debt, Pt() is the put option on the assets of the firm, Ab, T is the time to 

repricing, and Rfk is the value of the default risk-free rate for repricing frequency k. Since the 

8

 See Black and Cox (1976); Merton (1974); and Cox and Rubenstein (1985), 378–80 for the structural models for 

debt valuation. Conceptually, the value of the shareholders’ interest can be thought of as a call option on the assets 

of the firm, with the ability to put the assets to the debt holders if the value of the assets is less than the promised 

value of the debt. Thus debt holders, lenders such as banks, have a short put option on the firm’s assets with a strike 

price of the promised value of the debt. 

19 
 

book value of interest-earning assets is approximately equal to the promised value, we can 

substitute EAk in equation (6) with equation (8) to arrive at the following relationship: 

⎛ ⎞ ⎜∑

m 

(BEAk − Pt ()) f (yk , t)+ (BEAk − Pt ())σ y dz k + yk d(BEAk − p()) − Lk f (rk , t)− Lk σ r dz k − rk dLk ⎟ ⎝ k =1 k k ⎠ dNIM t = (BEAk − Pt ()) 

d (BEAt − Pt ()) − NIM t (BEAt − Pt ())

 (9) 

Since the promised value of the debt is fixed, the value of the put option directly reflects changes 

in credit risk. An increase in the value of the put option means that the put is closer to being in 

the money and default is more likely. By considering these factors, we see that the change in 

NIM is inversely related to increases in credit risk. 

We can evaluate the effect of interest-rate changes on default-risky debt by using 

equation (8). An increase in the base interest-rate index will reduce the promised value, BEAk, 

by increasing the discount factor. However, a rise in interest rates will also reduce the value of 

the put option because the present value of the strike price (the promised value) is reduced. The 

reduction implies that default-risky debt is less sensitive to a given change in the interest-rate 

index than default-free debt. If default risk is independent of interest-rate changes, bank 

specializing in higher credit-risk lending should be less interest sensitive than banks with 

concentrations in default-risky debt. 

4. Data and the Empirical Model 

In this section we describe the data, the empirical variables (for interest-rate shock, for 

term-structure shock, for credit shock, other institutional variables, and for seasonality), and our 

empirical specifications. 

20 
 

 

4.1 Data 

We obtained individual bank data for the estimation of these models from the Reports of 

Condition and Income (Call Reports) collected on a quarterly basis by the FDIC from the first 

quarter of 1986 to the second quarter of 2003. Data for financial market variables are from 

Haver Analytics and the Federal Reserve Board of Governors. Because of issues related to data 

consistency and availability, BIF-insured thrifts and Thrift Financial Report filers are excluded 

from the sample. Although available, bank data before the first quarter of 1986 were excluded 

from the sample because of the existence of Regulation Q, which constrained banks’ ability to 

adjust interest rates on deposits in response to changes in market interest rates.9 To exclude 

spurious financial ratios, we restricted the sample to commercial banks with earning assets of $1 

million or more and a ratio of earning assets to total assets exceeding 30 percent. This left 

22,077 commercial bank observations in the sample of banks that were in existence for one or 

more quarters over the sample period. We also excluded any observation with missing data 

points, reducing the sample to 17,789 commercial banks. 

We then divided the sample into 12 different bank groups based on the specialization and 

asset size of the bank at the end of each quarter. These bank groups practically correspond to the 

classification method used by the FDIC to identify a specialty peer group of insured institutions 

except that we make three main alterations to the FDIC peer grouping to better reflect 

differences in the institutions’ risk characteristics. First, we break down “commercial lenders” 

more finely to better reflect differences in risk characteristics between commercial and industrial 

(C&I) loan and commercial real estate (CRE) loan portfolios. Second, we separate consider 

noninternational banks with assets over $10 billion to account for potentially greater reliance on 

hedging activities that may offset the adverse effects of interest-rate shocks. Finally, to be able 

9

 The final phasing out of Regulation Q occurred in the second quarter of 1986. 

21 
 

 

to compare asset size over time, we use real assets rather than nominal assets to classify bank 

size groups.10 This classification method helps stratify commercial banks on the basis of their 

business models, portfolio compositions, and risk characteristics. Given dissimilarities in their 

risk characteristics, we expect banks in these different groups to exhibit varying degrees of 

sensitivity to credit, interest-rate, and term-structure shocks. We also considered a classification 

method based on derivative activities; however, data on derivatives are severely limited, 

particularly for the full sample period, so it would be difficult to assess the extent to which 

commercial banks use derivatives for hedging purposes. We use asset size as a proxy to identify 

groups of banks most likely to use derivatives to hedge their interest-rate risk. 

The 12 bank groups are 

• International banks 

• Large noninternational banks with real assets over $10 billion 

• Agricultural banks 

• Credit card banks 

• Commercial and industrial (C&I) loan specialists 

• Commercial real estate (CRE) specialists 

• Commercial loan specialists 

• Mortgage specialists 

• Consumer loan specialists 

• Other small specialists with real assets of $1 billion or less 

• Nonspecialist banks with real assets of $1 billion or less 

• Nonspecialist banks with real assets between $1 billion and $10 billion. 

10 To compute real assets, we divided nominal assets by the CPI-U price-level index for the quarter. 

22 
 

Because of the size and diversity of the group of commercial loan specialists and the 

grouop of small nonspecialists, each is further broken down into three groups on the basis of the 

size of their real assets. Appendix 1 describes the criteria for each of these bank groups. Each 

bank is classified in 1 of the 12 groups in a given quarter, but it may belong to 2 or more bank 

groups throughout the sample period as the bank changes its asset composition or its business 

model or both. For each bank group, we eliminated any bank that did not belong to the group for 

at least four quarters, thus making the final sample 16,522 commercial banks. 

The Call Reports require banks to report cumulative year-to-date income and expenses on 

a quarterly basis. Reflecting this reporting standard, most studies and quarterly reports by the 

FDIC and Federal Reserve of bank performance report NIM as an annualized, cumulative value 

(see the FDIC release of the Quarterly Bank Performance Report at www.FDIC.gov). The use of 

quarterly cumulative reports tends to smooth changes in NIM, reducing actual quarterly 

variations. To overcome this problem, we focus on quarterly changes in the net interest margin. 

For the second quarter through the fourth quarter of each year, we estimate actual income and 

expenses for the quarter by subtracting the previous quarter’s cumulative reported values from 

the current cumulative reported values. For the first quarter, we use reported income and 

expenses for the quarter. We then annualize these values by multiplying each by four. We 

compared the resulting series with the cumulative series in model estimation and found that the 

resulting series’ performance was much more consistent with the hypothesized behavior. 

Therefore, all income and expense derived data are based on adjusted series. The reported 

earning assets—the denominator of computed ratios—are the average of ending values for the 

quarter and the previous quarter. 

23 
 

Nine panels in figure 1 show trends in net interest margins and noninterest income for 

each of our 12 bank groups. These panels show a long-term trend of a decline in net interest 

margins for most bank types, beginning around the 1992–1993 period. In particular, 

international banks have experienced a significant compression in their net interest margins since 

the early 1990s, with the median net interest margin for the group falling by more than 175 basis 

points. It is not clear how much of this long-term decline can be attributed to the low interestrate environment, greater competitive pressure, or regulatory changes that made securitization 

and other off-balance-sheet activities more attractive. However, it is interesting to note that the 

peak year in net interest margins roughly corresponds to the implementation of capital regulation 

rules and prompt corrective action as specified in FDICIA. 

Aggregate industry statistics show a growing importance of noninterest income as a 

source of bank earnings. The FDIC Quarterly Banking Profile shows that noninterest income 

rose from 31 percent of quarterly net operating revenue in first quarter 1995 to 41 percent in 

second quarter 2003. However, most bank groups did not experience a notable increase in 

noninterest income as a percentage of average earning assets over most of the sample period. In 

fact, the median quarterly noninterest income as a percentage of average earning assets remained 

mostly stable for most bank groups throughout the 1990s. DeYoung and Rice (2004) suggest 

that the long-term increase in noninterest income may have already peaked as the risk-return 

trade-off reached a plateau. 

International banks, large banks with real assets exceeding $10 billion, and credit card 

specialists did experience a sharp increase in noninterest income over the sample period. The 

median ratio of noninterest income to average earning assets for the international bank group 

rose sharply after 1997, overtaking net interest margins as the primary source of this grooup’s 

24 
 

 

 

earnings. This trend likely reflects earnings and product diversification and a greater reliance on 

off-balance-sheet instruments by these banks in response to deregulation, capital regulation, and 

financial market developments.11 Rogers and Sinkey (1999) found that banks that are larger and 

have smaller net interest margins and fewer core deposits, as is the case of international banks, 

tend to engage more heavily in nontraditional activities. 

As figure 1 shows, large banks with real assets greater than $10 billion saw their 

noninterest income rise steadily, although net interest income still represents their primary source 

of earnings. The net interest margin fluctuated between 3.5 percent and 4.5 percent of average 

earning assets for this group of banks. Unlike for other bank groups, for credit card specialists 

the median net interest margins did not exhibit a discernible downward trend in the 1990s. The 

median net interest margin for credit card banks increased sharply in the 2001–2003 period 

despite a steady decline in short-term interest rates. At the same time, the median noninterest 

income for the group has risen sharply since 1997. This trend likely reflects a widespread use of 

risk-based pricing and risk-related fees in response to heightened rate competition and greater 

availability of credit card loans to higher-risk and higher-revenue-generating borrowers than 

previously.12 

Figure 2 presents the median quarterly return on average earning assets (ROA) for 

selected groups of banks. The median ROA for large and midtier banks has improved 

significantly since the implementation of FDICIA, and it has remained more stable since then 

compared with prior periods. Between 2002 and 2003, however, large banks with real assets 

greater than $10 billion saw their ROA rising sharply, whereas international and midtier 

11 See Angbazo (1997) for the effects of off-balance-sheet instruments on net interest margins. Angbazo found a 

negative relationship between letters of credit, net securities lent, and net acceptances acquired and net interest 

margins, but a positive relationship between net loans originated/sold and net interest margins. 

12 See Furletti (2003) for discussions of recent developments in credit card pricing and fee income. 

25 
 

nonspecialists reported weaker earnings. These differing trends suggest diversity across these 

three largest asset size groups in their business models in terms of asset composition, correlation 

among earning components, and earnings management. The earnings volatility of international 

banks suggests that they are vulnerable to market factors other than those included in our model; 

however, discussion on the effect of these factors on bank earnings is outside the scope of this 

paper. As for large banks, the median ROA for small nonspecialist banks made discrete 

improvements in 1992. The ROA of these banks, particularly the smallest asset size group, has 

exhibited a high degree of seasonality over time. 

4.2 Empirical Variables 

Appendix 2 lists the explanatory variables included in our empirical model and their 

expected signs. All variables representing financial ratios or interest rates are expressed in 

annualized percentage terms. Bank-specific variables and financial market variables included in 

the empirical model are derived from the theoretical model of bank behavior presented in section 

3 of this paper. Table 1 presents descriptive statistics for bank-specific variables for each bank 

group. To preserve earnings data for an individual institution at a given time, we did not adjust 

the bank data for mergers and acquisitions that occurred over the sample period. Instead, we 

screened the sample for any aberrant data on an individual-bank basis. As discussed below, 

there exist significant variations in the value of each of these bank-specific variables across bank 

groups as well as within the given bank group. 

4.2.1 Interest-Rate-Shock Variables 

26 
 

VOL_1Y represents short-term interest-rate volatility and is measured by the standard 

deviation of a weekly series of one-year Treasury yields for the quarter. ST_DUMMY is a 

dummy variable that takes a value of one if the one-year Treasury yield rose during the quarter, 

and zero if the yield fell. Figure 4 illustrates a mostly positive but imperfect correlation between 

the quarterly short-term interest-rate volatility and the level of short-term interest rates. Equation 

(7) posits that the coefficients for both VOL_1Y and ST_DUMMY would have a negative sign 

for most banks. 

The duration gap between assets and liabilities measures respective changes in assets and 

liabilities due to an interest-rate shock and is a key determinant of bank net interest margins 

(Mays [1999]). The duration gap reflects the repricing frequency of assets and liabilities as well 

as the value of embedded call options. Data necessary to calculate the duration gap are not 

collected in the Call Report for commercial banks, so we are prevented from using a reported 

duration gap in our empirical model. As a proxy for the interest-rate sensitivity of bank 

portfolios, we use net short-term assets—the difference between short-term assets and short-term 

liabilities. We define a repricing frequency less than one year as “short term.” STGAP_RAT is 

net short-term assets as a percentage of earning assets. Although there have been changes in Call 

Report data items and their definitions over time, we believe that STGAP_RAT is generally 

comparable over time because many of these changes affected both assets and liabilities. Our 

definition of STGAP_RAT includes nonmaturing liabilities that are discussed more fully below. 

Table 1 shows that, whereas international banks and credit card specialists tend to have better 

matched assets and liabilities than other bank groups, consumer loan specialists, mortgage 

specialists, other small specialists, and small nonspecialist banks tend to have the most 

unmatched balance sheets. Holding everything else constant, we expect the coefficient for 

27 
 

STGAP_RAT to have a negative sign since longer-term assets have higher yields than shorterterm assets with the same risk characteristics. In addition, we expect the size of STGAP_RAT to 

have a positive effect on NIM when short-term interest rates rise. 

Flannery and James (1984) show that deposits with uncertain maturity, such as demand 

deposits, regular savings accounts, and small time deposits, have an effective maturity longer 

than one year. This finding suggests that the “effective” cost of these liabilities is relatively 

insensitive to changes in market interest rates. Indeed, Mays (1999) found that thrifts with a high 

percentage of nonmaturing deposits, defined as the sum of demand deposits and regular savings, 

experienced a positive increase in net interest margins in response to a positive interest-rate 

shock. Although these relationships may have changed in recent years as short-term interest 

rates have reached 1.0 percent and less, we can test for any changes in this structure with models 

estimated for different periods. We include NM_RAT, nonmaturing deposits as a percentage of 

earning assets, in the model to proxy for the degree of interest-rate sensitivity of the bank’s 

funding from nonmaturing deposits. As shown in table 1, commercial loan specialists and small 

nonspecialist banks seem to rely most heavily on nonmaturing deposits to fund their lending, 

whereas international and credit card banks fund their lending activities with more interest-ratesensitive liabilities. On the basis of previous studies, we expect the coefficient for NM_RAT to 

have a positive sign. In addition, we expect the size of NM_RAT to have a marginal and 

positive effect on NIM as interest rates rise, given the documented insensitivity of nonmaturing 

liabilities to interest rate changes (Mays [1999]). 

4.2.2 Term-Structure-Shock Variables 

28 
 

Figures 3 through 5 present historical trends in the financial market variables included in 

our empirical model. DS5Y_1Y is the change in the spread between five-year and one-year 

Treasury yields (yield spread). Given that maturities of bank assets are generally longer than 

those of bank liabilities, we expect DS5Y_1Y to be positively related to DNIM_RAT. Figure 3 

shows that the average DNIM_RAT for mortgage lenders has roughly tracked DS5Y_1Y over 

time, although it seems to respond to the changing shape of the yield curve with one- or twoquarter lags. A similar correlation exists between DNIM_RAT and DSY_1Y for other groups of 

lenders, although visually the relationship is not as strong. 

4.2.3 Credit-Shock Variables 

DLN_AST and DCI_RAT are changes in, respectively, the ratio of loans to earning 

assets and the ratio of C&I loans to earning assets from the prior quarter. Both variables proxy a 

size-preserving increase in higher-yielding assets and are expected to be positively related to 

DNIM_RAT. Table 1 shows that C&I specialists, CRE specialists, and credit card specialists 

experienced the largest increases in median loan-to-asset ratios during the sample period, 

whereas international banks, small other-specialty banks, and midtier nonspecialists reported the 

largest declines. All bank subgroups other than C&I specialists experienced, on average, a 

decline in C&I loan-to-asset ratios over the sample period. 

We use the spread between the Baa corporate bond and the Aaa corporate bond yields 

(CSPRD) to proxy for shocks in the credit market due to deterioration in credit quality or to other 

credit market disturbances or to both, which may result in reduced liquidity in the market as well 

as credit rationing. Among previous episodes of these credit events are the Mexican peso crisis 

in 1995 and the Russian devaluation and default in August 1998 (the latter preceded the near 

29 
 

collapse of Long-Term Capital Management in the fall of 1998). In addition, as shown in figure 

5, CSPRD is also closely related to the credit-risk premium measured by the spread between the 

C&I loan rate and the intended federal funds rate. The relationship appears to have tightened in 

the 1990s for C&I loans of all sizes, thus indicating that banks, both small and large, are better 

able to price expected changes in credit risk. The coefficient for CSPRD is expected to have a 

negative sign if banks are unable to increase loan rates but ration the supply of credit. 

DNPERF_RAT is a change in the ratio of nonperforming assets to earning assets 

(NPERF_RAT). This variable represents a change in realized credit losses, and is used as a 

proxy for a “credit shock.” International banks, credit card specialists, and C&I specialists have 

the highest median NPERF_RAT. The median value of DNPERF_RAT is close to zero; 

however, there are some institutions within each group with large positive or negative values. 

As discussed in section 3, the coefficient for DPERF_RAT is expected to have a negative sign if 

banks are unable to price credit risk effectively in the short term, as bank managers shift funds to 

lower-yielding assets. 

4.2.4 Other Institutional Variables 

Net interest margin (NIM_RAT) is annualized net interest income for the quarter divided 

by average earnings assets. Table 1 shows that the median NIM_RAT varies significantly across 

bank groups, with international banks having the lowest NIM_RAT on average (2.7 percent), 

whereas while credit card specialists have the highest average NIM (9.3 percent). The median 

DNIM_RAT, the change in NIM_RAT between t-1 and t, is close to zero for most bank groups, 

although there are institutions experiencing a large change in net interest margins on a quarter-toquarter basis. The derivation of the change in NIM presented in equation (6) of section 3.1 

30 
 

shows 

that 

⎛ m

EA f (y ,t) + EA σ dz + y dEA − L f (r ,t) − L σ dz − r dL ⎞ ⎜∑ k k k y k k k k k k r k k k ⎟ ⎝ k =1 k k ⎠ dEAt dNIM t = − NIM

EAt 

t EAt 

This suggests that if the rate of change in EA is held constant, the change in NIM should be 

inversely related to the level of prior-period NIM. The coefficient for the lagged value of 

NIM_RAT is therefore expected to have a negative sign. 

ROA is annualized after-tax net income for the quarter divided by average earning assets, 

and DROA is the change in ROA from the previous quarter. Median ROA varies from 0.95 for 

mortgage and consumer credit specialists to 2.33 for credit card specialists. Like DNIM_RAT, 

the median DROA is close to zero; however, some large positive or negative numbers are 

observed in the sample. DNONII_RAT is an annualized noninterest income for the quarter 

divided by average earning assets, while DSECGL_RAT is annualized security gains and losses 

for the quarter divided by average assets. Both variables proxy the effects of bank earnings 

diversification on net interest margins. Signs of these variables could be positive or negative, 

depending on whether these earnings are substituted for or complementary to NIM. 

Finally, LOGAST is the log of total real assets derived as nominal assets deflated by the 

urban consumer price index (CPI-U). Table 1 shows that there is a negative cross-section 

relationship between the asset size of the bank within the group and the median NIM_RAT. This 

relationship implies that, ceteris paribus, the asset size would be also negatively correlated to 

DNIM_RAT, and therefore we expect the coefficient for LOGAST to have a negative sign. 

However, as shown from the model development in section 3, a simple change in the scale of 

operations for an individual bank should have no effect on changes in net interest margin. 

31 
 

 

4.2.5 Seasonality 

As discussed in section 4.1, reported earnings of small banks tend to exhibit significant 

seasonality. Reported ROA for these banks is consistently and significantly lower in the fourth 

quarter of the year than in any other quarter. This pattern raises some questions about the 

reliability of reported earnings for earlier quarters of the year. To control for these seasonal 

patterns in reported earnings, we include three quarterly dummy variables. QTR2 takes a value 

of one if the reported period is the second quarter, QTR3 if it is the third quarter, and QTR4 if it 

is the fourth quarter. 

4.3 Empirical specifications 

Our empirical model of net interest margins is a one-way random-effects model and is 

specified as follows: 

yi,t = γ 1 yi,t−1 + β ′xi,t −2 +φ′zt−1 +τdquarter + vit

 (8) 

where i = 1, ..., N, t = 1,...,T, vit = α i + uit . αi, is a random-disturbance term unique for the ith 

observation, and both αi and uit are assumed to be normally distributed. yi,t is the dependent 

variable, the change in NIM, xi,t−2 is a vector of bank-specific explanatory variables, zi,t −1 is a 

vector of financial market explanatory variables and dquarter are quarterly dummies. Finally, γ, β, 

φ, and δ are a vector of coefficients. Section 4.2 has just discussed the expected signs of each of 

these coefficients. 

32 
 

 

Following Brock and Franken (2002), bank-specific variables enter the model with twoquarter lags; thus we avoid the potential endogeneity problems that may exist for these variables. 

We estimate the model using the generalized-least-squares (GLS) technique, based on the 

estimated disturbance variances. For dynamic random-effect models, the GLS estimator is 

equivalent to the maximum likelihood estimator. The GLS estimator is consistent and 

asymptotically normally distributed as the number of cross-sectional observations, N, approaches 

infinity (Hsiao [2003]). N is very large for all the bank groups in our sample except international 

banks, large noninternational banks, and credit card specialists. We address potential 

heterogeneity and serial correlation problems in model specifications by controlling for the size 

of the institution, applying cross-sectional random effects, and adding a lagged value of the 

dependent variable. As an alternative to GLS estimation, we also tested the mixed-model 

specification, which allows us to explicitly control for heteroscedasticity and serial correlation 

problems in the unbalanced panel (Littell et al. [1996]). Within the mixed-model framework, we 

tested for a potential bias arising from a number of institutions appearing only for a limited 

number of quarters, a bias that was not controlled in the GLS specification, and we found the 

effect to be insignificant. The results of the mixed-model specification were more or less similar 

to those of the GLS estimation and are therefore not reported in this paper. 

For each bank group, the empirical model for changes in net interest margins 

(DNIM_RAT) to be tested is as follows: 

DNIM _ RAT it = c + β1 *VOL _1Y it −1 2 + β * ST _ Dummy it −1 3 + β * STGAP _ RAT it −2 4 + β * NM _ RATit −2 Interest Rate Risk 

+ β5 * STGAP _ SD it −1 6 + β * NM _ SDt −1 

+ β * DS5Y _1Y + β * DS5Y _1Y + β * DS5Y _1Y + β * DS5Y _1Y Term Structure Risk 7 t −1 8 t −2 9 t −3 10 t −4 

+ β * DLN _ AST + β * DCI _ RAT + β * DCSPRD + β * DNPERF _ RAT Credit Risk 11 t −2 12 it −2 13 t −1 14 it −2 

+ β15 * LOGAST it −1 16 + β * NIM _ RAT it −2 17 + β * DNIM _ RAT it −1 18 + β * DNONII _ RATit −2 

+ β19 * DESCGL _ RAT t −2 20 + β * QTR2 + β 21 * QTR3 + β 22 * QTR4 

(9) 

33 
 

The second empirical model we test measures the effect of interest-rate, term-structure, 

and credit-risk shocks on overall profitability of the bank and has the same empirical 

specification as the net interest margin model. We expect the ROA of banks with welldiversified earning sources to be less sensitive to interest-rate and other shocks than net interest 

margins. In other words, the better diversified a bank’s earnings are, the less significant are the 

coefficients for most dependent variables; in other words again, bank earnings are expected to be 

less sensitive to these shocks. The empirical model for changes in ROA (DROA) for each bank 

group is specified as 

DROAit = c + β1 *VOL _1Yit −1 + β 2 * ST _ Dummyit −1 + β3 * STGAP _ RATit −2 + β 4 * NM _ RATit −2 Interest Rate Risk 

+ β5 * STGAP _ SD it −1 6 + β * NM _ SDt −1 

+ β * DS5Y _1Y + β * DS5Y _1Y + β * DS5Y _1Y + β * DS5Y _1Y Term Structure Risk 7 t −1 8 t −2 9 t −3 10 t −4 

+ β * DLN _ AST + β * DCI _ RAT + β * DCSPRD + β * DNPERF _ RAT Credit Risk 11 t −2 12 it −2 13 t −1 14 it −2 

+ β15 * LOGAST it −1 16 + β * NIM _ RAT it −2 17 + β * DNIM _ RAT it −1 18 + β *QTR2

(10) + β * QTR3 + β *QTR4 

We also test these models for stability over four separate subsample periods representing 

changes in legislation that had effects on competition in the banking industry. These periods, 

discussed more fully in the next section, are 1986–1988, 1989–1991, 1992 to the second quarter 

of 1997, and the third quarter of 1997 to the second quarter of 2003. 

19 20 

5. The Results 

Here we discuss the results first for the full sample period and then for the subsample 

periods. 

5.1 Full Sample Period results 

34 
 

Tables 2A and 2B summarize the results of cross-sectional time series regressions on 

DNIM_RAT and DROA for each of the 12 bank groups. We applied the Hausman test for the 

presence of one-way random effects for all bank groups. Except for international banks, we 

cannot reject the presence of random effects for these groups of banks. The F-test shows that 

one-way fixed effects exist for international banks. On the basis of these test results, we applied 

one-way random-effects estimation to all bank groups other than international banks and applied 

one-way fixed-effects estimation to international banks. 

The statistical significance of explanatory variables and the size of coefficients for these 

variables vary considerably across bank groups. The goodness of the fit for the DNIM_RAT 

model measured by the modified R-square varies from 0.10 for consumer loan specialists to 0.37 

for international banks, credit card specialists, and midtier nonspecialist banks. The goodness of 

the fit for the DROA model varies from 0.32 for consumer loan specialists to 0.54 for small 

nonspecialists with real assets greater than $300 million. 

In general, the regression results presented in tables 2A and 2B imply that different types 

of banks are sensitive to different types of shocks. Larger and more diversified institutions and 

credit card specialists appear to be less vulnerable to interest-rate and term-structure shocks, but 

still sensitive to credit shocks. Both of these relationships may reflect the greater use of offbalance-sheet instruments that help these institutions hedge their interest-rate risk. In 

comparison, agricultural banks, mortgage specialists, commercial loan specialists with real assets 

less than $300 million, and small nonspecializing banks with real assets less than $300 million 

are sensitive to all three types of shocks examined in this paper—credit, interest-rate, and termstructure shocks. 

35 
 

5.1.1 Sensitivity to Interest-Rate Shocks 

The lagged ratio of net short-term assets (STGAP_RAT2) generally has a small negative 

coefficient, when significant. The finding implies that, ceteris paribus, banks with longer-term 

net assets experience greater variations in net interest margins. As found by Mays (1999), the 

lagged ratio of nonmaturing deposits to earning assets (NM_RAT2) has a small positive 

coefficient, when significant. These results suggest, as hypothesized, that the interest-rate 

sensitivity associated with a bank’s funding has a significant, positive effect on the bank’s net 

interest margins, regardless of the interest-rate environment. Two interaction terms included in 

the model—STGAP_SD1 and NM_SD1—are designed to capture the marginal effect of an 

increase in net short-term assets (STGAP_RAT2) and nonmaturing deposits (NM_RAT2) when 

short-term interest rates rise (ST_DUMMY1 = 1). A negative coefficient for the short-term 

interest-rate dummy variable (ST_DUMMY1), when considered together with interaction terms, 

can be interpreted as showing that when short-term interest rates increase, there is an adverse 

effect on the change in net interest margins for banks with no net short-term assets or 

nonmaturing deposits. The two interaction terms, STGAP_SD1 and NM_SD1, have positive 

signs for almost all bank groups and, when statistically significant, suggest that an increase in 

short-term interest rates has an increasingly positive effect on the change in net interest margins 

as the proportion of net short-term assets or nonmaturing deposits increases. 

The economic significance of these coefficients also varies across the 12 bank groups. 

For instance, in the case of C&I specialists, holding everything else constant, net interest margins 

would be 6 basis points lower in the quarter following an increase in the interest rate. A 10 

percent increase in STGAP_RAT2 or NM_RAT2 would offset that decline by 4 basis points. 

For mortgage specialists, an increase in the short-term interest rate is followed by a 12-basis36 
 

point decline in NIM, with a 10 percent increase in STGAP_RAT2 or NM_RAT2 offsetting this 

decline by less than 1 basis point and 3 basis points, respectively. Dissimilarities between C&I 

specialists and mortgage specialists in the sensitivity of NIM to an increase in interest rates likely 

reflect inherent differences in the maturity and interest-rate terms of the two groups’ loan 

portfolios. 

With a few exceptions, our two measures of interest-rate shocks—interest-rate volatility 

(VOL_1Y1) and a short-term interest-rate dummy variable (ST_DUMMY)—have negative 

coefficients, when significant. These results seem to contradict the findings of previous research 

on determinants of net interest margins, which showed that interest-rate volatility positively 

affects the level of net interest margins. However, the results are in line with our model that tests 

for the effect of interest-rate volatility on the quarterly change in net interest margins. We find 

that as we hypothesized in the equation (7), given the balance-sheet composition of banks in our 

sample, higher interest-rate volatility lowers the change in net interest margins. The coefficients 

are economically significant and vary widely across bank groups. A 1-percentage-point increase 

in interest-rate volatility, measured by the standard deviation of one-year Treasury yields within 

the quarter, is followed by about a 4- to 6-basis-point decline in NIM for most bank groups. The 

coefficients are significantly larger for C&I specialists and credit card specialists, which tend to 

have shorter-term and more default-risky assets. A 1-percentage-point increase in interest-rate 

volatility leads to a 23-basis-point decline in NIM for C&I specialists and a 137-basis-point 

decline in NIM for credit card specialists. These results also suggest that most banks are unable 

to hedge against interest-rate risk effectively, at least in the short term. In comparison, mortgage 

lenders that typically have assets with longer maturities appear to benefit from higher interestrate volatility. A 1 percent increase in interest-rate volatility leads to a 6-basis-point increase in 

37 
 

NIM for mortgage specialists. In the next section, we show significant within-group variations 

in banks’ sensitivity to interest-rate volatility across different regulatory and economic regimes. 

5.1.2 Term-Structure-Shock Variables 

Lagged changes in the Treasury yield spread (DS5Y_1Y1, DS5Y_1Y2, DS5Y_1Y3, and 

DS5Y_1Y4) have positive and lingering effects on the change in net interest margins for 

mortgage specialists and small nonspecialist banks with real assets greater than $50 million, up 

to four quarters following the initial shock. On the other hand, none of the coefficients for the 

four lagged values of the change in the Treasury yield spread is significant for credit card 

specialists and midtier nonspecialist banks. For the remaining bank groups, the change in the 

Treasury yield spread affects the variation in net interest margins only with a noticeable lag, and 

one-quarter lagged change in the Treasury yield spread (DS5Y_1Y1) has a significantly negative 

coefficient for some bank groups. For many bank groups, coefficients for two-quarter and threequarter lagged change in the Treasury yield spread (DS5Y_1Y2 and DS5Y_1Y3) are 

significantly positive. For instance, ceteris paribus, a 100-basis-point increase in the yield spread 

boosts NIM for mortgage specialists by 11 basis points two quarters after the initial shock, and 9 

basis points three quarter after. In the case of small nonspecialists, the effects are similar but less 

economically significant. A 100-basis-point increase in the yield spread leads to about a 6-basispoint increase two quarters following the initial shock. These results suggest that banks that 

have a large percentage of long-term fixed-rate assets, such as mortgages, are more vulnerable to 

a flattening of the yield curve than other types of banks. These results also support our 

hypothesis that portfolio composition, asset concentration, and business models, separately 

38 
 

represented by bank groupings, are critical to an understanding of the effects of a term-structure 

shock on bank net interest margins over time. 

5.1.3 Credit-Shock Variables 

The coefficient for the lagged value of the change in the ratio of loans to earning assets 

(DLN_AST2) is positive and significant for most banks groups, as hypothesized. However, the 

coefficient is significant but negative for agricultural banks, C&I specialists, commercial loan 

specialists with real assets less than $50 million, and consumer loan specialists. On the basis of 

the interpretation of this variable as a proxy for a size-preserving increase to higher earning 

assets, a negative sign may imply either that these banks attract new loans by offering lower rates 

or that they do not price the credit risk of new loans correctly or both. The results show that a 

10-percentage-point increase in the loan-to-asset ratio typically accounts for a 2- to 4-basis-point 

increase in NIM, although large noninternational banks benefit significantly more (8 basis 

points) from the same increase. 

The lagged value of the change in the C&I loan ratio (DCI_RAT2) has significantly 

negative coefficients for commercial loan specialists with real assets over $300 million, other 

small specialists, and small nonspecialist banks with real assets over $300 million. The negative 

coefficient is contrary to prior expectations. It may reflect exogenous variables not specified in 

the model, such as the competitive market landscape affecting loan pricing. Alternatively, for 

this group of banks, a trade-off and diversification benefit may exist between an expansion to 

higher-yielding assets and higher risk. Agricultural banks and mortgage specialists, however, 

seem to benefit from expanding their C&I loan portfolios—a diversification effect for these 

banks. 

39 
 

The lagged value of a change in the credit spread (DCSPRD1) has a negative and 

significant coefficient for most bank groups, a result suggesting that banks tighten underwriting 

standards following credit market disturbances. In addition, despite the fact that banks do charge 

a higher risk premium in response to higher credit risk, as shown in figure 5, banks are typically 

unable to fully price credit risk and may instead rely on credit rationing. The effect of changes in 

the credit spread appears to be economically significant for many bank groups, although there 

are significant variations across groups. For instance, it is estimated that a 100-basis-point 

increase in the credit spread subsequently reduces NIM by 5 basis points for CRE specialists, 16 

basis points for C&I specialists, and 20 basis points for consumer specialists. As in the case of 

interest-rate volatility, within-group results for the change in the credit spread vary significantly 

across regulatory and economic regimes. These results are discussed in the next section. 

With the exception of agricultural banks, the coefficient for the lagged value of the 

change in the nonperforming asset ratio (DNPERF_RAT2) is negative, when significant. In 

most cases, credit-quality deterioration, measured by DNPERF_RAT2, has considerably weaker 

economic significance than the forward-looking loan-quality variable (DCSPRD1). However, 

credit shocks appear to have particularly significant effects on credit card lenders, with a 1-

percentage-point increase in the nonperforming asset ratio leading to a 53-basis-point reduction 

in NIM. This result may be primarily driven by subprime lenders that tend to be very credit 

sensitive. Although several proxy measures for subprime lenders—such as a dummy variable to 

indicate the top 5 percentile in terms of the loan yield—were considered, these variables were 

ultimately dropped from the model because of data limitations. The results of credit-shock 

variables suggest that, as we hypothesized in section 3, banks are unable to effectively price an 

40 
 

unexpected increase in credit losses, and managers respond to this increase in the short term by 

shifting funds to less default-risky and lower-yielding assets. 

5.1.4 Other Institutional Variables 

Other institutional variables have generally similar effects on the change in net interest 

margins (DNIM_RAT) across bank groups, with some notable exceptions. The coefficient for 

the lagged value of net interest margins (NIM_RAT2) is negative and significant at the 1 percent 

level for all bank groups. This is in line with the expectation, as presented in equation (6), that 

posits a negative correlation between the lagged level of net interest margins and the change in 

net interest margins. The lagged change in net interest margins (DNIM_RAT1) has a significant 

negative coefficient, implying error correction over time. Coefficients for DNIM_RAT1 and 

NIM_RAT2 have the largest coefficients among all explanatory variables. Estimates show that 

on average, a 100-basis-point increase in the lagged change in NIM leads to a 55-basis-point 

increase in NIM in the subsequent quarter. On average, the 100-basis-point difference in twoquarter lagged NIM translates to the 31-basis-point difference in the change in NIM on average. 

The lagged value of the change in the noninterest income ratio (DNONII_RAT2) is 

significant and positive for some groups, including large noninternational banks, agricultural 

banks, CRE specialists, and commercial loan specialists with real assets less than $50 million; 

however, it is significantly negative for the others, such as commercial loan specialists with real 

assets over $300 million, other small specialists, small nonspecialist banks, and midtier 

nonspecialist banks. This result shows that not all banks have reaped diversification benefits 

from noninterest income. 

41 
 

The coefficient for the lagged value of the change in security gains and losses 

(DSECGL_RAT2) has a negative sign, where significant, except for mortgage specialists. This 

implies that, except for mortgage specialists, security gains and losses are typically a 

complementary source of earnings. However, for mortgage specialists, net interest margins and 

security gains and losses seem to move together, perhaps because of a common response to 

changes in short-term interest rates. 

Finally, with the exception of midtier nonspecialist banks, the coefficient for the log of 

total assets (LOGAST2) shows that the change in net interest margins is inversely related to asset 

size, when significant. In other words, holding all else constant, the larger the bank in terms of 

assets, the less sensitive the net interest margins will be with respect to a proportional change in 

the asset size of the bank. 

5.1.4 Comparison with the ROA Model 

The results of the ROA model are mostly similar to those of the net interest margin 

models, with a few exceptions. Changes in the ratio of loans to earning assets (DLN_AST2) as 

well as the C&I loan ratio (DCI_RAT2) have positive effects on the change in net interest 

margins, where significant. This result suggests that holding all else constant, the average bank 

earns higher profits by shifting to higher-risk, and presumably, higher yielding, assetsthat may 

not always be reflected in higher net interest margins. As in the case of the net interest margin 

model, the coefficient for the change in nonperforming assets (DNPERF_RAT2) is negative for 

all bank groups, where significant. Coefficients for net short-term assets (ST_GAP2) and 

nonmaturing deposits (NM_RAT2) are negative and positive, respectively, as expected. 

Estimates in the ROA model show that as with the NIM model, an increase in short-term interest 

42 
 

rates has a positive effect on the change in net interest margins as the proportion of net shortterm assets or nonmaturing deposits increases for all banks except credit card specialists, where 

the sign is negative and significant. 

Overall profitability of the bank appears to be significantly less sensitive to the termstructure shock than net interest margins. The widening of the Treasury yield spread negatively 

affects the change in ROA for the majority of banks, with one-, three-, and four-quarter lags. For 

a majority of bank groups, the one-quarter lagged change in the Treasury yield spread has a 

negative and significant coefficient. This may reflect the fact that the Treasury yield spread is a 

leading indicator of the business cycle. The Treasury yield spread tends to become narrower 

several quarters before the beginning of a recession and then subsequently widens as the Federal 

Reserve lowers short-term interest rates to stimulate the economy. As the recession goes on, 

loan quality diminishes, and banks react by increasing provisions for loan losses, a highly 

cyclical component of bank earnings. This reaction would tend to compress bank earnings when 

the Treasury yield spread widened rapidly. 

In comparison, the ROA is more sensitive to interest-rate volatility than net interest 

margins for most bank groups. These results suggest that, despite their diversification, banks are 

unable to hedge effectively against interest-rate volatility. In terms of credit shocks, the 

coefficient for the lagged value of the change in the credit spread (DCSPRD1) tends to be 

positive, although not always statistically significant, for banks with a greater focus on 

commercial lending. These results suggest that while banks tighten their lending standards when 

the credit spread widens, reducing their loan volume, they also tend to rely on higher fees to 

compensate for higher credit risk. 

43 
 

5.1.5 Seasonality 

As is evidenced from the quarterly time series charts, net interest margins and ROA show 

seasonality in the reported data. To account for this, we used quarterly dummy variables in the 

regression models. Coefficients for the three quarterly dummy variables—QTR2, QTR3, and 

QTR4—indicate significant seasonality in reported net interest margins and ROA. The change 

in the net interest margins is significantly higher in the later quarters of the year than in the first 

quarter, with the third quarter showing the greatest difference from the first quarter. In 

comparison, for most bank groups other than large and midtier banks, the reported change in the 

ROA is significantly lower in the fourth quarter of the year than in the first quarter. This 

difference is economically significant. On average, the change in NIM was 12 basis points 

higher in the third quarter than in the first quarter, while the reported change in ROA was 39 

basis points lower in the fourth quarter than in the first quarter. These results are consistent with 

banks’ conducting earnings management via the net interest margin in the later quarters and with 

banks’ penchant for deferring loan losses and other charges until the year-end quarter. 

5.2 Subsample Period Results 

Over our sample period, the banking industry underwent a series of major legislative and 

regulatory changes, severe thrift and banking crises, and an unprecedented consolidation 

movement. Presumably these changes have had numerous effects on the way banks operate. It 

is therefore reasonable to assume that earnings sensitivity to interest-rate and other shocks would 

vary across different regulatory and economic regimes. As we discussed in section 1, we focus 

primarily on the effects of three key legislative changes during this period: DIDMCA, FDICIA, 

and Riegle-Neal. These pieces of legislation are important to the banking industry because they 

44 
 

 

 

spurred a price competition for deposits that led to less volatility in bank lending, improved the 

capital positions of banks, allowed geographic and earnings diversification, and changed the 

general competitive landscape. 

To test the effects of these changes on the sensitivity of bank earnings, we test our model 

in four different subsample periods: 1986–1988, 1989–1991, 1992 to the second quarter of 1997, 

and the third quarter of 1997 to the second quarter of 2003. We separate the first two periods 

from each other in order to separate the post–Regulation Q period (1986–1988) from the 

exogenous effect of a banking crisis, recession, and credit crunch (1989–1991)—an effect that 

would have distorted the sensitivity of bank earnings to interest-rate and other shocks. The 

Riegle-Neal provision that allows bank mergers across state lines became effective on June 1, 

1997. As a result, we would expect some notable differences between the last subsample period 

and previous periods in earnings sensitivity to interest-rate and other shocks. We present the 

estimation results of the models for the subsample periods for net interest margin for selected 

bank groups in tables 3A through 3C.13 In this section, we discuss within-group variations 

across subsample periods, focusing primarily on variables that exhibit the greatest variations. 

We expect that the progressive movement to greater competition in banking may have 

increased the ability of banks to adjust loan and deposit rates more readily as the economic 

environment changed. Accordingly, this increased ability may have smoothed changes in net 

interest margin and reduced the effect of interest-rate changes on net interest margins. In 

contrast, banks may have chosen to specialize more in the face of greater competition, taking 

advantage of clientele more suitable to the bank and avoiding competition with larger banking 

13 International and credit card banks do not have enough observations for subperiod variations to be analyzed; as a 

result, we left them out of this section. In the case of other groups, such as C&I lenders, patterns of their withingroup variation are similar to the patterns of other groups presented in the paper, so to avoid duplication we do not 

report the results. 

45 
 

 

companies. This increase in specialization may have resulted in portfolio structures that are less 

flexible. 

5.2.1 Interest-Rate Shocks 

As with the full sample, lagged values of net short-term assets (STGAP_RAT2) have 

generally negative coefficients, when significant, while lagged values of nonmaturing deposits 

(NM_RAT2) have significantly positive coefficients, when significant. The coefficient for 

interest-rate volatility shifts from being positive in the 1992–1997:Q2 period to negative in the 

1997:Q3–2003:Q2 period. Two factors potentially explain these results. One is differences in 

the market interest-rate environment between the two periods, if interest-rate volatility actually 

captures the movement in the short-term interest rate. However, our model includes a dummy 

variable for the change in short-term interest rates (ST_DUMMY1) that controls for the change 

in the interest-rate environment. Coefficients for interest-rate volatility and the short-term 

interest-rate dummy variables are often both statistically significant, although they generally 

have opposite signs. The second possible explanation is that an increase in market competition 

in the later period may have constrained the banks’ ability to factor interest-rate volatility into 

their pricing decisions. These empirical findings indicate that, despite diversification, most 

banks are still unable to hedge interest-rate risk effectively by using off-balance-sheet 

instruments.14 

5.2.2 Term-Structure Shocks 

14 Our results show that large banks are better able to hedge interest-rate volatility. The coefficient for interest-rate 

volatility is positive for large noninternational banks in the most recent period. We did not present subsample 

results for large noninternational banks because of the small size of N in earlier periods. 

46 
 

Our results show that earnings sensitivity to term-structure shocks has changed over time 

for most banks. Most banks appear to remain vulnerable to term-structure shocks in the post– 

Riegle-Neal era. Interestingly, in many cases, changes in the yield spread had weaker 

explanatory power in earlier periods than in the more recent period. This may reflect the fact 

that many banks faced significant financial difficulties in earlier periods, and therefore their 

earnings performance was primarily determined by bank-specific factors rather than market 

factors. As a result, a more useful comparison may be that of coefficients between the 1992– 

1997:Q2 period and the 1997:Q3–2003:Q2 period. The coefficient for the one-quarter lagged 

yield spread (DS5Y_1Y1) changed sign from positive in the 1992–1997:Q2 period to negative in 

the more recent period, while net interest margins are consistently and positively related to twoquarter and three-quarter lagged yield spreads (DS5Y_1Y2 and DS5Y_1Y3), when significant. 

These results suggest that changes in the narrowing of the Treasury yield spread still have 

lingering and positive effects on net interest margins, but with a greater lag than previously. 

Although some lenders, such as mortgage specialists and small nonspecialist banks, appear to be 

somewhat less sensitive to term-structure shocks in the more recent period than previously, 

others, including commercial lenders, seem to be more sensitive to term-structure shocks. These 

results may reflect changes in mortgage specialists and smaller banks’ balance-sheet and 

earnings composition as well as the use of greater off-balance-sheet funding vehicles, such as 

securitization, which are not captured in our model. 

5.2.3 Credit Shocks 

Many banks appear to be better able to price expected changes in credit risk in the more 

recent period than previously. The coefficient for the change in the credit spread (DCSPRD1) 

47 
 

was generally negative in the 1992–1997:Q2 period but was positive in the most recent period. 

This result is similar to what we found for the change in the nonperforming asset ratio. The main 

difference between these two explanatory variables is that the credit spread tends to be a 

forward-looking indicator, whereas the nonperforming asset ratio is backward looking. More to 

the point, we can view the lagged change in the credit spread as the anticipated change in credit 

risk, and the lagged change in the nonperforming asset ratio as the actual change in credit risk. 

Together, these results suggest that banks may have become more efficient in pricing credit risk 

in the more recent period, as market competition increased while regulatory restrictions relating 

to loan rates eased. For agricultural banks, mortgage loan specialists, C&I specialists, and other 

nonspecialist banks with real assets less than $50 million, coefficients for lagged values of 

nonperforming assets in the net interest margin model shifted from being negative in earlier 

subsample periods to being significantly positive in the last subperiod. These results may also 

suggest that in the more recent period, banks are relying more on pricing than on quantity control 

in response to heightened credit risk. If an increase in credit risk primarily results in tighter 

underwriting standards or the curtailment of loan supply, as was the case during the credit crunch 

in the 1980s and 1990s, greater credit spreads would depress earnings. The change in credit 

spread in the most recent period leads to improvement in NIM and implies that banks respond to 

higher credit risk more by charging higher rates through risk-based pricing and other tools than 

by reducing loan supply. 

6. Conclusion 

This paper presents a dynamic model of bank behavior that captures net interest margin 

dynamics as banks face unanticipated credit, interest-rate, and term-structure shocks. We show 

48 
 

that net interest margins for commercial banks in different bank groups, defined in terms of the 

product-line specialization and asset size of each bank at the end of each quarter, usually respond 

in a predictable yet dissimilar way to unanticipated shocks. These differences likely reflect a 

significant variation in asset composition (in terms of repricing frequencies and credit risks) and 

business model across these groups. We present the results of cross-section time series randomeffects regression estimation that show that quarterly changes in net interest are sensitive to 

credit, interest-rate, and term-structure shocks for most bank groups, but with varying degrees. 

The size of the coefficient for variables that proxy these shocks, as well as the speed of changes 

in net interest margins in response to term-structure shocks, vary widely across these groups. In 

general, large and more diversified banks seem to be vulnerable to credit shocks but less 

sensitive to interest-rate or term-structure shocks. In comparison, credit card specialists are not 

sensitive to increased short-term interest-rate volatility or a change in the shape of the yield 

curve. As hypothesized, we show that the greater the proportion of net short-term assets and 

nonmaturing deposits a bank holds, the more positive the effect of an increase in short-term 

interest rates on net interest margins. Finally, we present evidence that banks continue to be 

sensitive to credit, interest-rate, and term-structure shocks even after they are allowed to operate 

across state lines. However, for some bank groups the sensitivity to term-structure shocks seems 

to have lessened somewhat, although the results are not universal. On the other hand, banks 

seem to be more constrained than previously in their ability to factor in interest-rate volatility, 

possibly because of increased competition in the general lending arena. Finally, our results 

suggest that many banks appear to be able to price actual and expected credit risk more 

efficiently than in earlier periods. 

49 
 

The models estimated and the results reported in this study are a comprehensive 

contribution to an understanding of the systematic effects of changes in interest-rate and credit 

risks on bank NIM, one of the principal elements of bank cash flows, and after-tax earnings. 

Since a fundamental risk to the earnings and equity valuation of a majority of FDIC-insured 

institutions largely arises from credit and interest-rate risks, these bank risks remain the key 

elements underlying the contingent liability borne by the FDIC insurance funds. Modeling the 

magnitude and significance of these effects is an important contribution to the FDIC for its riskmanagement purposes. These models allow the FDIC to pose scenarios, such as an unanticipated 

increase in interest rates, and trace the effects on the rapidity with which bank profitability might 

weaken and the degree to which banks could become exposed to the adverse effects of credit 

risks. Further analysis can contribute to estimating the increase in the chance of bank default and 

failure and the increase in the FDIC’s chances of loss. 

Future research may expand these models to other earnings components, such as 

noninterest income and security gains and losses so as to more consistently model interest-rate 

risk as it independently affects each component of bank earnings. With new banking activities 

such as securitization that allow banks to move certain assets off the balance sheet, certain 

components of noninterest income have likely become more sensitive to interest-rate shocks. If 

various earning components for certain bank groups respond similarly to unanticipated shocks 

over time, the benefits these banks derive from earnings diversification would be more limited 

than otherwise. Future studies may also want to model explicitly the competitive market 

structure in which the bank operates and the effect of that structure on banks’ abilities to respond 

to unanticipated shocks. The local competitive landscape may have a particularly significant 

effect on small, locally based banks. However, lack of data on bank loan concentrations in local 

50 
 

areas continues to challenge researchers in this endeavor. Finally, developing a new and more 

refined methodology to classify the banking industry into unique product-line specializations 

would enhance our understanding of various business models. 

51 
 

APPENDIX 1: Descriptions of Bank Groupings 

Bank Grouping Description 

International banks 

Large noninternational 

banks 

Agricultural banks 

Credit card specialists 

Commercial and industrial 

(C&I) loan specialists 

Commercial real estate 

(CRE) loan specialists 

Commercial loan specialists 

Mortgage loan specialists 

Consumer loan specialists 

Other small specialists 

Small nonspecialist banks 

Midtier nonspecialist banks 

Banks with real assets over $10 billion and foreign assets over 25 

percent of total assets. 

Noninternational banks with real assets over $10 billion. 

Banks with the sum of agricultural and farmland-secured loans in 

excess of 25 percent of total assets. 

Banks with the sum of credit card loans, credit card-related asset 

backed securities, and credit card loans sold and securitized with 

recourse in excess of 50 percent of total assets. 

Banks with C&I loans in excess of 25 percent of total assets. 

Banks with CRE loans in excess of 25 percent of total assets. 

Banks that are not classified as C&I or CRE specialists and the sum 

of whose C&I and CRE loans is in excess of 25 percent of total 

assets. The group is further broken down into three subgroups by 

real asset size: less than $50 million, between $50 million and $300 

million, and greater than $300 million in real assets. 

Banks with the sum of mortgage loans and mortgage-backed 

securities in excess of 50 percent of total assets. 

Banks that are not classified as credit card specialists or mortgage 

specialists and the sum of whose consumer loans and residential 

real estate loans is in excess of 50 percent of total assets. 

Banks with a loan-to-asset ratio less than 40 percent and real assets 

less than $1 billion. 

Banks not in other groups and with real assets less than $1 billion. 

The group is further broken down into three subgroups by real asset 

size: less than $50 million, between $50 million and $300 million, 

and greater than $300 million in real assets. 

Banks not in other groups and with real assets between $1 billion 

and $10 billion. 

52 
 

APPENDIX 2: Description of Explanatory Variables 

Variable 

Name Description Expected Sign 

Interest-Rate Risk 

vol_1y1 Standard deviation of one-year Treasury yields (weekly series) in t-1. – 

st_dummy1 1 if the one-year Treasury yield increased between t and t-1; 

0 if the one-year Treasury yield decreased between t and t-1. – 

stgap_rat2 The ratio of assets with remaining maturity less than one year less 

liabilities with remaining maturity less one year (net short-term 

assets) to earning assets in t-2. – 

nm_rat2 The ratio of nonmaturing deposits (transactional deposits, money 

market, and regular savings) to earning assets in t-2. + 

stgap_sd1 Interaction term between stgap_rat2 and st_dummy1. + 

nm_sd1 Interaction term between nm_rat2 and st_dummy1. + 

Term-Structure Risk 

ds5y_1y1 Difference in 5-year and 1-year Treasury yield spreads between t-2 

and t-1. + 

ds5y_1y2 Difference in 5-year and 1-year Treasury yield spreads between t-3 

and t-2. + 

ds5y_1y3 Difference in 5-year and 1-year Treasury yield spreads between t-4 

and t-3. +/– 

ds5y_1y4 Difference in 5-year and 1-year Treasury yield spreads between t-5 

and t-4. +/– 

Credit Risk 

dln_ast2 Difference in the loan-to-earning assets ratio between t-3 and t-2. + 

dci_rat2 Difference in the commercial and industrial (C&I) loans to earning 

assets ratio between t-3 and t-2. + 

53 
 

Dcsprd1 Difference in Baa-rated and Aaa-rated corporate bond yield spreads 

between t-2 and t-1. – 

Variable 

Name Description Expected Sign 

dnperf_rat2 Difference in the nonperforming assets to earning assets ratio between 

t-3 and t-2. – 

Other Institutional Variables 

Logast2 Log of total assets in t-2. – 

nim_rat2 Annualized quarterly net interest income divided by average earning 

assets (net interest margin)—two-quarter lag (t-2). – 

Roa2 Annualized quarterly net income divided by average earning assets 

(return on average earning assets)—two- quarter lag (t-2). – 

dnim_rat1 Difference in net interest margins between t-2 and t-1. – 

droa1 Difference in the return on average earning assets between t-2 and t-1. – 

Dnonii_rat2 Difference in the noninterest income to average earning assets ratio 

between t-3 and t-2. +/– 

Dsecgl_rat2 Difference in the security gains/losses to average earning assets ratio 

between t-3 and t-2. +/– 

Seasonal Dummy 

qtr2 1 if the reporting period is the second quarter of the year, 

0 otherwise. +/– 

qtr3 1 if the reporting period is the third quarter of the year, 

0 otherwise. +/– 

qtr4 1 if the reporting period is the fourth quarter of the year, 

0 otherwise. + 

54 
 

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58 
 

Table 1: Descriptive Statistics by Bank Grouping 

Variables included in this table are net interest income to average earning assets (NIM_RAT), return on average assets (ROA), 

noninterest income to average earning assets (NONII_RAT), security gains/losses to average earning assets (SECGL_RAT), 

loans to earning assets ratio (LN_AST), C&I loans to earning assets ratio (CI_RAT), nonperforming assets to earning assets ratio 

(NPERF_RAT), net short-term assets to earning assets ratio (STGAP_RAT), and log of total assets (LOGAST). All variables 

beginning with “d”—for example, DNIM_RAT—represent quarterly changes. See appendix 2 for further details on variables. 

Variables 

International Banks Large Non-Internatinoal Banks 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 2.84 2.72 1.06 0.07 10.06 4.08 4.01 1.29 -0.02 17.60 

dnim_rat -0.01 -0.02 0.55 -7.36 8.27 -0.01 -0.02 0.58 -7.20 10.47 

roa 0.71 1.01 1.60 -12.98 6.25 1.33 1.40 1.49 -26.70 12.82 

droa -0.01 0.00 2.15 -14.54 13.85 -0.01 0.00 1.48 -22.79 14.95 

nonii_rat 2.89 2.59 1.35 -0.87 11.90 2.86 2.00 3.25 -4.35 28.33 

dnonii_rat 0.02 0.02 1.06 -7.70 11.44 0.01 0.01 1.39 -16.04 18.52 

secgl_rat 0.07 0.04 0.16 -1.71 1.06 0.05 0.01 0.27 -4.80 2.98 

dsecgl_rat 0.00 0.00 0.19 -1.79 1.94 0.00 0.00 0.35 -4.77 4.80 

ln_ast 59.05 68.83 22.64 7.05 89.39 75.16 76.01 13.61 7.67 105.17 

dln_ast -0.26 -0.22 3.18 -21.83 15.36 0.08 0.08 4.26 -39.71 49.88 

ci_rat 25.07 25.60 12.50 0.29 55.37 22.33 21.48 11.79 0.00 69.96 

dci_rat -0.20 -0.15 1.58 -7.48 6.27 -0.11 -0.03 2.08 -17.42 22.52 

nperf_rat 2.38 1.83 2.12 0.00 9.08 1.50 0.91 1.77 0.05 24.62 

dnperf_rat -0.01 -0.02 0.44 -1.98 2.89 0.02 -0.01 0.44 -2.08 7.11 

stgap_rat -1.89 -4.86 17.77 -53.25 47.47 -31.86 -33.59 16.72 -76.31 59.16 

nm_rat 26.74 27.16 13.82 1.39 63.67 45.14 44.66 16.32 0.02 100.37 

logast 17.68 17.60 0.76 16.21 19.70 16.77 16.62 0.61 16.12 19.70 

Agricultural Banks Credit Card Specialists 

Variables Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 4.32 4.27 0.86 -12.56 27.89 10.65 9.34 8.03 -67.66 163.72 

dnim_rat 0.00 0.00 0.63 -23.34 27.13 0.09 0.04 5.95 -106.72 104.23 

roa 1.06 1.23 1.59 -111.46 48.21 3.31 2.33 7.55 -137.90 113.01 

droa 0.02 0.01 2.04 -96.26 63.77 0.18 0.04 7.03 -126.57 93.44 

nonii_rat 0.63 0.52 0.64 -37.37 45.64 17.33 5.39 40.13 -52.44 823.58 

dnonii_rat 0.00 0.00 0.59 -37.75 41.26 0.59 0.01 17.40 -189.27 384.50 

secgl_rat 0.03 0.00 0.40 -29.19 69.51 0.00 0.00 0.29 -14.22 2.56 

dsecgl_rat 0.00 0.00 0.54 -69.57 69.51 0.00 0.00 0.38 -13.92 14.22 

ln_ast 55.55 56.39 16.68 0.21 115.60 93.45 100.63 15.35 0.00 179.67 

dln_ast 0.22 0.37 4.05 -59.58 57.54 0.42 0.05 7.84 -82.41 86.27 

ci_rat 7.80 6.86 4.95 0.00 55.14 1.99 0.00 4.63 0.00 44.61 

dci_rat -0.01 -0.01 1.33 -39.28 29.58 -0.07 0.00 2.33 -90.32 22.54 

nperf_rat 1.48 0.80 2.18 -0.81 46.29 1.94 1.42 2.22 0.00 39.83 

dnperf_rat -0.03 -0.01 0.84 -22.08 26.18 0.03 0.01 1.04 -21.76 30.68 

stgap_rat -31.07 -32.79 17.81 -144.94 98.72 -6.72 -5.22 49.61 -163.96 117.49 

nm_rat 43.33 42.43 10.33 0.00 215.64 14.06 2.42 20.53 0.00 100.40 

logast 9.95 9.94 0.77 6.51 13.78 12.74 12.62 1.81 7.21 16.11 

Variables 

C&I Specialists CRE Specialists 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 5.04 4.89 1.55 -19.85 35.60 4.97 4.88 1.22 -13.11 34.10 

dnim_rat -0.01 0.00 0.85 -40.66 29.07 -0.02 -0.01 0.66 -25.93 29.51 

roa 0.23 0.99 4.21 -199.12 39.53 0.90 1.18 2.39 -122.89 44.62 

droa -0.08 0.01 4.60 -195.20 206.50 -0.02 0.02 2.46 -116.79 88.58 

nonii_rat 1.45 0.96 6.15 -14.87 512.58 1.11 0.85 1.33 -21.40 55.17 

dnonii_rat 0.02 0.01 1.57 -57.08 87.11 0.00 0.00 0.86 -46.51 50.92 

secgl_rat 0.04 0.00 0.31 -11.60 9.08 0.02 0.00 0.25 -23.50 9.15 

dsecgl_rat 0.00 0.00 0.42 -11.79 17.61 0.00 0.00 0.32 -23.50 17.78 

ln_ast 76.02 76.64 10.82 28.62 123.13 76.30 76.85 10.43 31.71 123.23 

dln_ast 0.56 0.49 5.14 -56.57 88.09 0.36 0.39 4.30 -37.04 71.45 

ci_rat 37.27 34.62 9.19 25.32 101.48 12.34 11.88 6.68 0.00 37.87 

dci_rat 0.46 0.33 4.34 -52.81 88.67 -0.22 -0.05 2.41 -68.60 23.69 

nperf_rat 2.69 1.40 3.85 -1.83 58.87 1.94 0.82 3.54 -11.85 63.62 

dnperf_rat 0.15 0.00 1.40 -24.26 46.22 0.07 0.00 1.08 -45.86 47.35 

stgap_rat -25.24 -26.27 18.38 -109.26 101.81 -32.03 -32.99 18.44 -166.68 91.77 

nm_rat 53.04 53.09 17.65 0.00 125.87 51.25 50.69 17.09 0.00 203.15 

logast 11.17 10.90 1.41 7.21 16.12 11.32 11.22 1.05 7.08 16.07 

59 
Table 1: Descriptive Statistics by Bank Groupings 

Variables included in this table are net interest income to average earning assets (NIM_RAT), return on average assets (ROA), noninterest income to average earning assets (NONII_RAT), security gains/losses to average earning assets (SECGL_RAT), loan-toearning asset ratio (LN_AST), C&I loans to earning asset ratio (CI_RAT), non-performing asset to earning asset ratio 

(NPERF_RAT), net short-term assets to earning asset ratio (STGAP_RAT) and log of total assets (LOGAST). All variables 

beginning with “d” – for example DNIM_RAT – represent quarterly changes. See Appendix 2 for further details on variables. 

Variables 

Commerical Loan Specialists -- Real Assets <=$50 Mil. Commerical Loan Specialists -- Real Assets $50 - 300 Mil. 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 5.00 4.91 1.06 -17.18 32.64 4.66 4.60 0.87 -11.87 21.74 

dnim_rat 0.00 0.00 0.64 -23.28 23.05 -0.01 0.00 0.47 -17.81 15.64 

roa 0.60 1.05 2.79 -153.73 41.66 1.04 1.22 1.60 -66.22 45.12 

droa 0.01 0.03 3.19 -147.59 137.16 0.00 0.01 1.83 -63.16 63.17 

nonii_rat 1.18 0.95 1.19 -18.38 58.04 1.08 0.89 1.32 -17.46 67.89 

dnonii_rat 0.00 0.00 1.00 -122.81 27.21 0.01 0.00 0.79 -52.60 61.78 

secgl_rat 0.03 0.00 0.30 -15.89 16.65 0.04 0.00 0.27 -14.72 9.34 

dsecgl_rat 0.00 0.00 0.41 -16.47 16.58 0.00 0.00 0.36 -15.62 14.72 

ln_ast 69.41 69.90 10.72 24.46 111.15 69.07 69.66 10.71 26.97 103.53 

dln_ast 0.17 0.27 4.51 -42.58 40.99 0.08 0.24 3.44 -47.88 50.05 

ci_rat 17.01 16.96 6.04 0.24 42.77 15.59 15.19 5.88 0.23 35.04 

dci_rat -0.08 0.00 2.75 -58.08 29.11 -0.09 -0.02 2.07 -43.16 22.67 

nperf_rat 2.27 1.15 3.72 -1.63 204.54 1.64 0.89 2.43 -0.97 65.80 

dnperf_rat 0.03 0.00 1.14 -22.46 34.97 0.02 -0.01 0.73 -27.23 31.41 

stgap_rat -33.44 -34.63 17.95 -136.06 54.67 -36.91 -37.88 16.30 -163.32 56.15 

nm_rat 53.14 51.92 15.26 0.00 157.84 52.67 51.40 13.23 0.01 141.77 

logast 10.16 10.24 0.48 7.30 10.82 11.56 11.49 0.48 10.82 12.61 

Variables 

Commerical Loan Specialists -- Real Assets >$ 300 Mil. Mortgage Specialists 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 4.41 4.40 0.82 -1.10 18.81 3.95 3.84 1.13 -15.25 25.16 

dnim_rat -0.01 0.00 0.43 -15.36 14.53 0.00 0.00 0.54 -20.38 14.28 

roa 1.05 1.25 1.47 -42.41 22.03 0.91 0.95 1.33 -42.71 46.79 

droa -0.01 0.01 1.53 -40.09 42.06 0.01 0.00 1.45 -54.88 45.71 

nonii_rat 1.54 1.28 1.71 -3.94 37.38 0.79 0.44 2.01 -32.05 85.93 

dnonii_rat 0.01 0.01 0.68 -17.59 20.29 0.00 0.00 1.09 -84.57 50.77 

secgl_rat 0.04 0.00 0.28 -5.92 7.42 0.05 0.00 0.37 -14.32 13.82 

dsecgl_rat 0.00 0.00 0.35 -8.36 6.80 0.00 0.00 0.49 -16.31 14.53 

ln_ast 71.78 72.69 10.92 28.39 102.15 66.52 70.41 19.31 0.00 122.72 

dln_ast 0.05 0.22 3.34 -37.12 47.62 0.09 0.08 3.55 -84.83 58.48 

ci_rat 17.30 17.32 5.95 1.47 56.99 2.87 1.68 3.45 0.00 26.36 

dci_rat -0.09 -0.01 1.81 -24.69 18.78 -0.01 0.00 1.08 -78.14 21.70 

nperf_rat 1.64 0.95 2.12 -2.23 40.25 1.22 0.60 1.99 -4.75 78.77 

dnperf_rat 0.02 -0.01 0.67 -22.83 20.87 -0.01 -0.01 0.74 -31.82 67.99 

stgap_rat -38.44 -39.19 15.08 -135.34 44.88 -41.49 -43.37 21.82 -166.53 106.00 

nm_rat 52.35 51.74 12.97 0.00 176.65 41.79 41.50 16.41 0.00 109.45 

logast 13.79 13.52 0.95 12.61 16.12 11.33 11.21 1.33 6.66 16.10 

Variables 

Consumer Loan Specialists Other Small Specialists 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 3.70 3.63 1.00 -7.62 25.16 3.99 3.96 1.02 -8.21 36.17 

dnim_rat -0.01 -0.02 0.42 -19.60 13.51 -0.03 -0.02 0.72 -52.21 28.15 

roa 0.98 0.95 1.24 -42.71 46.79 1.26 1.18 3.77 -60.51 439.70 

droa -0.01 0.00 1.36 -54.88 45.71 0.03 0.00 4.18 -437.39 434.83 

nonii_rat 1.10 0.71 5.27 -5.19 321.09 1.79 0.64 11.18 -18.75 737.07 

dnonii_rat 0.01 0.00 1.34 -84.57 50.77 0.01 0.00 6.68 -733.19 721.77 

secgl_rat 0.02 0.00 0.25 -9.27 8.69 0.06 0.00 0.90 -22.92 102.57 

dsecgl_rat 0.00 0.00 0.41 -14.77 13.85 0.00 0.00 1.22 -98.10 102.34 

ln_ast 77.41 77.13 8.76 45.84 115.57 29.73 31.82 8.40 -1.12 40.00 

dln_ast -0.02 0.04 3.35 -84.83 47.77 -0.48 -0.17 3.83 -100.62 36.09 

ci_rat 5.75 5.12 4.21 0.00 27.29 5.26 4.46 4.07 -3.58 36.87 

dci_rat 0.02 0.00 0.82 -25.95 11.45 -0.14 -0.03 1.58 -60.66 21.24 

nperf_rat 1.34 0.78 1.86 0.00 33.47 0.89 0.43 2.45 -1.36 196.05 

dnperf_rat -0.01 0.00 0.42 -21.33 14.90 -0.05 -0.01 1.29 -101.04 160.83 

stgap_rat -49.59 -51.14 18.28 -102.75 106.00 -40.42 -44.09 24.60 -156.93 101.12 

nm_rat 39.79 39.34 16.19 0.00 109.45 51.46 49.83 17.55 0.00 225.05 

logast 11.46 11.34 1.33 6.76 16.10 10.55 10.53 1.03 6.87 13.81 
 

 

Table 1: Descriptive Statistics by Bank Groupings 

Variables included in this table are net interest income to average earning assets (NIM_RAT), return on average assets (ROA), noninterest income to average earning assets (NONII_RAT), security gains/losses to average earning assets (SECGL_RAT), loan-toearning asset ratio (LN_AST), C&I loans to earning asset ratio (CI_RAT), non-performing asset to earning asset ratio 

(NPERF_RAT), net short-term assets to earning asset ratio (STGAP_RAT) and log of total assets (LOGAST). All variables 

beginning with “d” – for example DNIM_RAT – represent quarterly changes. See Appendix 2 for further details on variables. 

Variables 

Small Non-Specialists w/ Real Assets <=$50 Mil. Small Non-Specialists w/ Real Assets $50 - $300 Mil. 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 4.65 4.58 0.93 -22.62 39.40 4.35 4.32 0.82 -21.93 50.32 

dnim_rat -0.01 0.00 0.65 -85.52 23.96 -0.01 0.00 0.48 -41.64 27.12 

roa 0.95 1.15 1.82 -125.17 67.43 1.20 1.27 1.12 -38.73 98.62 

droa 0.01 0.01 2.18 -123.13 124.24 0.01 0.01 1.28 -98.39 86.06 

nonii_rat 0.96 0.75 1.65 -10.77 134.68 0.95 0.70 2.76 -66.10 162.65 

dnonii_rat 0.00 0.00 0.87 -59.41 87.25 0.00 0.00 0.94 -66.98 57.92 

secgl_rat 0.04 0.00 0.37 -44.60 24.68 0.03 0.00 0.29 -15.06 11.72 

dsecgl_rat 0.00 0.00 0.49 -45.62 44.71 0.00 0.00 0.40 -15.30 16.34 

ln_ast 58.91 58.61 10.46 40.00 104.56 59.62 59.58 10.49 40.00 106.95 

dln_ast 0.01 0.13 3.71 -54.56 89.10 0.01 0.13 3.24 -59.72 99.98 

ci_rat 8.69 7.96 5.09 0.00 37.67 8.73 8.03 4.83 0.00 63.20 

dci_rat -0.09 -0.03 1.80 -65.01 22.00 -0.09 -0.03 1.46 -37.48 33.48 

nperf_rat 1.58 0.90 2.45 -0.84 135.12 1.13 0.71 1.44 -0.44 30.50 

dnperf_rat -0.03 -0.01 0.86 -50.65 71.97 -0.03 -0.02 0.47 -16.26 13.08 

stgap_rat -37.79 -39.44 19.04 -202.53 107.11 -39.38 -41.01 17.92 -224.11 100.00 

nm_rat 49.66 48.09 13.62 0.00 184.46 49.39 48.08 12.68 0.00 204.15 

logast 10.01 10.12 0.57 7.11 10.82 11.44 11.34 0.46 10.82 12.61 

Variables 

Small Non-Specialists w/ Real Assets >$300 Mil. Mid-Tier Non-Specialists 

Mean Median St. Dev. Min. Max Mean Median St. Dev. Min. Max 

nim_rat 4.29 4.27 0.82 -1.36 20.74 3.97 4.13 1.17 -4.11 20.83 

dnim_rat 0.00 0.00 0.47 -16.24 15.63 -0.04 -0.01 0.70 -15.89 15.25 

roa 1.17 1.26 1.16 -52.36 20.47 1.24 1.21 1.44 -9.44 51.95 

droa -0.02 0.01 1.50 -81.17 18.94 -0.01 0.00 1.49 -48.04 53.71 

nonii_rat 1.37 1.09 1.36 -4.80 30.95 2.16 1.49 3.23 -1.09 78.11 

dnonii_rat -0.01 0.01 1.69 -128.33 18.41 -0.01 0.01 1.89 -74.68 72.99 

secgl_rat 0.04 0.00 0.30 -5.47 8.07 0.05 0.00 0.35 -7.34 4.74 

dsecgl_rat 0.00 0.00 0.39 -7.63 7.36 0.00 0.00 0.43 -7.34 7.37 

ln_ast 62.58 62.93 10.70 40.01 98.31 58.04 60.86 16.95 0.01 100.00 

dln_ast -0.09 0.05 3.82 -33.60 49.69 -0.38 -0.02 5.88 -94.02 99.99 

ci_rat 10.10 9.62 4.89 0.00 26.73 11.30 11.42 5.82 0.00 33.12 

dci_rat -0.15 -0.04 1.52 -21.99 16.84 -0.17 -0.02 1.87 -41.79 19.56 

nperf_rat 1.03 0.64 1.47 -0.89 33.85 1.08 0.72 1.28 -0.98 13.75 

dnperf_rat -0.03 -0.02 0.38 -4.67 11.24 -0.05 -0.02 0.33 -4.63 3.95 

stgap_rat -44.26 -45.92 16.46 -181.19 59.97 -41.02 -44.11 21.78 -144.21 99.96 

nm_rat 53.41 53.34 13.22 0.64 224.55 51.85 51.54 18.60 0.02 183.29 

logast 13.08 13.01 0.33 12.61 13.81 14.63 14.55 0.60 13.82 16.12 

61 
 

 

Table 2A: Cross-sectional Time Series Regression with Random Effects (NIM) 1986:Q1–2003:Q2 

Independent variables are presented by types of risk they represent— interest-rate risk, term-structure risk, and credit risk; also presented are 

institutional variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutionalspecific variables other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables 

beginning with “d”—for example, DNIM_RAT—represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two-quarter (t-2) 

lags, respectively. The Hausman test rejected random effects for "international" institutions; therefore, results for international banks represent an 

estimate using cross-sectional time series regression with fixed effects. 

Large NonInternational International Agricultural Credit Card C&I CRE 

Banks* Banks Banks Specialists Specialists Specialists 

Intercept 3.3067 *** 1.2475 *** 1.8520 *** 5.3794 *** 2.2551 *** 2.0949 *** 

(1.0724) (0.3965) (0.043) (1.0838) (0.0919) (0.0551) 

Interest Rate Risk 

vol_1y1 -0.0714 0.1489 -0.0412 *** -1.3689 * -0.2287 *** -0.0515 ** 

(0.1651) (0.1164) (0.011) (0.7649) (0.0357) (0.0225) 

st_dummy1 -0.1092 -0.3171 *** -0.0560 *** -0.0435 -0.0616 ** -0.0741 *** 

(0.0794) (0.0743) (0.0107) (0.1857) (0.0251) (0.0148) 

stgap_rat2 -0.0035 * -0.0023 -0.0012 *** -0.0044 0.0017 *** -0.0001 

(0.0021) (0.0015) (0.0001) (0.0027) (0.0004) (0.0002) 

nm_rat2 0.0034 0.0009 0.0074 *** 0.0041 0.0102 *** 0.0070 *** 

(0.0033) (0.0017) (0.0002) (0.0028) (0.0005) (0.0003) 

stgap_sd1 0.0031 0.0028 ** 0.0032 *** 0.0081 ** 0.0040 *** 0.0037 *** 

(0.002) (0.0015) (0.0001) (0.0033) (0.0005) (0.0003) 

nm_sd1 0.0031 0.0074 *** 0.0034 *** -0.0022 0.0037 *** 0.0043 *** 

(0.0026) (0.0015) (0.0002) (0.0034) (0.0005) (0.0003) 

Term Structure Risk 

ds5y_1y1 -0.1232 0.0395 -0.0228 *** -0.0506 -0.0791 *** -0.0019 

(0.0809) (0.0531) (0.0054) (0.3879) (0.0167) (0.0101) 

ds5y_1y2 0.0252 0.0764 0.0842 *** 0.3319 0.0007 0.0155 

(0.0849) (0.0541) (0.0057) (0.3959) (0.0182) (0.0102) 

ds5y_1y3 -0.0493 0.1390 *** 0.0979 *** 0.3410 0.0670 *** 0.0666 *** 

(0.0842) (0.0534) (0.0057) (0.3985) (0.0183) (0.0102) 

ds5y_1y4 0.1583 ** -0.0296 0.0159 *** -0.5244 0.0173 0.0270 *** 

(0.0764) (0.0519) (0.0051) (0.3636) (0.0162) (0.0098) 

Credit Risk 

dln_ast2 0.0103 0.0085 *** -0.0008 ** 0.0076 -0.0030 *** -0.0006 

(0.0067) (0.0028) (0.0003) (0.0089) (0.0009) (0.0005) 

dci_rat2 -0.0082 -0.0003 0.0065 *** -0.0139 0.0011 -0.0006 

(0.0139) (0.0062) (0.0009) (0.0294) (0.0011) (0.0009) 

dcsprd1 0.0836 0.0739 -0.1264 *** -0.6030 -0.1627 *** -0.0472 ** 

(0.1934) (0.1174) (0.0128) (0.8826) (0.0423) (0.0224) 

dnperf_rat2 -0.0166 -0.0427 ** 0.0081 *** -0.5330 *** -0.0134 *** -0.0147 *** 

(0.0416) (0.0166) (0.0014) (0.0938) (0.0032) (0.0022) 

Other Institutional Variables 

logast1 -0.1466 ** -0.0085 -0.0643 *** -0.1205 -0.0880 *** -0.1048 *** 

(0.0616) (0.0225) (0.0039) (0.0812) (0.0073) (0.0043) 

nim_rat2 -0.4520 *** -0.3146 *** -0.3834 *** -0.3226 *** -0.3759 *** -0.2917 *** 

(0.0423) (0.0189) (0.0022) (0.016) (0.0048) (0.0032) 

dnim_rat1 -0.6740 *** -0.5301 *** -0.6605 *** -0.5873 *** -0.5755 *** -0.5378 *** 

(0.0378) (0.0223) (0.0021) (0.0164) (0.0051) (0.0038) 

dnonii_rat2 -0.0211 0.0233 *** 0.0036 * -0.0017 0.0003 0.0055 ** 

(0.0165) (0.008) (0.002) (0.005) (0.0022) (0.0023) 

dsecgl_rat2 -0.0056 0.0089 -0.0067 *** -1.0742 *** -0.0153 * -0.0124 * 

(0.087) (0.0318) (0.0023) (0.2034) (0.0086) (0.007) 

Seasonal Dummy 

qtr2 -0.0273 0.0840 ** 0.0951 *** -0.1682 0.1325 *** 0.1615 *** 

(0.0539) (0.0368) (0.0038) (0.2545) (0.0116) (0.0071) 

qtr3 0.0704 0.0942 ** 0.1736 *** 0.0256 0.1568 *** 0.1835 *** 

(0.0563) (0.039) (0.0039) (0.2674) (0.0128) (0.0075) 

qtr4 0.2361 *** 0.0842 ** 0.0794 *** -0.3015 0.1177 *** 0.1049 *** 

(0.054) (0.0377) (0.0037) (0.2509) (0.0121) (0.0073) 

# of Cross Sections 15 89 4308 145 2378 3564 

Time Series Length 70 70 70 70 70 70 

R-Square 0.37 0.25 0.36 0.37 0.28 0.26 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

62 
 

 

 

 

Table 2A: Cross-sectional Time Series Regression with Random Effects (NIM) 1986:Q1–2003:Q2 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DNIM_RAT – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, 

respectively. Hausman test rejected random effects for "international" institutions; therefore, results for international banks represent an estimate 

using cross-sectional time series regression with fixed effects. 

Commerical Commerical 

Loan Commerical Loan Loan 

Specialists Specialists $50 - Specialists Mortgage Consumer Other Small 

<=$50 mil. $300 mil. >$300 mil. Specialists Specialists Specialists 

Intercept 1.2048 *** 1.5601 *** 0.9470 *** 1.5647 *** 0.3317 *** 1.9708 *** 

(0.0902) (0.0778) (0.087) (0.0704) (0.0948) (0.0751) 

Interest Rate Risk 

vol_1y1 -0.0864 *** -0.0597 *** -0.0165 0.0626 ** -0.0161 0.0464 * 

(0.0239) (0.0164) (0.0272) (0.0263) (0.0452) (0.0263) 

st_dummy1 -0.0971 *** -0.0764 *** -0.0937 *** -0.1204 *** -0.1388 *** -0.1251 *** 

(0.0192) (0.0151) (0.0257) (0.0158) (0.0317) (0.0172) 

stgap_rat2 -0.0002 -0.0001 -0.0005 0.0008 *** 0.0003 -0.0004 *** 

(0.0003) (0.0002) (0.0003) (0.0002) (0.0004) (0.0002) 

nm_rat2 0.0072 *** 0.0063 *** 0.0046 *** 0.0046 *** -0.0003 -0.0004 

(0.0003) (0.0003) (0.0005) (0.0004) (0.0006) (0.0003) 

stgap_sd1 0.0031 *** 0.0028 *** 0.0024 *** 0.0006 ** 0.0019 *** 0.0030 *** 

(0.0003) (0.0002) (0.0004) (0.0003) (0.0005) (0.0002) 

nm_sd1 0.0040 *** 0.0035 *** 0.0030 *** 0.0025 *** 0.0028 *** 0.0048 *** 

(0.0004) (0.0003) (0.0005) (0.0003) (0.0007) (0.0003) 

Term Structure Risk 

ds5y_1y1 -0.0276 ** 0.0092 0.0065 0.0418 *** 0.0683 *** -0.0166 

(0.0117) (0.0079) (0.0132) (0.0132) (0.023) (0.013) 

ds5y_1y2 0.0341 *** 0.0464 *** 0.0424 *** 0.1090 *** 0.0301 0.0652 *** 

(0.0123) (0.0084) (0.0139) (0.0132) (0.0239) (0.0138) 

ds5y_1y3 0.0727 *** 0.0823 *** 0.0571 *** 0.0893 *** 0.1093 *** 0.0359 *** 

(0.0124) (0.0084) (0.0139) (0.0133) (0.0241) (0.0136) 

ds5y_1y4 0.0272 ** 0.0297 *** 0.0519 *** 0.0565 *** 0.0106 0.0454 *** 

(0.0111) (0.0076) (0.0126) (0.0124) (0.0218) (0.0121) 

Credit Risk 

dln_ast2 -0.0013 ** 0.0009 * 0.0033 *** 0.0023 *** -0.0034 *** 0.0022 *** 

(0.0006) (0.0006) (0.001) (0.0007) (0.0012) (0.0008) 

dci_rat2 0.0012 0.0008 -0.0058 *** 0.0045 * -0.0010 -0.0097 *** 

(0.0009) (0.0009) (0.0018) (0.0025) (0.0027) (0.0019) 

dcsprd1 -0.0836 *** -0.0665 *** -0.0426 -0.1168 *** -0.2021 *** -0.1876 *** 

(0.028) (0.0187) (0.031) (0.0282) (0.0525) (0.0316) 

dnperf_rat2 -0.0049 ** -0.0118 *** -0.0211 *** 0.0041 0.0034 -0.0055 ** 

(0.0023) (0.0025) (0.0047) (0.0041) (0.0062) (0.0022) 

Other Institutional Variables 

logast1 -0.0312 *** -0.0625 *** -0.0155 *** -0.0629 *** -0.0099 -0.0630 *** 

(0.0085) (0.0064) (0.0057) (0.0056) (0.0079) (0.0065) 

nim_rat2 -0.2807 *** -0.2771 *** -0.2461 *** -0.2577 *** -0.0508 *** -0.3557 *** 

(0.0039) (0.0035) (0.0059) (0.0046) (0.0037) (0.0041) 

dnim_rat1 -0.5222 *** -0.5173 *** -0.4909 *** -0.4978 *** -0.2841 *** -0.5931 *** 

(0.0044) (0.004) (0.0072) (0.0054) (0.007) (0.0041) 

dnonii_rat2 0.0043 ** -0.0008 -0.0106 *** 0.0011 -0.0013 -0.0013 *** 

(0.002) (0.0023) (0.0041) (0.0022) (0.0022) (0.0002) 

dsecgl_rat2 0.0092 0.0014 -0.0004 0.0181 *** -0.0192 0.0022 

(0.0058) (0.0045) (0.0078) (0.0052) (0.0153) (0.0024) 

Seasonal Dummy 

qtr2 0.1638 *** 0.1393 *** 0.0863 *** 0.0377 *** 0.1449 *** 0.0544 *** 

(0.0078) (0.0053) (0.0089) (0.0084) (0.0149) (0.0087) 

qtr3 0.1903 *** 0.1592 *** 0.1302 *** 0.0324 *** 0.1699 *** 0.0773 *** 

(0.0083) (0.0056) (0.0094) (0.0088) (0.0157) (0.0093) 

qtr4 0.1011 *** 0.0942 *** 0.0877 *** 0.0275 *** 0.1399 *** 0.0562 *** 

(0.0079) (0.0054) (0.0089) (0.0084) (0.0148) (0.0085) 

# of Cross Sections 3220 3300 917 1659 1220 2146 

Time Series Length 70 70 70 70 70 70 

R-Square 0.25 0.26 0.23 0.21 0.10 0.34 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

63 
 

 

 

 

Table 2A: Cross-sectional Time Series Regression with Random Effects (NIM) 1986:Q1 – 2003:Q2 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DNIM_RAT – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, 

respectively. Hausman test rejected random effects for "international" institutions; therefore, results for international banks represent an estimate 

using cross-sectional time series regression with fixed effects. 

Small Non- Small Non- Small Non- Mid-Tier 

Specialists Specialists $50 - Specialists > Non-

<=$50 mil $300 mil. $300 mil Specialists 

Intercept 2.2397 *** 1.2624 *** 0.4606 * 0.5442 

(0.0643) (0.0689) (0.2379) (0.3499) 

Interest Rate Risk 

vol_1y1 -0.0176 -0.0125 0.0515 -0.0322 

(0.0143) (0.014) (0.0467) (0.0851) 

st_dummy1 -0.1279 *** 0.0350 *** 0.0464 0.0349 

(0.012) (0.0112) (0.0427) (0.0536) 

stgap_rat2 0.0001 -0.0008 *** -0.0004 0.0000 

(0.0000) (0.0002) (0.0005) (0.0000) 

nm_rat2 0.0075 *** 0.0035 *** 0.0058 *** 0.0014 

(0.0002) (0.0002) (0.0008) (0.001) 

stgap_sd1 0.0017 *** 0.0036 *** 0.0027 *** 0.0025 *** 

(0.0001) (0.0002) (0.0006) (0.0008) 

nm_sd1 0.0034 *** 0.0019 *** 0.0008 0.0003 

(0.0002) (0.0002) (0.0008) (0.001) 

Term Structure Risk 

ds5y_1y1 -0.0345 *** 0.0178 *** 0.0370 * -0.0014 

(0.007) (0.0068) (0.0222) (0.0405) 

ds5y_1y2 0.0587 *** 0.0635 *** 0.0601 ** -0.0409 

(0.0075) (0.0073) (0.0234) (0.042) 

ds5y_1y3 0.0901 *** 0.0966 *** 0.0500 ** 0.0039 

(0.0075) (0.0073) (0.0234) (0.0421) 

ds5y_1y4 0.0420 *** 0.0340 *** 0.0406 * 0.0532 

(0.0067) (0.0065) (0.0213) (0.0385) 

Credit Risk 

dln_ast2 0.0036 *** 0.0010 * 0.0047 *** 0.0001 

(0.0005) (0.0005) (0.0013) (0.0017) 

dci_rat2 0.0008 0.0012 -0.0060 * 0.0019 

(0.0009) (0.0011) (0.0031) (0.0047) 

dcsprd1 -0.1501 *** -0.0875 *** -0.0626 0.0064 

(0.017) (0.0164) (0.0535) (0.0976) 

dnperf_rat2 -0.0063 *** -0.0030 -0.0504 *** -0.0022 

(0.0019) (0.0031) (0.0135) (0.0251) 

Other Institutional Variables 

logast1 -0.0898 *** -0.0366 *** 0.0317 * 0.0605 *** 

(0.006) (0.0058) (0.0177) (0.0231) 

nim_rat2 -0.3909 *** -0.2614 *** -0.2970 *** -0.3948 *** 

(0.0027) (0.0027) (0.0099) (0.0139) 

dnim_rat1 -0.6812 *** -0.5238 *** -0.5236 *** -0.6348 *** 

(0.0028) (0.0033) (0.0114) (0.0136) 

dnonii_rat2 -0.0089 *** -0.0050 *** -0.0068 *** -0.0099 ** 

(0.0018) (0.0014) (0.0021) (0.0047) 

dsecgl_rat2 -0.0053 ** -0.0009 0.0161 0.0141 

(0.0026) (0.0035) (0.0125) (0.0223) 

Seasonal Dummy 

qtr2 0.1062 *** 0.1142 *** 0.0860 *** 0.0508 * 

(0.0047) (0.0046) (0.0148) (0.027) 

qtr3 0.1607 *** 0.1307 *** 0.0937 *** 0.1025 *** 

(0.005) (0.0049) (0.0157) (0.028) 

qtr4 0.0884 *** 0.0873 *** 0.0760 *** 0.1085 *** 

(0.0047) (0.0046) (0.0147) (0.0267) 

# of Cross Sections 4869 3406 432 221 

Time Series Length 70 70 70 70 

R-Square 0.35 0.27 0.25 0.37 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level

 *** Significant at the 1% Level) 

64 
 

 

Table 2B: Cross-sectional Time Series Regression with Random Effects (ROA) 1986:Q1 – 2003:Q2 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DROA – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, respectively. 

Hausman test rejected random effects for "international" institutions; therefore, results for international banks represent an estimate using crosssectional time series regression with fixed effects. 

Large NonInternational International Agricultural Credit Card C&I 

Banks* Banks Banks Specialists Specialists CRE Specialists 

Intercept -3.4242 0.0943 -0.1312 4.2925 *** -2.9175 *** -0.0689 

(3.6403) (0.8674) (0.1129) (1.1928) (0.3308) (0.1592) 

Interest Rate Risk 

vol_1y1 -1.2934 ** -0.2030 -0.4314 *** -2.4592 *** -0.9534 *** -0.2697 

(0.5662) (0.2771) (0.0318) (0.9106) (0.18) (0.0784) 

st_dummy1 -0.0188 -0.2201 0.1942 *** 0.0665 -0.2919 ** -0.0430 

(0.2702) (0.176) (0.0308) (0.2215) (0.1271) (0.0517) 

stgap_rat2 0.0087 -0.0021 -0.0058 *** 0.0015 -0.0009 -0.0029 

(0.0072) (0.0032) (0.0003) (0.0032) (0.0019) (0.0008) 

nm_rat2 0.0166 0.0031 0.0066 *** 0.0108 *** 0.0103 *** 0.0050 

(0.011) (0.0035) (0.0007) (0.0032) (0.0022) (0.0009) 

stgap_sd1 0.0079 0.0025 0.0059 *** -0.0008 0.0037 0.0014 

(0.0069) (0.0036) (0.0004) (0.0039) (0.0023) (0.0009) 

nm_sd1 0.0154 * 0.0076 ** 0.0012 * -0.0108 *** 0.0073 *** 0.0031 

(0.0088) (0.0036) (0.0007) (0.004) (0.0024) (0.001) 

Term Structure Risk 

ds5y_1y1 0.7192 *** 0.1137 -0.0361 ** -0.6660 -0.3855 *** -0.1273 

(0.2779) (0.126) (0.0155) (0.4632) (0.0844) (0.035) 

ds5y_1y2 -0.6067 ** -0.2226 * -0.0272 * 0.1554 -0.1463 0.0498 

(0.2948) (0.129) (0.0165) (0.4728) (0.0925) (0.0355) 

ds5y_1y3 0.0866 -0.0747 0.2882 *** 0.7509 0.2678 *** 0.0248 

(0.2912) (0.1273) (0.0165) (0.4752) (0.0929) (0.0355) 

ds5y_1y4 0.4549 * 0.2445 ** 0.0024 0.3258 -0.2823 *** -0.0812 

(0.2617) (0.1235) (0.0148) (0.4337) (0.082) (0.0341) 

Credit Risk 

dln_ast2 -0.0135 0.0083 0.0021 ** 0.0162 0.0137 *** 0.0098 

(0.023) (0.0066) (0.0009) (0.0107) (0.0045) (0.0018) 

dci_rat2 -0.0101 -0.0218 0.0171 *** 0.0269 0.0051 0.0042 

(0.0469) (0.0146) (0.0026) (0.035) (0.0054) (0.0031) 

dcsprd1 1.7532 *** 0.4188 0.0495 1.3212 0.0036 0.2474 

(0.6588) (0.2797) (0.0371) (1.0534) (0.2145) (0.0782) 

dnperf_rat2 0.0032 -0.0788 ** -0.0903 *** 0.0536 -0.3096 *** -0.1859 

(0.1526) (0.0368) (0.004) (0.1122) (0.0162) (0.0077) 

Other Institutional Variables 

logast1 0.2804 0.0266 0.0646 *** -0.1741 * 0.2604 *** 0.0258 

(0.2122) (0.0508) (0.0106) (0.0905) (0.027) (0.0133) 

roa2 -0.9266 *** -0.5438 *** -0.8367 *** -0.5037 *** -0.6476 *** -0.5224 

(0.0583) (0.0288) (0.0032) (0.0175) (0.0084) (0.0053) 

droa1 -0.9803 *** -0.7057 *** -0.9323 *** -0.6227 *** -0.8560 *** -0.7892 

(0.0425) (0.0222) (0.0023) (0.0173) (0.0061) (0.0042) 

Seasonal Dummy 

qtr2 -0.5645 *** -0.0891 0.0260 ** -0.2963 -0.2525 *** -0.0393 

(0.1803) (0.0878) (0.0111) (0.3043) (0.0595) (0.0249) 

qtr3 -0.4450 ** 0.1407 0.0150 0.0116 -0.3114 *** -0.1301 

(0.1919) (0.0928) (0.0115) (0.3192) (0.0651) (0.0263) 

qtr4 -0.0195 -0.1157 -0.5448 *** -0.5725 * -1.0279 *** -0.5085 

(0.1838) (0.0902) (0.0108) (0.2996) (0.0617) (0.0256) 

# of Cross Sections 15 89 4308 145 2378 3564 

Time Series Length 70 70 70 70 70 70 

R-Square 0.52 0.35 0.48 0.35 0.36 0.35 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

65 
 

 

 

Table 2B: Cross-sectional Time Series Regression with Random Effects (ROA) 1986:Q1 – 2003:Q2 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DROA – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, respectively. 

Hausman test rejected random effects for "international" institutions; therefore, results for international banks represent an estimate using crosssectional time series regression with fixed effects. 

Commerical 

Commerical Commerical Loan Loan 

Loan Specialists Specialists $50 - Specialists Mortgage Consumer Other Small 

<=$50 mil. $300 mil. >$300 mil. Specialists Specialists Specialists 

Intercept -4.4615 *** 0.9300 *** 0.4310 -0.2145 * -0.1781 -0.6373 * 

(0.3991) (0.2404) (0.2695) (0.128) (0.2485) (0.3818) 

Interest Rate Risk 

vol_1y1 -0.5512 *** -0.3628 *** -0.1674 * -0.0803 -0.3274 ** -0.2528 * 

(0.1058) (0.0557) (0.0885) (0.0619) (0.131) (0.1483) 

st_dummy1 -0.1910 ** -0.0255 0.0895 0.0453 -0.2696 *** 0.0504 

(0.0851) (0.0512) (0.0836) (0.037) (0.092) (0.0968) 

stgap_rat2 -0.0056 *** -0.0045 *** -0.0048 *** -0.0023 *** -0.0001 0.0008 

(0.0011) (0.0007) (0.0011) (0.0005) (0.0012) (0.0009) 

nm_rat2 0.0081 *** 0.0055 *** 0.0058 *** 0.0084 *** 0.0035 * -0.0013 

(0.0015) (0.0009) (0.0014) (0.0008) (0.0018) (0.0017) 

stgap_sd1 0.0048 *** 0.0022 *** 0.0032 ** 0.0008 0.0002 0.0032 ** 

(0.0013) (0.0008) (0.0013) (0.0006) (0.0014) (0.0013) 

nm_sd1 0.0061 *** 0.0016 * 0.0009 -0.0010 0.0038 ** 0.0002 

(0.0016) (0.0009) (0.0015) (0.0008) (0.0019) (0.0019) 

Term Structure Risk 

ds5y_1y1 -0.3077 *** -0.1314 *** -0.0695 -0.1599 *** -0.2526 *** -0.1478 ** 

(0.0515) (0.0269) (0.0428) (0.031) (0.0668) (0.0729) 

ds5y_1y2 -0.0113 -0.0722 ** -0.0263 0.0678 ** 0.0583 -0.0955 

(0.0543) (0.0283) (0.0451) (0.0312) (0.0695) (0.0779) 

ds5y_1y3 0.3432 *** 0.1622 *** -0.0170 0.0560 * 0.1630 ** 0.0742 

(0.0547) (0.0284) (0.0453) (0.0314) (0.07) (0.0767) 

ds5y_1y4 -0.1289 *** -0.0544 ** -0.0173 0.0033 -0.0109 0.0145 

(0.0491) (0.0256) (0.041) (0.0291) (0.0633) (0.068) 

Credit Risk 

dln_ast2 0.0136 *** 0.0054 *** 0.0082 *** -0.0036 ** 0.0030 -0.0160 *** 

(0.0027) (0.0019) (0.0031) (0.0017) (0.0035) (0.0047) 

dci_rat2 0.0013 0.0078 *** 0.0032 0.0139 ** 0.0077 0.0278 ** 

(0.0041) (0.003) (0.0057) (0.006) (0.0078) (0.0109) 

dcsprd1 -0.0156 0.1284 ** 0.0193 -0.0501 -0.1393 -0.0848 

(0.1237) (0.0635) (0.1007) (0.0662) (0.1523) (0.178) 

dnperf_rat2 -0.1687 *** -0.1966 *** -0.1371 *** -0.1203 *** -0.0860 *** -0.0243 ** 

(0.0101) (0.0084) (0.0155) (0.0096) (0.0181) (0.0121) 

Other Institutional Variables 

logast1 0.4411 *** -0.0502 ** -0.0135 0.0472 *** 0.0758 *** 0.1832 *** 

(0.0385) (0.0204) (0.0184) (0.0108) (0.0215) (0.0352) 

roa2 -0.7317 *** -0.6963 *** -0.6360 *** -0.7080 *** -0.6276 *** -0.8231 *** 

(0.0063) (0.0054) (0.0095) (0.0072) (0.0118) (0.0057) 

droa1 -0.8707 *** -0.8765 *** -0.8041 *** -0.8241 *** -0.8198 *** -0.8517 *** 

(0.0047) (0.0042) (0.0078) (0.0056) (0.0088) (0.0052) 

Seasonal Dummy 

qtr2 -0.0591 * 0.0066 -0.0937 *** -0.0149 0.0509 -0.1753 *** 

(0.0347) (0.0181) (0.0288) (0.0198) (0.0436) (0.049) 

qtr3 -0.1058 *** 0.0254 -0.0715 ** -0.0804 *** -0.0276 -0.1533 *** 

(0.0366) (0.0191) (0.0306) (0.0206) (0.0457) (0.0522) 

qtr4 -0.6353 *** -0.3350 *** -0.2860 *** -0.2630 *** -0.4498 *** -0.4807 *** 

(0.0348) (0.0182) (0.0288) (0.0198) (0.043) (0.0482) 

# of Cross Sections 3220 3300 917 1659 1220 2146 

Time Series Length 70 70 70 70 70 70 

R-Square 0.42 0.44 0.38 0.40 0.32 0.39 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

66 
 

 

 

Table 2B: Cross-sectional Time Series Regression with Random Effects (ROA) 1986:Q1 – 2003:Q2 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DROA – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, respectively. 

Hausman test rejected random effects for "international" institutions; therefore, results for international banks represent an estimate using crosssectional time series regression with fixed effects. 

Small Non- Small Non- Small Non- Mid-Tier 

Specialists Specialists $50 - Specialists > Non-

<=$50 mil $300 mil. $300 mil Specialists 

Intercept -1.8796 *** 1.1603 *** 1.4211 ** 0.7014 

(0.1771) (0.1648) (0.5771) (0.625) 

Interest Rate Risk 

vol_1y1 -0.3427 *** -0.2525 *** -0.3757 *** -0.1157 

(0.0441) (0.0331) (0.116) (0.1735) 

st_dummy1 -0.0357 0.1698 *** 0.2319 ** 0.3914 *** 

(0.037) (0.0264) (0.1061) (0.1082) 

stgap_rat2 -0.0003 ** -0.0029 *** -0.0023 * 0.0000 *** 

(0.0001) (0.0004) (0.0013) (0) 

nm_rat2 0.0052 *** 0.0023 *** -0.0026 0.0031 * 

(0.0007) (0.0006) (0.0019) (0.0018) 

stgap_sd1 0.0007 0.0040 *** 0.0035 ** 0.0081 *** 

(0.0004) (0.0004) (0.0015) (0.0015) 

nm_sd1 0.0008 -0.0002 -0.0018 0.0000 

(0.0007) (0.0006) (0.002) (0.0021) 

Term Structure Risk 

ds5y_1y1 -0.1385 *** -0.0840 *** -0.1036 * -0.0529 

(0.0214) (0.0161) (0.0551) (0.0827) 

ds5y_1y2 -0.0355 -0.0118 0.0130 -0.0752 

(0.0229) (0.0172) (0.0581) (0.0861) 

ds5y_1y3 0.2090 *** 0.1213 *** 0.0535 -0.0387 

(0.0229) (0.0171) (0.0583) (0.0863) 

ds5y_1y4 0.0047 0.0049 -0.0592 0.0149 

(0.0204) (0.0153) (0.0529) (0.0789) 

Credit Risk 

dln_ast2 0.0142 *** 0.0065 *** 0.0113 *** 0.0074 ** 

(0.0014) (0.0012) (0.0032) (0.0034) 

dci_rat2 0.0080 *** 0.0108 *** -0.0004 -0.0030 

(0.0027) (0.0025) (0.0078) (0.0096) 

dcsprd1 -0.0471 0.0294 0.2667 ** 0.7010 *** 

(0.0524) (0.0389) (0.1328) (0.1997) 

dnperf_rat2 -0.0977 *** -0.0396 *** -0.2562 *** -0.1627 *** 

(0.0058) (0.0073) (0.0336) (0.0511) 

Other Institutional Variables 

logast1 0.2419 *** -0.0373 *** -0.0337 -0.0084 

(0.017) (0.0142) (0.0436) (0.0423) 

roa2 -0.7488 *** -0.7377 *** -0.7847 *** -0.6256 *** 

(0.0038) (0.0043) (0.0129) (0.0195) 

droa1 -0.8794 *** -0.8347 *** -0.8781 *** -0.8360 *** 

(0.0028) (0.0035) (0.0096) (0.0153) 

Seasonal Dummy 

qtr2 -0.0092 -0.0161 0.0191 -0.0190 

(0.0146) (0.0109) (0.0367) (0.0552) 

qtr3 -0.0230 -0.0212 * -0.0305 0.1212 * 

(0.0155) (0.0115) (0.0389) (0.0575) 

qtr4 -0.5424 *** -0.2612 *** -0.1007 *** -0.0318 

(0.0145) (0.0108) (0.0366) (0.0547) 

# of Cross Sections 4869 3406 432 221 

Time Series Length 70 70 70 70 

R-Square 0.46 0.43 0.54 0.43 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level

 *** Significant at the 1% Level) 

67 
 

 

 

 

 

Table 3A: Cross-sectional Time Series Regression with Random Effects 

Subsample Periods—Agricultural Banks and Mortgage Loan Specialists 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DNIM_RAT – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, 

respectively. 

Agricultrual Banks Mortgage Loan Specialists 

1997:Q3 - 1997:Q3 -

1986 - 1988 1989-1991 1992-1997:Q2 2003:Q2 1986 - 1988 1989-1991 1992-1997:Q2 2003:Q2 

Intercept 5.6046 *** 3.3325 *** 1.6648 *** 1.6408 *** 4.2783 *** 4.1440 *** 1.6248 *** 2.0441 *** 

(0.1722) (0.1279) (0.074) (0.0744) (1.0755) (0.3375) (0.1104) (0.1323) 

Interest Rate Risk 

vol_1y1 0.3796 *** 0.0025 0.2210 *** -0.2920 *** 1.5830 * -0.4766 ** 0.1585 *** 0.0169 

(0.0923) (0.0586) (0.0162) (0.0247) (0.9202) (0.2203) (0.0372) (0.0459) 

st_dummy1 -0.0176 -0.2054 *** -0.0299 ** 0.0001 0.0327 -0.5403 *** -0.1097 *** -0.0990 *** 

(0.035) (0.0284) (0.0152) (0.0162) (0.1885) (0.0852) (0.0231) (0.0218) 

stgap_rat2 -0.0022 *** -0.0003 -0.0007 *** -0.0003 0.0009 0.0023 ** 0.0008 ** 0.0001 

(0.0005) (0.0003) (0.0002) (0.0002) (0.0035) (0.0011) (0.0004) (0.0004) 

nm_rat2 0.0168 *** 0.0114 *** 0.0077 *** 0.0048 *** 0.0069 0.0048 ** 0.0040 *** 0.0041 *** 

(0.0009) (0.0007) (0.0004) (0.0004) (0.0055) (0.0021) (0.0006) (0.0007) 

stgap_sd1 0.0026 *** 0.0040 *** 0.0027 *** 0.0040 *** -0.0024 0.0019 ** 0.0007 * 0.0001 

(0.0005) (0.0003) (0.0002) (0.0002) (0.0032) (0.001) (0.0004) (0.0004) 

nm_sd1 0.0003 0.0070 *** 0.0021 *** 0.0052 *** -0.0023 0.0080 *** 0.0025 *** 0.0022 *** 

(0.0007) (0.0005) (0.0003) (0.0003) (0.0042) (0.0014) (0.0005) (0.0004) 

Term Structure Risk 

ds5y_1y1 -0.5360 *** 0.1371 *** 0.0303 *** -0.0679 *** -2.0785 ** 0.1285 0.2002 *** -0.0083 

(0.0862) (0.0319) (0.0111) (0.0082) (0.9687) (0.1185) (0.0256) (0.0149) 

ds5y_1y2 0.5067 *** 0.0083 0.1338 *** 0.1520 *** 2.0824 -0.6062 ** 0.1196 *** 0.0866 *** 

(0.1346) (0.0832) (0.0112) (0.0096) (1.3437) (0.2995) (0.0256) (0.0177) 

ds5y_1y3 -0.3716 *** 0.0304 0.0456 *** 0.0902 *** -1.7131 0.6785 *** 0.1087 *** 0.1144 *** 

(0.1055) (0.0532) (0.0088) (0.009) (1.0637) (0.1969) (0.0201) (0.0164) 

ds5y_1y4 0.0349 0.0669 *** 0.0278 *** -0.0657 *** -0.8511 * 0.0260 0.0558 *** 0.0090 

(0.0393) (0.022) (0.0079) (0.0098) (0.4464) (0.092) (0.0182) (0.0186) 

Credit Risk 

dln_ast2 0.0036 *** 0.0004 -0.0028 *** -0.0019 *** -0.0044 0.0073 *** -0.0006 0.0017 * 

(0.0009) (0.0006) (0.0005) (0.0005) (0.0074) (0.0025) (0.001) (0.001) 

dci_rat2 0.0072 *** 0.0042 ** 0.0059 *** 0.0046 *** -0.0089 -0.0066 0.0055 0.0151 *** 

(0.0023) (0.0018) (0.0014) (0.0014) (0.0186) (0.0073) (0.0041) (0.004) 

dcsprd1 -0.4000 *** -0.2984 *** -0.0538 0.0054 -2.2226 0.2572 ** -0.4492 *** -0.0004 

(0.148) (0.0353) (0.0419) (0.0182) (1.5587) (0.1265) (0.0971) (0.034) 

dnperf_rat2 -0.0124 *** 0.0090 *** 0.0162 *** 0.0089 *** -0.0244 0.0050 0.0016 0.0303 *** 

(0.0028) (0.003) (0.0025) (0.0026) (0.0308) (0.0077) (0.0068) (0.0087) 

Other Institutional Variables 

logast1 -0.2963 *** -0.1134 *** -0.0345 *** -0.0330 *** -0.2474 *** -0.1828 *** -0.0416 *** -0.0607 *** 

(0.0159) (0.0116) (0.0067) (0.0067) (0.0905) (0.027) (0.0087) (0.0107) 

nim_rat2 -0.8457 *** -0.6475 *** -0.4074 *** -0.3687 *** -0.4934 *** -0.5642 *** -0.3175 *** -0.4074 *** 

(0.0064) (0.0064) (0.004) (0.0046) (0.0453) (0.018) (0.0077) (0.0083) 

dnim_rat1 -0.9446 *** -0.8166 *** -0.6084 *** -0.5827 *** -0.5177 *** -0.7081 *** -0.5596 *** -0.5076 *** 

(0.0047) (0.0051) (0.0039) (0.0043) (0.0401) (0.0154) (0.0085) (0.0089) 

dnonii_rat2 0.0035 0.0063 * 0.0037 -0.0019 0.0200 0.0330 ** 0.0001 0.0062 ** 

(0.0041) (0.0038) (0.0035) (0.0037) (0.0383) (0.0145) (0.0032) (0.0027) 

dsecgl_rat2 -0.0080 ** 0.0055 -0.0119 *** 0.0058 -0.0019 0.0276 * 0.0073 0.0326 *** 

(0.0041) (0.0061) (0.0034) (0.009) (0.0498) (0.0166) (0.0067) (0.0077) 

Seasonal Dummy 

qtr2 -0.1173 *** 0.0043 0.1150 *** 0.1225 *** 0.1411 0.0155 -0.0075 0.0527 *** 

(0.0128) (0.012) (0.006) (0.0065) (0.1686) (0.0466) (0.013) (0.0116) 

qtr3 0.2656 *** 0.1146 *** 0.1980 *** 0.0961 *** 0.9627 * 0.4006 *** 0.0221 * 0.0408 *** 

(0.0488) (0.0306) (0.0054) (0.0077) (0.5071) (0.1102) (0.0118) (0.0143) 

qtr4 -0.2299 *** 0.1055 *** 0.1146 *** -0.0182 ** -0.9948 0.1519 *** 0.0372 *** 0.0059 

(0.0711) (0.0086) (0.0052) (0.0076) (0.725) (0.0364) (0.0115) (0.014) 

# of Cross 

Sections 3629 3274 3221 2447 83 482 1105 856 

Time Series 

Length 12 12 22 24 12 12 22 24 

R-Square 0.51 0.43 0.33 0.31 0.26 0.35 0.25 0.25 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

68 
 

 

 

 

 

 

Table 3B: Cross-sectional Time Series Regression with Random Effects 

Sub-sample Periods – Commercial Loan Specialists 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DNIM_RAT – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, 

respectively. 

Commercial Loan Specialists w/ Real Assets <$50 million Commercial Loan Specialists w/ Real Assets >=$300 million 

1997:Q3 - 1997:Q3 -

1986 - 1988 1989-1991 1992-1997:Q2 2003:Q2 1986 - 1988 1989-1991 1992-1997:Q2 2003:Q2 

Intercept 2.5168 *** 2.4953 *** 1.6914 *** 1.7292 *** 0.9702 *** 0.6949 *** 2.0593 *** 1.8136 *** 

(0.3113) (0.2849) (0.1928) (0.2492) (0.2507) (0.2211) (0.215) (0.2036) 

Interest Rate Risk 

vol_1y1 0.2044 -0.3966 *** 0.3127 *** -0.4435 *** -0.1775 -0.5719 *** 0.0785 -0.2818 *** 

(0.1542) (0.1246) (0.0396) (0.0754) (0.1394) (0.1092) (0.0573) (0.071) 

st_dummy1 -0.1881 *** -0.1645 *** -0.0309 0.0028 -0.0539 -0.3083 *** -0.0059 0.0529 

(0.0531) (0.0563) (0.0315) (0.043) (0.0535) (0.0551) (0.056) (0.05) 

stgap_rat2 -0.0005 -0.0016 ** -0.0008 * -0.0028 *** 0.0020 ** -0.0029 *** -0.0023 ** -0.0036 *** 

(0.0008) (0.0007) (0.0005) (0.0007) (0.001) (0.0008) (0.0009) (0.0008) 

nm_rat2 0.0120 *** 0.0087 *** 0.0073 *** 0.0056 *** 0.0061 *** 0.0009 0.0076 *** 0.0029 *** 

(0.001) (0.001) (0.0006) (0.0009) (0.0013) (0.0012) (0.0011) (0.0011) 

stgap_sd1 0.0034 *** 0.0060 *** 0.0032 *** 0.0044 *** 0.0016 0.0044 *** 0.0031 *** 0.0053 *** 

(0.0009) (0.0007) (0.0005) (0.0007) (0.0011) (0.0008) (0.001) (0.0007) 

nm_sd1 0.0032 *** 0.0087 *** 0.0030 *** 0.0054 *** 0.0006 0.0064 *** 0.0021 ** 0.0049 *** 

(0.0009) (0.0008) (0.0006) (0.0007) (0.001) (0.0009) (0.001) (0.0008) 

Term Structure Risk 

ds5y_1y1 -0.4897 *** -0.1557 ** 0.0428 -0.0828 *** 0.0655 -0.1123 * 0.0544 -0.0127 

(0.1545) (0.0673) (0.0267) (0.0243) (0.1396) (0.06) (0.039) (0.0229) 

ds5y_1y2 0.6729 *** 0.1873 0.1462 *** 0.1456 *** -0.0404 -0.5589 *** 0.0370 0.1264 *** 

(0.229) (0.1803) (0.0267) (0.0287) (0.2086) (0.154) (0.0388) (0.027) 

ds5y_1y3 -0.2639 0.1309 -0.0032 0.0807 *** 0.2582 0.5416 *** 0.0382 0.0510 ** 

(0.1761) (0.1158) (0.0211) (0.0268) (0.1605) (0.1004) (0.0308) (0.0252) 

ds5y_1y4 0.0036 -0.2109 *** 0.0340 * -0.1298 *** 0.1011 0.0248 0.0716 *** -0.1773 *** 

(0.0698) (0.0483) (0.019) (0.0312) (0.063) (0.0429) (0.0277) (0.0296) 

Credit Risk 

dln_ast2 0.0004 0.0001 0.0001 -0.0016 -0.0003 0.0027 * 0.0109 *** -0.0027 

(0.0014) (0.0013) (0.001) (0.0014) (0.0017) (0.0016) (0.002) (0.0018) 

dci_rat2 0.0000 -0.0005 -0.0022 0.0061 ** -0.0083 *** -0.0035 -0.0085 ** -0.0021 

(0.0019) (0.0018) (0.0017) (0.0025) (0.0031) (0.0027) (0.0041) (0.0038) 

dcsprd1 -0.3179 -0.2093 *** 0.2640 *** 0.1263 ** 0.3038 0.1655 ** -0.2002 0.2220 *** 

(0.2538) (0.0782) (0.1008) (0.0551) (0.2311) (0.0703) (0.147) (0.052) 

dnperf_rat2 -0.0091 ** -0.0028 0.0054 -0.0020 -0.0109 -0.0148 ** 0.0008 -0.0361 

(0.0043) (0.0041) (0.0047) (0.0085) (0.0078) (0.0075) (0.0126) (0.0223) 

Other Institutional Variables 

logast1 -0.0827 *** -0.0812 *** -0.0471 *** -0.0579 ** 0.0051 0.0239 * -0.0399 *** -0.0372 *** 

(0.0289) (0.0265) (0.018) (0.0239) (0.0161) (0.0144) (0.0141) (0.0134) 

nim_rat2 -0.5094 *** -0.4534 *** -0.3517 *** -0.3290 *** -0.3222 *** -0.2477 *** -0.4476 *** -0.3947 *** 

(0.0117) (0.0111) (0.0076) (0.0091) (0.0156) (0.015) (0.0141) (0.0123) 

dnim_rat1 -0.7058 *** -0.6822 *** -0.5351 *** -0.5084 *** -0.4742 *** -0.4814 *** -0.6647 *** -0.5258 *** 

(0.01) (0.0103) (0.0084) (0.0096) (0.0188) (0.0169) (0.0136) (0.0119) 

dnonii_rat2 0.0089 *** -0.0011 -0.0105 ** -0.0085 0.0115 * 0.0132 * -0.0351 *** -0.0209 ** 

(0.003) (0.0047) (0.0044) (0.0076) (0.0063) (0.007) (0.0102) (0.0089) 

dsecgl_rat2 0.0138 -0.0063 -0.0008 -0.0098 0.0153 -0.0126 -0.0041 -0.0223 

(0.0096) (0.0154) (0.009) (0.0246) (0.0144) (0.0181) (0.0137) (0.0169) 

Seasonal Dummy 

qtr2 0.0753 *** 0.1736 *** 0.2012 *** 0.1598 *** 0.1399 *** 0.0311 0.0692 *** 0.0933 *** 

(0.023) (0.0262) (0.0139) (0.0188) (0.0202) (0.0235) (0.0206) (0.0177) 

qtr3 0.4786 *** 0.1194 * 0.1958 *** 0.0759 *** 0.2216 *** 0.2836 *** 0.1246 *** 0.0429 * 

(0.0863) (0.0666) (0.0124) (0.0233) (0.0777) (0.0579) (0.0182) (0.0221) 

qtr4 -0.0611 0.0768 *** 0.1592 *** -0.0441 * 0.2836 *** 0.0521 *** 0.0843 *** 0.0133 

(0.1176) (0.0189) (0.0122) (0.023) (0.1073) (0.0169) (0.0178) (0.0218) 

# of Cross 

Sections 1212 1045 1280 1055 351 388 406 358 

Time Series 

Length 12 12 22 24 12 12 22 24 

R-Square 0.36 0.37 0.27 0.22 0.26 0.22 0.34 0.35 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

69 
 

 

 

 

 

Table 3C: Cross-sectional Time Series Regression with Random Effects 

Sub-sample Periods – Other Non-Specialist Banks 

Independent variables are presented by types of risk they represent – interest rate risk, term structure risk and credit risk as well as institutional 

variables and seasonal dummy. Appendix 2 contains descriptions of all independent variables included in this table. All institutional specific 

variables, other than the log of total real assets (LOGAST1) are divided by average earning assets between two quarters. All variables beginning with 

“d” – for example DNIM_RAT – represent quarterly changes. Suffixes “1” and “2” represent one-quarter (t-1) and two quarter (t-2) lags, 

respectively. 

Other Non-Specialist Banks w/ Real Assets <$50 million Other Non-Specialist Banks w/ Real Assets >= $300 million 

1997:Q3 - 1997:Q3 -

1986 - 1988 1989-1991 1992-1997:Q2 2003:Q2 1986 - 1988 1989-1991 1992-1997:Q2 2003:Q2 

Intercept 4.3752 *** 3.0332 *** 4.3591 *** 1.3266 *** 1.7709 * 0.2221 0.7483 1.4564 *** 

(0.1958) (0.1612) (0.171) (0.1386) (1.0168) (0.7037) (0.5234) (0.3799) 

Interest Rate Risk 

vol_1y1 0.4619 *** -0.0902 0.2157 *** -0.3670 *** -0.3606 0.0364 0.2429 ** 0.0013 

(0.089) (0.0658) (0.0245) (0.045) (0.2583) (0.2082) (0.1041) (0.0819) 

st_dummy1 -0.1028 *** -0.0572 * -0.1100 *** 0.0673 ** 0.0221 -0.1016 0.1294 -0.0044 

(0.0294) (0.0295) (0.021) (0.0277) (0.1126) (0.1144) (0.0887) (0.0521) 

stgap_rat2 -0.0030 *** -0.0014 *** -0.0025 *** 0.0001 -0.0053 *** -0.0019 0.0000 -0.0014 ** 

(0.0004) (0.0004) (0.0003) (0.0000) (0.0017) (0.0016) (0.0015) (0.0007) 

nm_rat2 0.0085 *** 0.0088 *** 0.0102 *** 0.0042 *** 0.0082 *** 0.0001 0.0101 *** -0.0012 

(0.0007) (0.0006) (0.0005) (0.0006) (0.0031) (0.0024) (0.0017) (0.0012) 

stgap_sd1 0.0051 *** 0.0035 *** 0.0022 *** 0.0033 *** 0.0068 *** 0.0044 *** 0.0024 0.0031 *** 

(0.0005) (0.0004) (0.0003) (0.0004) (0.0017) (0.0015) (0.0017) (0.0007) 

nm_sd1 0.0032 *** 0.0048 *** 0.0033 *** 0.0039 *** 0.0042 ** 0.0049 ** -0.0010 0.0028 *** 

(0.0006) (0.0005) (0.0004) (0.0005) (0.002) (0.0019) (0.0016) (0.001) 

Term Structure Risk 

ds5y_1y1 -0.6135 *** -0.0331 0.0253 -0.0356 ** 0.0376 -0.0219 -0.0143 0.0366 

(0.0861) (0.0368) (0.0166) (0.0149) (0.2447) (0.1126) (0.0674) (0.0272) 

ds5y_1y2 0.7619 *** 0.2410 *** 0.1610 *** 0.1426 *** -0.3118 0.1276 0.1965 *** 0.0471 

(0.1317) (0.0922) (0.0168) (0.0178) (0.3845) (0.2882) (0.0683) (0.032) 

ds5y_1y3 -0.4234 *** -0.0182 0.1045 *** 0.0786 *** 0.4646 0.1810 -0.0896 * 0.1126 *** 

(0.1028) (0.0593) (0.0131) (0.0168) (0.3049) (0.1861) (0.0537) (0.0301) 

ds5y_1y4 -0.0797 ** -0.0575 ** 0.0657 *** -0.0336 * -0.0027 -0.1303 0.1405 *** -0.0413 

(0.0387) (0.0255) (0.0118) (0.0186) (0.1098) (0.0809) (0.0482) (0.0344) 

Credit Risk 

dln_ast2 0.0023 ** 0.0024 *** 0.0051 *** 0.0011 -0.0045 0.0024 -0.0006 0.0001 

(0.001) (0.0009) (0.0008) (0.001) (0.0033) (0.0026) (0.0027) (0.0018) 

dci_rat2 0.0010 -0.0001 0.0029 * 0.0000 -0.0060 0.0000 -0.0021 0.0032 

(0.0016) (0.0014) (0.0017) (0.0026) (0.0057) (0.0059) (0.0078) (0.005) 

dcsprd1 -0.5320 *** -0.3024 *** -0.0373 0.1169 *** 0.1100 -0.0700 0.3555 -0.0051 

(0.1449) (0.0408) (0.0623) (0.0337) (0.4191) (0.1253) (0.2564) (0.0606) 

dnperf_rat2 -0.0117 *** 0.0023 0.0024 0.0231 *** -0.0498 ** -0.0430 ** -0.0433 -0.0636 ** 

(0.0033) (0.0033) (0.0037) (0.0058) (0.022) (0.0205) (0.0323) (0.032) 

Other Institutional Variables 

logast1 -0.2094 *** -0.1247 *** -0.1702 *** -0.0334 *** 0.0399 0.0986 * 0.0267 -0.0600 ** 

(0.0182) (0.0149) (0.016) (0.0128) (0.0761) (0.0516) (0.0386) (0.029) 

nim_rat2 -0.6498 *** -0.5128 *** -0.7056 *** -0.2801 *** -0.7166 *** -0.3845 *** -0.3860 *** -0.1911 *** 

(0.007) (0.0073) (0.0052) (0.0065) (0.0246) (0.0305) (0.0242) (0.0138) 

dnim_rat1 -0.8194 *** -0.7070 *** -0.8953 *** -0.5716 *** -0.8460 *** -0.5474 *** -0.5428 *** -0.3022 *** 

(0.0055) (0.0063) (0.0048) (0.0081) (0.0174) (0.0307) (0.0264) (0.0217) 

dnonii_rat2 0.0011 0.0179 *** -0.0377 *** -0.0155 *** -0.0491 ** -0.0057 -0.0959 *** -0.0039 *** 

(0.0031) (0.0041) (0.0034) (0.0054) (0.0198) (0.016) (0.025) (0.0015) 

dsecgl_rat2 -0.0082 * -0.0028 -0.0063 0.0140 0.0258 -0.0354 * 0.0168 0.0271 

(0.0045) (0.0035) (0.0056) (0.0144) (0.0164) (0.0208) (0.0336) (0.0231) 

Seasonal Dummy 

qtr2 0.0098 0.1242 *** 0.1111 *** 0.1004 *** 0.0795 ** 0.0425 0.1065 *** 0.1043 *** 

(0.0119) (0.0139) (0.0087) (0.0115) (0.034) (0.0433) (0.0358) (0.0201) 

qtr3 0.4492 *** 0.0866 ** 0.1544 *** 0.0739 *** 0.1176 0.0201 0.0962 *** 0.0772 *** 

(0.0482) (0.0342) (0.0079) (0.0141) (0.1365) (0.106) (0.0319) (0.0247) 

qtr4 -0.2230 *** 0.1050 *** 0.1081 *** -0.0132 0.4171 ** -0.0070 0.0974 *** 0.0783 *** 

(0.0688) (0.01) (0.0077) (0.0139) (0.2046) (0.032) (0.0314) (0.0245) 

# of Cross 

Sections 2883 2596 2518 1623 136 131 211 149 

Time Series 

Length 12 12 22 24 12 12 22 24 

R-Square 0.45 0.36 0.56 0.21 0.35 0.32 0.29 0.39 

Note: Standard error in parenthesis (* Significant at the 10% level ** Significant at the 5% Level *** Significant at the 1% Level) 

70 
 

Figure 1: Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average 

Earning Assets (Annualized Percentage Points) 

Panel 1: International Banks 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

Noninterest 

Income 

Net Interest 

Income 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Panel 2: Large Non-International Banks 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

Net Interest 

Income 

Noninterest 

Income 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Panel 3: Agricultural Banks 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

Net Interest 

Income 

Noninterest 

Income 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

71 
 

Chart 1: Median Quarterly Net Interest Income and Non-interest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

Panel 4: Credit Card Specialists 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

20.0 

18.0 

16.0 

14.0 

12.0 

10.0 

8.0 

6.0 

4.0 

2.0 

0.0 

Noninterest 

Income 

Net Interest 

Income 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Panel 5: C&I and CRE Specialists 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

0.0 

1.0 

2.0 

3.0 

4.0 

5.0 

6.0 

NIM -- C&I Specialists 

NIM -- CRE Specialists 

NONII -- C&I Specialists 

NONII -- CRE Specialists 

Noninterest 

Income (NONII) 

Net Interest 

Income (NIM) 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Panel 6: Commercial Loan Specialists 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

6.0 

5.0 

4.0 

3.0 

2.0 

1.0 

0.0 

Noninterest 

Income (NONII) 

Net Interest 

Income (NIM) 

NIM -- <=$50 M 

NIM -- $50 - $300 M 

NIM -- >$300 M 

NONII -- <$50 M 

NONII -- $50 - $300 M 

NONII -- >$300 M 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

72 
 

 

Chart 1: Median Quarterly Net Interest Income and Non-interest Income as a Percentage of Average 

Earning Assets (Annualized Percentage Points) 

Panel 7: Mortgage and Other Consumer Loan Specialists 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

6.0 

5.0 

4.0 

3.0 

2.0 

1.0 

0.0 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Noninterest 

Income (NONII) 

Net Interest 

Income (NIM) 

NIM -- Mortgage 

NIM -- Other Consumer 

NONII -- Mortgage 

NONII -- Other Consumer 

Panel 8: Other Small Specialists 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Net Interest 

Income 

Noninterest 

Income 

Panel 9: Small and Mid-Tier Non-Specialist Banks 

Median Quarterly Net Interest Income and Noninterest Income as a Percentage of Average Earning 

Assets (Annualized Percentage Points) 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

0.0 

1.0 

2.0 

3.0 

4.0 

5.0 

6.0 

NIM -- <=$50M NIM -- $50 - $300M 

NIM -- >$300M NIM -- $1 - $10B 

NONII -- <=$50M NONII -- $50 - $300M 

NONII -- >$300M NONII -- $1 - $10B Noninterest 

Income (NONII) 

Net Interest 

Income (NIM) 

73 
 

Figure 2: Selected Median Quarterly Return on Average Earning Assets 

(Annualized Percentage Points) 

Large Banks 

Median Quarterly Return on Average Earning Assets (Annualized Percentage Points) 

3.0 

2.0 

1.0 

0.0 

-1.0 

-2.0 

-3.0 

-4.0 

-5.0 

-6.0 

Large Non- Mid-Tier 

Non-Specialists International Banks (Right Scale) 

International 

Banks 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Small Non-Specialist Banks 

Median Quarterly Return on Average Earning Assets (Annualized Percentage Points) 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

Real Assets 

$50 - $300 Million 

Real Assets 

> $300 Million 

Real Assets 

<=$50 Million 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

74 
 

 

-1.2 

-0.9 

-0.6 

-0.3 

0 

0.3 

0.6 

0.9 

1.2 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

-0.4 

-0.3 

-0.2 

-0.1 

0 

0.1 

0.2 

0.3 

0.4 

Figure 3: Trend in Changes in Net Interest Margins and Yield Spreads (1986:Q2–2003:Q2) 

Quarterly Changes in the Spread between Mean Quarterly Changes in Net Interest Margins 

5-Year and 1-Year Treasury Yields (%) for Mortgage Specialists (Annualized, %) 

Figure 4: Trends in the One-Year Treasury Yield and Its Volatility (1986:Q2–2003:Q2) 

Quarterly Volatility of the One-Year Treasury Yield Average One-Year Treasury Yield for the Quarter (% 

0.8 

Volatility 

One-Year 

Treasury Yield 

16 

0.7 14 

0.6 12 

0.5 10 

0.4 8 

0.3 6 

0.2 4 

0.1 2 

0 0 

1986 1988 1990 1992 1994 1996 1998 2000 2002 

Source: Haver Analytics and the Federal Reserve Board 

75 
 

 

Figure 5: Trend in Credit Spreads: 1986:Q2–2003:Q2 

5A: Loans Over $1 Million 

Quarterly Changes in Yield Spreads Between Aaa and Baa Corporate Bonds and C&I Loan Rate Spread 

Over Intended Fed Funds Rate (4-Quarter Moving Average -- Percentage Points) 

0.15 

0.10 

0.05 

0.00 

-0.05 

-0.10 

-0.15 

C&I Loan Rate 

Spread 

Yield Spread 

Between Aaa 

and Baa 

Corp. Bonds 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

5B: Loans Less Than $1 Million 

Quarterly Changes in Yield Spreads Between Aaa and Baa Corporate Bonds and C&I Loan Rate Spread 

Over Intended Fed Funds Rate (4-Quarter Moving Average -- Percentage Points) 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

-0.05 

-0.10 

-0.15 

-0.20 

C&I Loan Rate 

C&I Loan 

Spread ($100

 Rate 

K - $1M) 

Spread (<$100K) 

Yield Spread 

Between Aaa 

and Baa 

Corp. Bonds 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

76