metadata: authors: 0: author: Ozler, Sule
1: author: Huizinga, Harry
count: World
docna: 0: docna: How factors in creditor countries affect
            secondary market prices for developing country debt
docty: Policy Research Working Paper
subtopic: Banks & Banking Reform,International Trade and Trade Rules,Food Security,Inflation,Deposit Insurance
teratopic: Environment,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: Bank loans to many developing countries
            trade at a discount on the secondary market. These discounts
            are typically assumed to reflect only the repayment
            prospects of the borrower country. But the authors
            demonstrate that factors in the creditor countries have a
            major impact on secondary market prices. Their empirical
            investigation suggests a systematic relationship between
            secondary market prices and the size distribution of
            banks' portfolios. There is a strong negative
            correlation between discounts in the secondary market and
            U.S. banks' heavy exposure to developing country debt.
            It is estimated that every US$4 billion increase in a large
            bank's exposure to a country reduces the discount 10 to
            15 cents on the dollar. The authors find that discounts and
            total bank capital are positively correlated over time : a
            US$8 billion increase in the capital of the largest U.S.
            banks increases discounts by nearly 25 cents on the dollar.
            They explain their results with a simulation model of a
            representative bank with minimum capital requirements,
            flat-rate deposit insurance, and limited liability. The
            bank's portfolio adjustment decision involves trading
            risky foreign loans in the secondary market or making
            short-term domestic loans. The model yields a negative
            relationship between the banks' exposure to developing
            countries and discounts in the secondary market.
repnme: repnme: How factors in creditor countries affect
            secondary market prices for developing country debt
display_title: How factors in creditor countries
            affect secondary market prices for developing country debt
listing_relative_url: /research/1991/03/700033/factors-creditor-countries-affect-secondary-market-prices-developing-country-debt
topicv3: Commercial Banking m1326069 1236,Agriculture and Food Security m1326205 435,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Deposit Insurance m1326722 322,Trade m1326924 698
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Macroeconomics and Economic Growth,Finance and Financial Sector Development
origu: Policy, Research & Ect Affairs
no_of_pages: 45
model_extractions: **Policy, Research, and External Affairs**

a *

# **Debt and International Finance**

# WPS 622

This paper -- a product of the Debt and Intemational Finance D:vision, International Economics Department -is part of a larger effort in PRE to understand the secondary market lor developing country debtaspairtol'itsanatlysisofvoluntarymarket-baseddebtanddebt.serviccreduction. Copiesare available free from the World Bank, 1818 **H** Street NW, Washington DC 20433. Please contact Sheilah King- **Watson, room S8-040, extension 33730 (40 pages).** the largest U.S. banks increases discounts by nearly 25 cents on the dollar.

Bank **loans** to many developing countries trade the largest U.S. banks increases discounts by at a discount on the secondary market. These nearly 25 cents on the dollar. discounts are typically assumed to reflect only the repayment prospects of the borrower country. They explain their results with a simulation model of a representative bank with minimum But Ozler and Huizinga demonstrate that capital requirements, flat-rate deposit insurance, factors in the creditor countries have a major and limited liability. The bank's portfolio impact on secondary market prices. Th ., adjustment decision involves trading risky empirical investigation suggests a systematic foreign loans in **tihe** secondary market or making relationship between secondary market prices shon-term domestic loans. T'he model yields a and the size distribution of banks' portfolios. negative relationship between the banks' exposure to developing countries and discounts in the There is a strong negative correlation secondary market.

There is a strong negative correlation between discounts in the secondary market and U.S. banks' heavy exposure to developing country debt. They estimate that every US$4 billion increase in a large bank's exposure to a country reduces the discount 10 to 15 cents on the dollar.

This is because flat-rate deposit insuraince, combined with limited liability, encourages banks to gamble with depositors' money **and** to choose a more heavily concentrated developing country loan ponfolio. Similarly, poorly capitalized banks with deposit insurance benefit more than well-capitalized banks do fromii a risky developing country loan ponfblio.

'Fhey also find that discounts and total bank capital are strongly positively correlated over time: a US$8 billion increase in the capital of Thc PRE Working Paper Scrics disscminates thc findings of work under way in **tiC** Bank's Policy, Reseairch. and External Affairs Complex. An objectivc ofthescries is to get thcsc findings out quickly, cven ifpresentations ar less Ithan fully 1 )1 ishld The findings. interpretahions, andl conclusionc in these paperc do not necessarily reprent official Rank **polio-**

**Pioduced** hs the l'RE Disweniinaion C'enicr
How Factors in Creditor Countries<sup>Affect Secondary Market Fr:ces</sup> for Developing Country Debt by Sule Ozler (Department of Economics,University of California, Los Angeles and John F. Kennedy School of Government,<sup>Harvard University</sup> Cambridge, MA 02138) and Harry Huizingd* (Department of Economics, Stanford<sup>University</sup> Stanford, CA 94305)

# Table of Contents

1. Introduction 1 2. Does Bank Exposure Affect Secondary<sup>Market Prices?</sup> 4 2.1 A Cursory Inspection of t:ie Data 4 2.2 Empirical Specification 7 9

- 2.3 Estimation Results

3. How Does Deposit Insurance Affect Secondary<sup>Market Prices?13</sup> 3.1 A Model 13 3.2 Simulation Method 17 3.3 Simulation Results 18 4. Are the Results a Consequence of<sup>Implicit Insuraice?</sup> 19 5. Concluding Remarks 23 References 25 Endnotes 27 Tables 30

* We thank Alberto Alesina, Raquel Fernandez, Stijn Claessens, Daniel Cohen, Robert Kahn, Graciella<sup>Kaminsky, Maurice Obstfeld, George</sup> Pennacchi, Guido Tabellini, and especially Kenneth Froot for helpful discussions, and Robert L. Whetten for valuable advice on the Monte Carlo simulation algorithm. We are particularly grateful to Kenneth Rogoff for many insightful comments. We have also benefitled from the comments of seminar participants at U.C. Berkeley, Boston, Harvard,<sup>and</sup> Yale Universities, and of participants at NBER Summer Institute. Part of this research was done when<sup>Sule Ozler was a visitor at the National</sup> Bureau of Economic Research. Finally, the authors gratefully acknowledge the support provided by the Debt<sup>and International</sup> Finance Division of the World Bank.
# Introduction

In the secondarv mark.) for developing countrv debt, bank loans to manv countries trade_,at a discount. The discounts are tvpically assumed to reflect onlv the repavmernt prospects of the **borrower countries. In fact,** secondary market prices are taken as a guide in discussions of **the possible** debt **relief and valuation problems associated with developing country debt** (for example see Borensztein and Pennacchi (1989)). **Actual debt-reduction** schemes such as the offer of the Philippines to **repurchase between $1.2** billion and $1.4 billion of its own debt, **have also been guided by secondary** market prices (VSJ, September 18, 1989). To the extent **that secondary** market prices are svstematically affected by factors other than **countries'** repayment prospects, however, debt relief or buyback decisions **bas6d on** just the secondarv market prices will be contaminated and misguided.

In this paper we demonstrate that creditor country **factors have a major** impact on secondary market prices. Our empirical investigation suggests a systematic relationship between secondary market prices and the **size** distribution of banks' portfolios. Large U.S. banks' exposures to developing countries and discounts in the secondary market are found **to be** strongly negatively correlated, even when borrower characteristics and the size of the market are controlled for. The magnitude of this effect **is** substantial: a four billion U'.S.$ increase in the exposure of the **large** banks to a country is estimated to reduce the discount 10-15 cents on the dollar. The empirical investigation also suggests that discounts **and bank** capital aggregates are strongly positively related over time: an **eight** billion U.S. $ increase in the capital of largest U.S. banks is found to increase di;counts bv near 25 cents on the dollar.

An explanation for the empirical results is provided with the aid of a
2 simulatiori model. The model is of a representative bank that **faces** minimum capital requirement, flat rate dcposit insurance, and lirited liability. The bank's port4olio adjustment decision involves trading risky foreign loans in tte secondarv market and making short **term domestic loans. The** model vields a negative relationship between banks' **exposure to developing** countries and the discounts in **the secondary market. This is because flat** rate deposit ins.rance, combined with limited liability, **encourages banks to** gamble with depositors' money, and to **choose a more heavily concentrated** developing country loan portfolio. Similarly, poorly **capitalized banks that** enjoy deposit insurance benefit more from **a risky developing country loan** portfolio than well capitalized banks.

The model is simulated by holding default probabilities **of countries** equal so as to focus on creditor country factors that **affect secondary** market prices. Simulations permit us to calculate, for **example, that an** increase in the coverage of deposit insurance from 0% to 75%.results in higher discounts by 5 cents on the dollar for large countries **(based on** 1985 parameter values and the assumption of .5 default probability **for all** developing countries). A thirty percent increase of bank **capital leads to a** 12 cent increase of the discount for the large countries. In **addition, the** simulations suggest near 3.5 billion expected loss for the **federal deposit** insurance agency. Finallv, the magnitude of the impact of **exposure** distribution on secondary market prices are found to **be consistent with our** empirical estimations.

A competing explanation for the observed negative **relationship between** bank exposure and secondarv market discounts stems from the **behavior of** the multilateral lending agencies and the large creditor country governments. It mav be argued that these institutions provide implicit subsidy **to the**
3 banks in order to avoid financial instability. Loans from these institutions to large borrowers would presumably enhance repayment prospects of these borrowers, which could explain why their debt trades at lower discounts. While we can not rule out that differential treatment by multilateral lending agencies affects secondary market prices, we _show_ empirically that the presence of deposit insurance agency renders the relationship between stock market value and bank exposure convex. This result lends additional credence for our explanation of cross country differences in secondary market prices.

Overall, we demonstrate that creditor country factors such as the compositions of banks' developing country loan portfolios, and the regulatory environment are significant determinants of secondary market prices. These findings have two important policy implications. First, our findings undermine the argument for using secondary market prices as guidelines for possible debt relief to developing countries, as it is arbitrary to render debt forgiveness dependent on the particulars of creditor country financial institutions and bank regulation. The second policy implication of our findings concerns debt buybacks. In several recent papers Bulow and Rogoff (1988 a,b) have argued that debt buybacks may be harmful to the debtor country because the country ends up paying the average market price to retire marginal debt. The presence of deposit insurance strengthens the argument that the country (in particular a large exposure country) pays too much, since the market price now reflects the average payment per dollar of debt from the country, as well as an expected payment from the deposit insurance agency.

Finally, the finding that countries that buy back debt have to pay a premium over expected future repayment is consistent with Krugman (1990) who
4 argues that depos insurance biases the banks against providing debt relief. Both straight debt relief, which affects a country's adjustment effort, and deit.buybacks tend to lower the value of the banks' contingent claim on the deposit insurance agency. Our empirical **evidence suggests** that heavily indebted countries will experience **irore** difficulty obtaining debt relief or repurchasing their debt than lightly **indebted countries and** that heavily exposed banks will **be more biased against debt releif and** repurchases than lightly exposed banks.

The remainder of this paper is organized as follows. Section 2 contains the empirical evidence. We present the **model in Section 3, and** also discuss the simulation results, and their implications for bank regulatory practice. Section 4 presents evidence on banks' stock market valuation. Section 5 contains a summary and conclusions.

# 2. **Does Bank Exposure Affect Secondary Market Prices** ?

## 2.1 A Cursory Inspection of the Data

While the debt obligations of a number of d:eveloping countries are being traded at large discounts, there are several developing countries with debt that appears to be at par. In this section we will first present some distinguishing characteristics of the two groups of countries. Second, discount and bank exposure data are presented for the countries for which debt is traded at a discount.

A cursory look at the data suggests that the debt of **countries with bad** repayment prospects is more likely to be traded at a discount in the secondary market. Repsyment prospects of countries have typically been measured by their general economic chararacteristics. Economic characteristics of borrowers that are identified as important measures **of**
5 risk have been employed in a wide range cf empirical studies concerning developing country debt: predicting thb occurence of repayments difficulties, as well as investigating credit terms (for rev jws see McDonough (1982), Eaton and Taylor (1986); recent studies include Ozler (1990 a b)), and in the analysis of secondary market prices (Berg and Sachs (1989), Cohen (1988), Hajivassiliou (1988), Huizinga (1989), Sachs and Huizinga (1987), and Purcell and Orlanski (1988)). We employ variables similar to the ones employed in the previous studies and **these variables** are: debt-to-exports ratio, reserves-to-imports ratio, real GNP per capita, lagged value of investment-to-GNP-ratio, rate of inflation, debt-to-GN? ratio, imports-to GNP-ratio, and reserves-to-GNP ratio ratio. In addition to these standard indicators, some structural variables have also been considered. These include the share of agriculture in GDP and a measure of income distribution. A lower agricultural base and unequal income distribution are both argued to contribute to political instability and therefore a less favorable environment for timely repayment.

In Table I the means and standard errors of the repayment -ndicators for the countries in our sample are presented. Our data contains information on 46 developing countries for the period of 1986-88. The debt of 25 countries was traded at a discount. Overall, the repayment indicators appear worse for these 25 countries. Furthermore, almost all of the countries in this group have had rescheduling agreements with their creditor banks, in contrast to the remaining group with almost no reschedulings.2 Table 1 also indicates that the countries with debts that are traded at a discount have larger debt outstanding to private creditors. A closet inspection of this variable for countries that are not in the traded group is instructive. In this group countries that may be judged to have good
6 repayment prospects have larger outstanding debt to private creditors: near 16 billion real U.S. dollars for Korea<sup>and near</sup><sup>_b_billion 1986U.S.dollars</sup> for Greece. In cOhtrast, countries with<sup>relatively bad repayment prospects</sup> also appear in this group, such as Ethiopia,<sup>and Trinidad-and-Tobago. Debt</sup> to private creditors are .08 b.llion real<sup>U.S. dollars, and .19 billion real</sup> U.S. dollars for Ethiopia and Trinidad-and-Tobago **respectively. One** plausible suggestion drawn from these observations is that transaction costs of trading private debt in the secondary market,<sup>prevents</sup><sup>**trading**for</sup> countries with small amount of private debt.

Table 2 presents some summary information concerning the secondary market discounts for the 25 countries whose<sup>debt was traded at a discount in</sup> the market and bank exposures for the<sup>1986-88 period. The discounts</sup> presented in this table are calculated using<sup>bid prices. The mean discount</sup> for all countries over the period 1986-88<sup>is 54.24 (with a standard</sup> deviation of 24.24) and the mean exposure<sup>is $U.S. 2.28 billion (with a</sup> standard deviation of $4.11 billion). A<sup>cursory inspection of the data, on</sup> a country by country basis, shows that small<sup>borrowers, such as Bolivia,</sup> Liberia, and Sudan, trade at rather<sup>large discounts, in contrast to the</sup> larger borrowers.

Another interesting feature of Table 2<sup>is the pattern of the secondary</sup> market spreads, calculated as the percentage<sup>differences between the offer</sup> and bid prices quoted in the secondary<sup>market. For countries with large</sup> discounts, spreads are also quite<sup>large, suggesting that an investigation</sup> of bid prices only may be misleading.
# 2.2 Empiricai Specifica ion

Trading of loans at a discount in the secondarv market have taken place onlv for some of.r.he indebted countries. As the discussion in the data section indicates, on average those countries are<sup>1</sup> ess creditworthy and they have larger private debt. Accordinglv. define T* as follows (for **convenience time and** country indicators **are omitted):**

- where T - a latent variable such that, if T* > **0 then we observe trading** at a discount in the secondary market, but if T* <-O **then we do** not observe trading at a discurnt in the secondary market,

- variables that determine the occurrance of trading in the secondary market, including repayment indicators and the

- magnitude of debt to private creditors,

uI- normally distributed error term with standard error a1 The magnititude of discounts has been shown to be related to creditworthiness indicators. In addition, as posited in this paper, the discounts are expected to be influenced by bank exposure and capitalization rates as described below: (2) D* - **-y O'** + u2 where D* - discount in the secondary market, O' - in addition to the variables in 0 bank exposure and bank capital are included, U2 normally distributed random error term with a standard error a2 However, we observe positive discounts only when T* > 0, and the debt appears to be at par otherwise
It **is** well known that if **the** sample is censored, as **it** is **here,** employing data only for those countries with positive **-'iscounts and using** least squares method to estimate an equation described as in (2) **,ields** biased and inconsistent parameter estimates. **Alternatively, one may employ all** the observations by assigning zeros to **those countries for which** positive discounts are not recorded, and employ least squares method. This approach implies that if the debt of those countries were traded, th" secondary market price would be zero, which obviously is incorrect. Accordingly, we employ a censored regression estimation technique as discussed next.

**The** model described above is a Tobit model (Tobin 1958) (type two Tobit model according to the classification of Amemiya (1984)). The standard Tobit model is a special case of the model where T* - D*. It has been shown that Heckman's two-step estimator (Heckman 1976) can be used in this type of a model (Amemiya (1984)) and that it yields consistent **parameter** estimates. According to this method, one first estimates the probit model described in equation (1) using maximum-likelihood procedure and obtains the inverse Mills' ratio (or the hazard rate).3 The second stage equation is:

where **X** - inverse of the Mills' ratio (hazard rate),

**_e2_ -** error term with mean zero.

Equation (3) is estimated after replacing **A** with its estimated vc.lue form
9 equation (1), and using ordinary least squares method. The problem with this approach is that, the asymptotic variance-covariance matrix of **the Heckman** estimator is notScQnsistent.5 It has been pointed out by Lee (1982), however, that consistent **estimates of the variance covariance matrix can be** obtained by applying an estimator similar **tho** the **heteroskedasticity consistent estimator developed by White** (1980). **Accordingly, in this paper** we use Heckman's two **step estimator and follow the idea of Lee (1982) so as** to obtain consistent estimates of equation (3).

# 2.3 Estimation Results

In this section we present results from the **estimations of the equations** described in the previous section: the probit specification for the occurrence of trading in tha secondary market **as** in **equation (1), and the** secondary market discounts specified as a Heckman equation as in (3). The primary result of our empirical investigation **is:** holding **repayment** prospects of the borrowers constant, the discount in the secondary market decreases with increased bank exposure.

In order to investigate the robustness of these **results we attempted a** number of alternative specifications for the two equations. Two issues require attention. First, it is important to establish that the results are not a consequence of a particular choice of economic **indicators as measures** of country risk among the potential ones. Second, the results should not be contaminated by specific market characteristics such **as its size.** In **order** to address the first potential problem we employed alternative specifications of country characteristics. To address the second **source of concern,** we calculated the discounts as averages of bid and ask prices in the secondary markets, alternativcly we employed lagged values of spreads as an
expEallatorv variable in a specification where the discounts are calculated trorn the bid prices onlv.

In Tables 3-4.we preseot estimation results for two primary empirical m:odels, with five different speficatiors each for the probit **as** well as the Heckman equations. The second stage equations differ from the **probit** equations bv the inclusion of bank exposure and bank capital **variables. We** next describe the differences between these models and **va-ious** specifications: (In addition to the variables that are described below, each specification employs region-specific and quarterly **dummy variables.)** Model 1 (presented in tables 3-4) specification (1) incorporates the following variables: stock **of** debt to private creditors, **debt-to-exports,** reserves-to-imports ratios, real GNP per capita, the inflation **rate and the** ratio of countrv exposure to bank capital. Specifications 2-4 **differ from 1** primarily by inclusion of investment-to-gnp ratio, a measure of **income** distribution, and share of agriculture in gdp, respectively. **Specification** 5, is like specification 1, but the dependent variable **is** the discount calculated using only the bid price. In addition, in specification **5, the** lagged value of the spread between bid and ask prices is incorporated **as** an explanotory variable. Model 2 (presented in tables **5-6) primarily replaces** debt-to-exports and reserves-to-imports by debt-to-GNP, imports-to- GNP, and reserves-to-GNP. Alternative specifications of Model 2 are structured analogous to the alternatives of Model 1.6 Table 3 shows that higher debt to exports ratio, **higher inflation,** lower GNP per capita, and lower reserves to imports ratio **increase the** likelihood that, debt of a country is traded at a discount in the **secondary** market. 'The estimated probabilities of trading are large for all the countries, for which their is trading and thev drop dramatically for the
remaining countries.) The amount of debt outstanding to private creditors increase the likelihood of trading.

Table 4 indficates that, with the exception of the income distributiun variable, variables that contribute towards the occurence of trade, increase the magnititude of discounts; the variables that decrease the likelihood of trading decrease the magnititude of discounts. The sign of the agriculture to gdp ratio is consistent with Berg and Sachs (1989). The sign of the income distribution variable, however, differs from that of Berg and Sachs: we find that the debt of countries with historically worse income distribution were traded at lower discounts in the secondary market. The bank exposure is found to be negatively related to the discouts.

Table 5 indicates that GNP per capita and inflation rates continue to hold the same signs as in Tables 3-4, and they remain important determinants of trading. Higher reserves- to-GNP, higher imports-to-GNP, higher debt service payments to exports ratio all reduce the likelihood of trading. Higher debt-to-GNP ratio is shown to increase the likelihood of trading. Estimated probabilities from all these specifications also seem reasonable in that actual no-trading countries are assigned quite low probabilities of being traded, in contrast to the remaining countries. The discount equation estimations for this model are presented in Table 6. Except for income distribution all the variables continue to hold the same signs as in Table 5. As in Model 1, worsened income distribution is found to contribute to increased probability of trading but it appears to reduce the discounts. Exposure variable is found to be negatively related to the discounts.

Overall, the signs of repavment indicators are consistent with their expected signs: high debt ratio for a given amount of penalty for nonpavment makes default more likelv. The negative sign of reserves to gnp
12 ratio suggest that it is an indicator of the level of international liquiditv of the borrower. A high ratio of imports to gnp makes the borrower more. cteditworthv, since the borrower becomes more vulnerable to trade embargos. High inflation appears to be an indicator of a larger probability of balance of payments crisis. Finally, countries with high gnp per capita are found to be more creditworthy.

It is important to note several other **specifications to confirm the** robustness of our findings. First, our findings are not a consequence of a few extraordinarily influential observations. **Specifically, if Mexico and** Brazil (the largest two ex:posure countries) **are deleted from the sample all** the findings continue to hold. Second, our findings continue **to hold even** when we control for the size of the country. To control for the size of the country we have replaced the per capita real gnp with **real gnp.** Two important findings that emerge from these tables are important findings of this paper: First, independent of the specific form employed to control for repayment prospects of the borrowers, the exposure **of banks to** a country continues to hold an important role in **determining** the **magnitude of** secondary market discounts. In particular, high bank exposure to a country leads to smaller secondary market discounts. The elasticity **of discounts** with respect ot bank exposure is 10-15 percent in most specifications. Second, we find that increased bank capital increases the **secondary market** discounts. The magnititude of this impact is substantial: near 25 cents on the dollar for 8 billion U.S. $. (It is **also important** to **note that when** the exposure, and capital of the largest 24 U.S. banks are employed, instead of the largest 9 banks, the results we obtain are very **similar to the ones** reported in Tables 3-6.)
13

# 3. **How** Does DeRosit Insurance Affect Secondary **Market** Prices ?

## 3.1 A Model

This secti6ot-presents a model of the influence of deposit insurance on the pricing of developing country debt. In particular, the model examines how a representative bank that can trade in the secondary **market and that** is subject to deposit insurance, capital **requirements and accepted accounting** principals. Subsequently, the model is simulated by using exposure data for the largest U.S. banks.

In this two period model, the bank's loan **portfolio consists of two kinds f** of loans: domestic and foreign. The bank's exposure to country **i, Lt, is** long term and given by history in the first period. Country **i defaults with** probability **n** d, in which case the bank will not receive **any** payment from the country. In case of no default, foreign **loans pay** the **principal and the** f **h** foreign interest rate r. . The domestic loan portfolio, denoted L , **is** h short-term yielding a certain domestic return r . The bank **can adjust its** domestic loan portfolio by making new loans in the first period, and adjust its foreign loan portfolio by engaging in trading in the **secondary markets.** Deposits, denoted D, are assumed to be partly insured by a deposit **8** insurance agency and the deposit holders are risk neutral . **The average,** e9 effective interest rate, r , that banks have to pay to attract deposits is: where, dS and du are the shares of insured and uninsured **deposits,**<sup>**and rs**</sup> and r are the safe interest rate and the rate on uninsured<sup>deposits</sup> respectively.

Assuming that the bank will be closed in the future **period if the**
'alue of the bank's assets is less than the value of its liabilities, the future value of the bank is described below: k5) where V* (+r h)L h (+re)D.

V- expost value of foreign loan **exposure.** The bank's capital, denoted K , is **asssumed** to **be given, and the** regulators impose minimum capital requirements on domestic **and foreign loans** denoted 9h and 6 , respectively.

Let us now turn to the buying and selling decisions of **a bank that** observes a secondarv market price P. for a dollar of debt of country i. If the bank decides to sell the debt of a country, which is kept at face value on the books, then the bank realizes a holding loss. This **is because the** current regulations require the bank to write down all the previously acquired loans to this countrv. Realized holding losses reduce regulatory capital K. A reduction of regulatory capital losses forces the bank to curtail its domestic loan activities. If these activities **are very** rewarding, i.e. if the spread between domestic loan and deposit rates is verv large, then such a write down is prohibitively unprofitable.12 Thus banks' ability to carry developing country debt at face value effectively prevents sell offs of these loans.

A bank that buys developing country debt can carry its **debt** at **market** value without a write down of any previously acquired loans. Despite this favorable accounting treatment of debt purchases, banks can only **expand** their portfolio's in any direction to the extent that they have excess
15 regulatory capital. In the remainder of this section, we assume that the bank has one dollar of excess capital, which clearly restricts the bank's demand for develo;ng countrv debt. The bank is assumed to be interested in maximizing expected bank value in the second period. Secondary market prices a bank is willing to pay are then determined competitively by the condition that the bank can increase expected bank value equally by expanding its portfolio by purchasing loans to any country **or by expanding** its domestic loan porfolio.<sup>13</sup> To determine what developing country debt is worth to **the bank it has** to calculate the probabilities of states of nature in which holding this debt is going to contribute towards the value of the bank. These probabilities crucially depend on the relative size of banks' exposure to developing countries. This is because the bank's exposure to country j can only be worth something to the bank in the next period in those states of nature where any losses the bank sustains on its other developing country exposure are not large enough to bankrupt the bank. Accordingly, define itf to be the probability that loans to country i contribute something to the value of the bank: where V - the ex post value of the bank's foreign loan portfolio, excluding the bank's exposure to country j.

Conditional on the bank surviving if the country pays, the bank may continue to survive even if the country does not pay. The probability of this event is denotedf with I f 14 is denoted with i where wi.- prob (V + V i . > 0). Suppose the bank changes its balance sheet by using one dollar of
16 capital to purchase loans in the secondary market. The purchase leads to f two effects on expected bank value. First is a direct effect, denoted bi, that holds r e Qatnstant as below: The second effect of the purchase on **the bank value is the interest** effect through the adjustment of r . To **see** this **define S i** as the surplus generated from purchasing loans in the secondary **market, to be divided** between the bank and the deposit insurance agency (note that depositors on 15 average require a return rs) . If all deposits are uninsured **then the** expected gain to the bank is all of the surplus Sf 16 In contrast, if all deposits are insured, then re _ rS and the bank **gains bf as in** (7). **i** Accordingly, if part of deposits are insured, the expected **gain taking into** account both the direct balance sheet and indirect interest rate effects is: Analogously, define h to be the probability that one **additional** do1lar of domestic loans is going to contribute something to the future value of the bank, and b ' to be the expected gain from allocating **one** dollar capital to backing domestic loans.

For the case where the bank holds domestic **loans as well as loans** to all n countries and the capital requirement **is** binding, **the optimality** conditions require that b f'- bh, for all i.17 Accordingly, Pi is implicitly defined as a function of all the exogenous **variables, in** particular capital requirements, the bank's capital, the total size
11' distribution of developing countrY loan portfolio, the country's default probabilities, the given interet rates and the extent of deposit insurance.

# 3.2 Simurttion Method

To simulate the model described in the previous **section, we need to** compute Ih **Ifw** and Xi A direct method for implementing this model h i i . involves considering every possible outcome (i.e., **every permutation of** countries and defaults/payments), each weighted by its probability, **and the** fraction of successful outcomes. This **method is unsuitable for a large sample of** N countries, because the number **of outcomes to be considered is** _N_ 2 . To overcome this obstacle, we employ an algorithm for a large but finite number of representative outcomes, taking **advantage of the law of** large numbers to ensure accuracy of the results.

The algorithim is a very simple Monte Carlo procedure: **A represen-** tative event is generated by obtaining a default/payment from each country according to the following rule:

# (9) RND < ird implies default,

where RND - a random number on the (0,1) interval.

Accordingly, the outcome (bank failure or solvency) of the **event is** determined by summing the payments from all nondefaulting countries, i.e., computing V, and comparing it with V . This procedure is repeated m times, to obtain as many events as are desired for **statistical precision in** the ratio of successful outcomes to total events. Typically, precise results are obtained with m - 10 events runs, as judged by the **reproduc-** ability from run ro run. (This in accordance with the law of large numbers, which suggests relative precision approaches m l/2.) Similarly, to **obtain**
18 ff countrv-specific information on Yr. and ?i the algorithm actually used is extended from (9) above to compute modified bank-failure prok-' :lities conditional upon-pavment of the ith country.

# 3.3 Si;.iulation Results

The method just described is employed to **simulate the model.** In **all** the simulations, default probabilities of countries are assumed to **be equal** to focus on the impact of exposure distribution. First, a **benchmark case** (for 1985) is simulated. Second, the **following alternative scenarios are** considered: an increase in the default probability of **countries, a decrease** in banks' capital, an increase in miniimum **capital requirements, and an** increase in the share of insured deposits.

The results of the benchmark simulation and the parameter **values are** in Table 5 colomn (1). Secondary market prices are positively **related** to the extent of the country's bank indebtedness as is consistent **with our** empirical findings (prices range from about 49.9 cents for the smallest debtors to 54.4 cents for Brazil). The probability of the **bank's survival** is 88.9%, the expected value of the bank is roughly $19.9 billion, and the expected loss of the deposit insurance agency loss is about $3.5 **billion.** d An increase of the default probability, n , from 0.50 to 0.70 significantly decreases the bank's chance of survival to 55.7% (column 2). The bank's expected value is reduced, the deposit agency's **loss is** augmented, and secondary market prices decrease.

Second, we consider a reduction of the amount of bank **capital the bank** can loose before it is closed, as presented in coulumn 3. This **yields a** lower survival probability for the bank, lower expected future value of the bank, and higher expected losses for the deposit agency. Lower bank capitalization engenders higher secondary market prices, reflecting the
- **.** .. !; '.,: **ii** t : .+, . ' ': . ., . ' z ' t **0** e . ' ' : t Ofl: ' r" e1 -' <\1 :ip6l- I n I.. .l1 j><t(.r!. . t v 'I F.-..''. <.:s:. : 'i.i - tt.i mI.IlaPton resuits for the case wheret :wr(ot oihk(J',1(sits *ire i u".lrt-d t' *his instance, it does not benefit the 'bo.nk. to rvnder **'.** a **pit'** )ss. oio ri,'kier, since the deposit rates it has to pa; to attract deposits fully refit' the riskiness of the asset portfolio Hence, secondary mrarke- prices for all countries are the same. 18

. Are the Results a Consequence of Implicit Insurance ?

The simulation model shows that the existence of a deposit<sup>insurance</sup> s:stem can explain the negative relationship between<sup>bank exposure to a</sup> countrv and the discount of its debt in the secondary<sup>market. However, an</sup> alternative explanation mavbe based on the behaviour of<sup>multilateral lending</sup> agencies and creditor country governments.19 Since one of the aims of these institutions is to increase the stability of the creditor country banking svsteas, thev are more I ely to provide lending to debtors to which banks are more heavilv exposed. In this manner the third parties will<sup>be</sup> enhancing the repavment prospects of those debtors,<sup>providing implicit</sup> subsidy to the banks. Accordingly, the positive relation between exposure and discounts would be a mere reflection of the enhanced r payment prospects of the borrowers.

In this section we provide evidence that support the view that explicit guarantees bv a deposit insurance agencv (FDIC) have an impact on the secondary market prices. The first evidence. somewhat cursory in nature, is -he time trend of the discounts over the period considered. As presented in Table 6. average discounts increased form 44.8 to 64.7 between the first
**-jA r )-** @'and **6** ' 1ti. j:-., .'i I t of 1'4S8 Trhe pat tern is somewhat r: ., j: **vc.t.** ahat a r :-::r!'r rIt the borrowers, in adrticular majo! borr''O'E'rS ha'.'e-teE. r'.iin, **net** tr.insfers to their creditors. **Ani** explanation for this pattern, that is c'onsistent with the implicit insurance explanation mav, of course, he that net repayments at present are not good indicators of future repayment prospects. Alternativelv, the time pattern of the discounts could be a consequence of the strengthening of the capital position of U.S. banks, which is consistent with our model and empirical estimates.

The second evidence which supports the FDIC explanation **of** the relationship between bank exposure and secondary market prices, is provided by an investigation of stock market pricing of lender banks. U.S. banks differ widelv in the extent of their developing country exposure and capitalizat.on rates, and thus the banks faze very different probabilities of ever receiving any pavments from the FDIC. The heavily exposed banks thus have a more valuable contingent claim on the FDIC, which to some extent softens the negative impact of developing country exposure on bank valuation. W;e next describe this by expanding the model of section 3.1.

As before, the bank carries domestic loans, Lh, and developing f country debt, L The bank has capital, K, and is subject to a binding uniform capital requirement such that K - 9 (Lh + L f). Deposits D are equal to Lh + Lf - K from the bank's balance sheet, and there is partial deposit insurance. Let 6 be the realized future discount on developing country debt, i.e.. the bank will receive 1-6 dollars for every dollar of developing country debt. The discount 6 is taken to be uniformlv distributed on [0, 6'. where 6 s i. For simplicitv, assume that domestic and foreign loans pav a contractual interest of zero. and risk-neutral deposit holders require
,in expecced return of zero **a~.** 1 In this setting, we can derive the lol.owing rtlation for the tc.'-c:ed value, EB, of the hanik where Q - -(1/2)S is the mean discount on developing country **debt,** 2 a' _ 9 /(25).

The above equation states that **for high values of bank capital, bank** value is positive for any realization of 6 and the relation between bank value and exposure to developing countries as a share **of assets is linear.**<sup>**2 0**</sup> In contrast, for low values of bank capital the bank can be bankrupted, and hence the bank has contingent claims on the FDIC. In this case, the relation between bank value and share of foreign debt exposure becomes convex. This is because contingent claims on FDIC softens the negative impact of developing country exposure on bank valuation.<sup>2 1</sup> We approximate the relation described in equation (10) by the following equation: The hypothesis that deposit insurance matters corresponds to a1 > **0.** Equation (11) is estimated for 23 banks. 2 Data are for 1987 and the L variable captures the exposure of banks to four large Latin debtor countries: Argentina, Brazil, Mexico, and Venezuela.23 The regression results are reported in Table (9) 24 The negative estimate of a0 ic.dicates
^:--: i ~ ~ ,'''t **I 1)** : ' . t.S)}ti 'l it **.i** t ' k ', I: ; ' *'il **cI a** bo it sed to _calIculate_ **LoW ibank va 'ue** is **at Ic td** O'; av (o I r' ilc rease ot developing country k-:9,p05'1r Which j **'In1** he a'll t'1 the .arginal discount. The evidence, in support ot the FDIC hvpothesis, indicates that heavily exposed banks value dv.eloping countrv debt more hit,hlv **at** the margin than the less **exposed** banks (as reported in Table 9) The calculated marginal discounts are consistent with the trend that the smaller, regional banks are more aggressivelv using the secondary market to reduce their developing country exposure. The wide disparit; of marginal discounts also indicates that There are large profitable trading opportunities for interbank trades, as U.S. banks can inicrease their combined expected claim on the deposit insurance agency bv trading developing country debt.

The varying valuation of developing countrv debt across banks cannot be explained bv any differences in treatment of indebted countries by national governments or multilateral lending agencies that may give rise to differing repa';ment prospects across countries. As banks tend to hold developing countrv loan portfolio's of more or less equal composition, such differential treatment of countries can not explain different valuation of developing country debt across banks.<sup>2 5</sup>
23

# 5. Concluding Remarks

This paper has empirically demonstrated<sup>that secondary market discounts</sup> are negativelv_related to banks' exposure<sup>to countries. The evidence also</sup> suggest that discounts and bank capital<sup>are</sup><sup>**strongly positively related over**</sup> time. The results are explained with<sup>the aid</sup><sup>**of a simulation model, which**</sup> **incorporates deposit** insurance **combined with**<sup>**limited liability. In this**</sup> model, secondary market pricing<sup>is motivated by banks' desires to reshuffle</sup> developing cou -ry loan portfolio's<sup>so as</sup><sup>**to increase the value of their**</sup> implicit elaim on the deposit insurance<sup>agency.</sup> The observed negative relationship **between**<sup>**between bank exposure and**</sup> secondary market discounts can also be<sup>explained by the alternative</sup> hypothesis that the multilateral lending<sup>agencies and</sup><sup>**creditor country**</sup> governments tend to favor lending to the<sup>larger debtor countries.2</sup> Our evidence for a convex relation between<sup>stock market valuation of banks and</sup> developing country exposure, however,<sup>supports the</sup><sup>**view**that</sup><sup>**contingent**</sup> claims on the deposit insurance agency<sup>are important determinats of</sup> secondary market prices. The strong<sup>positive correlation of</sup><sup>**discounts and**</sup> bank capital over time is further evidence<sup>that is consistent with the</sup> explanation provided in this paper.

A main result of our model is that bank<sup>regulation, and in</sup><sup>**particular**</sup> capital requirements, can have repercussions<sup>for prices in the secondary</sup> market. The model suggests that a<sup>tightening of capital standards can be</sup> expected to increase secondary market<sup>prices,</sup><sup>**as**it</sup><sup>**forces domestic banks to**</sup> curtail potentially profitable lending<sup>activitities that can generate income</sup> to serve as a buffer against bankruptcy.<sup>Conversely, the</sup><sup>**sometimes applied**</sup> policy of capital forbearance which<sup>is the loose application of capital</sup> reachivements provides banks with<sup>a potential opportunity to earn their way</sup>
24 back to solvency, with a negative price effect on developing<sup>country debt.</sup> Ovarall, creditor country factors such as the exposure distribution of banks portfoll -ar,d the regulatory environment are demonstrated to be important determinants of secondary market prices. These findings strengthen the arguments that buybacks may be harmful<sup>to countries and</sup> undermine the arguments for using secondary market<sup>prices as indicators of a</sup> country's repayment prospects. In addition, our<sup>evidence</sup><sup>**supports the viev**</sup> that deposit insurance biases banks against providing debt relief.