Working Paper Series 

Congressional Budget Office 

Washington, D.C.

PRECAUTIONARY DEMAND FOR FOREIGN ASSETS IN SUDDEN STOP 

ECONOMIES: AN ASSESMENT OF THE NEW MERCANTILISM 

C. Bora Durdu 

Congressional Budget Office 

E-mail: bora.durdu@cbo.gov 

 

Enrique G. Mendoza 

University of Maryland, International Monetary Fund and 

National Bureau of Economic Research 

Email: mendozae@econ.umd.edu 

Marco Terrones 

International Monetary Fund 

Email: mterrones@imf.org 

July 2007 

2007-10 

Working papers, formerly termed technical papers, in this series are preliminary 

and are circulated to stimulate discussion and critical comment. These papers are 

not subject to CBO’s formal review and editing processes. The analysis and 

conclusions expressed in them are those of the authors and should not be 

interpreted as those of the Congressional Budget Office. References in publications 

should be cleared with the authors. Papers in this series can be obtained at 

http://www.cbo.gov/publications/. 
 May 8, 2007 

Precautionary Demand for Foreign Assets in Sudden Stop Economies: 

An Assessment of the New Mercantilism *

By 

 Ceyhun Bora Durdu Enrique G. Mendoza Marco E. Terrones 

Congressional Budget Office University of Maryland International Monetary Fund 

 International Monetary Fund 

 & NBER 

 Financial globalization in emerging economies has been challenged by a series of Sudden 

Stops since the mid 1990s. Foreign reserves grew very rapidly during this period; hence, it is 

often argued that we live in the era of a New Mercantilism in which large stocks of reserves 

are a war chest for defense against Sudden Stops. We conduct a quantitative assessment of 

this argument using a stochastic intertemporal equilibrium framework with incomplete asset 

markets in which precautionary saving affects foreign assets through three mechanisms: 

business cycle volatility, financial globalization, and Sudden Stop risk. In this framework, 

Sudden Stops are an equilibrium outcome produced by an endogenous credit constraint that 

triggers Irving Fisher’s debt-deflation mechanism. Our results show that financial 

globalization and Sudden Stop risk are plausible explanations of the observed surge in 

reserves but that business cycle volatility is not. In fact, business cycle volatility has declined 

in the post-globalization period. These results hold whether we use the formulation of 

intertemporal preferences of the Bewley-Aiyagari-Hugget class of precautionary savings 

models or the Uzawa-Epstein setup with endogenous time preference.

Keywords: Fisherian Deflation, Liability Dollarization, Credit Constraints, Sudden Stops 

JEL Classification codes: F41, F32, E44, D52 

 

*This paper was prepared for the Conference on New Perspectives on Financial Globalization held at 

the IMF, April 26-27, 2007. We thank the discussant, Dave Backus, for his thoughtful comments; 

Philip Lane and Gian Maria Milesi-Ferretti for sharing their data on cross-country foreign assets with

us; and Laura Alfaro, Woon Gyu Choi, Olivier Jeanne, Jaewoo Lee, Marcus Miller, Romain Ranciere, 

and Vincenzo Quadrini for their comments. The ideas expressed herein are those of the authors and 

should not be interpreted as those of the Congressional Budget Office or the International Monetary 

Fund. 
-1-

An absurdity does not cease to be an absurdity when we have discovered 

what were the appearances which made it plausible; and the Merchantile 

Theory could not fail to be seen in its true character when man began to 

explore into the foundations of things, and seek their premises from 

elementary facts, and not from the form and phrases of common 

discourse. (J. S. Mill, Principles of Political Economy, 1848, p. #5) 

1. Introduction 

 The early stages of financial globalization in emerging economies were characterized by 

a series of financial debacles and economic crises known as Sudden Stops. The indexes of 

capital account liberalization constructed by Edwards (2005) and Chinn and Ito (2005) show 

that financial globalization started in emerging economies in the late 1980s (see Figure 1). 

The waves of Sudden Stops that followed began with the Mexican crisis of 1994 to 1995. 

Table 1 lists 18 Sudden Stop episodes that occurred between 1994 and 2002. In addition, the 

period since the early 1990s witnessed a surge in foreign reserves in most Sudden Stop 

countries. As Table 1 shows, the median increase in reserves in those countries was 7.7 

percent of GDP (measured as the cross-country median of the differences between each 

country’s average reserves-to-GDP ratio from the year of the country’s Sudden Stop to 2004 

and the average from 1985 to the year of the Sudden Stop).1

 The increase was particularly 

sharp in the Asian Sudden Stop countries, where the median increase in reserves exceeded 13 

percent of GDP.2

 A popular view in policy institutions and academic circles is that this large buildup of 

reserves represents a form of self-insurance that emerging economies have taken against the 

risk of future Sudden Stops. The argument is that Sudden Stop countries, having realized 

that the sudden loss of access to capital markets is a shortcoming of financial globalization 

and aware of the limited financial mechanisms available to cope with Sudden Stops, opted 

for being a New Mercantilism in which large holdings of reserves are a war chest for defense 

against Sudden Stops. The studies by Aizenman and Lee (2007), Alfaro and Kanczuk (2006), 

Caballero and Panageas (2006), Choi, Sharma, and Stromqvist (2007), Jeanne and Ranciere 

(2006), and Jeanne (2007) examine key theoretical and empirical features of this New 

Mercantilism, and the potential to develop better insurance mechanisms. 

 This paper conducts a quantitative assessment of the New Mercantilism. We use a 

dynamic stochastic general equilibrium framework of optimal precautionary demand for 

foreign assets in a small open economy with incomplete asset markets. We quantify the 

 

1

 In most Sudden Stop countries, the change in reserves has been much larger than the change in net 

foreign assets indicating large portfolio shifts that are beyond the scope of this paper. Our focus is on 

how much of the increase in assets can be explained by precautionary motives. Still, portfolio 

considerations can be important for studying the surge in reserves (Alfaro and Kanczuk, 2006; Jeanne 

2007) and the dynamics of Sudden Stops (Durdu and Mendoza, 2006). 

2

 Setting the breakpoint in the year of the Sudden Stop is not critical. Similar qualitative results 

showing a surge in reserves are obtained comparing average reserves of Sudden Stop countries for the 

1986 to 2004 period with those for the 1970 to 1985 period. Because, 1985 is often viewed as the 

starting year of the globalization process, we can also say that reserves surged along with financial 

globalization. 
-2-

effects of three key factors that drive precautionary savings in this framework: (1) changes in 

the business cycle volatility of output, (2) financial globalization (i.e., the reduction of 

distortions affecting international asset trading), and (3) self-insurance against the risk of 

Sudden Stops. 

 The analysis proceeds in two stages. The first stage uses a canonical one-sector model of 

an endowment economy that faces noninsurable shocks in domestic income. Those shocks 

are noninsurable because asset markets are incomplete, but the economy still has access to a 

frictionless credit market in which it can borrow or lend at the world’s risk-free interest rate. 

The model is calibrated to match the variability and persistence of output in Sudden Stop 

economies and then used to compute the optimal short- and long-run dynamics of foreign 

assets triggered by changes in output volatility and financial globalization. 

 

 The second stage of the analysis studies a two-sector production economy with liability 

dollarization and endogenous Sudden Stops. The economy has a tradable-goods sector and a 

non-tradable goods sector, and nontradables are produced with imported intermediate goods 

(which are priced in world markets). Liability dollarization is present because non-statecontingent debt is denominated in units of tradables. Here, we reexamine the adjustments in 

foreign assets driven by financial globalization and business cycle volatility under the 

assumption of a frictionless credit market. The main goal, however, is to quantify the 

increase in foreign assets that is justified by optimal self-insurance due to the risk of 

endogenous Sudden Stops. To that end, we introduce a collateral constraint that limits debt 

not to exceed a fraction of the value of total income in units of tradable goods. As Mendoza 

(2006a) explains, this credit-market imperfection causes endogenous Sudden Stops because of 

the strong amplification mechanism that results from combining liability dollarization with a 

Fisherian deflation of the relative price of nontradables. In this setup, the precautionary 

demand for foreign assets takes into account how foreign asset holdings alter the probability 

and the magnitude of Sudden Stops, both of which are equilibrium outcomes of the model. 

 The paper’s quantitative analysis yields three main findings: First, financial 

globalization, even without Sudden Stops, can trigger significant increases in mean foreign 

asset holdings. Second, the risk of Sudden Stops can also produce significant increases in 

foreign assets, even when the long-run variability of output is unaffected by Sudden Stops. 

Third, changes in output volatility cannot explain the observed surge in foreign reserves. 

The models predict large increases in foreign assets in response to higher variability of 

income. In the data, however, no evidence points to systematic increases in the standard 

deviation of cyclical output for Sudden Stop economies in the era of financial globalization 

(see Table 2 and Figures 2 and 3). In some countries, volatility increased, but in many 

others it fell and this is also the case for the mean and median of the group. Looking at 

sectoral GDP volatility, we do find that the tradables GDP of Sudden Stop countries 

became more volatile (see Table 3), but not by the magnitudes that the model would require 

to explain the observed surge in reserves. 

 Our model also yields an important result in terms of the dynamics associated with the 

surge in reserves: The large buildup of foreign assets in response to financial globalization or 

Sudden Stop risk is a slow, gradual process characterized by current account surpluses and 

undervalued real exchange rates. Those dynamics do not require central bank intervention to 

target the real exchange rate in efforts to promote exports. Hence, our results can resolve 
-3-

the dichotomy dividing self-insurance-based explanations of the surge in reserves (Aizenman 

and Lee, 2007) from explanations based on external surpluses and undervalued exchange 

rates (Dooley, Folkerts-Landau and Garber, 2003). 

 

 Our framework yields predictions for precautionary savings under two specifications of 

intertemporal preferences that have not been compared before: the Bewley-Aiyagari-Hugget 

(BAH) approach (which for a small open economy requires a constant, exogenous rate of 

time preference higher than the world interest rate) and the Uzawa-Epstein (UE) approach 

(which features an endogenous rate of time preference). The BAH approach is widely used in 

the precautionary savings literature, whereas the UE approach is often used in Real Business 

Cycle (RBC) models of small open economies with incomplete markets. Both approaches 

feature precautionary savings because agents build a buffer stock of savings to facilitate 

consumption smoothing. In particular, they assume that the marginal utility of consumption 

goes to infinity as consumption approaches zero, so agents are extremely averse to holding 

asset positions that leave them exposed to the risk of “very low” consumption at any point 

in time. However, the elasticity of mean foreign assets with respect to the interest rate 

differs sharply in the two approaches; therefore, their quantitative implications for 

precautionary savings need to be studied separately. 

 The BAH approach requires a constant rate of time preference higher than the interest 

rate because, if the two rates are equal, optimal precautionary savings would imply 

accumulating an infinite amount of foreign assets: Agents desire a nonstochastic 

consumption stream, but they need an infinitely large buffer stock of assets to support it 

because income is stochastic and capital markets do not offer enough insurance instruments 

to diversify income risk fully. Mean foreign assets under this approach increase as the gap 

between the interest rate and the rate of time preference narrows, with mean foreign assets 

(and their elasticity) going to infinity as the rate of interest converges to the rate of time 

preference from below. Thus, at interest rates close to the rate of time preference, the BAH 

setup predicts that small variations in the interest rate will trigger large adjustments in 

average foreign assets. By contrast, the UE approach models the rate of time preference as 

an increasing function of past consumption but imposes conditions that limit the magnitude 

of this “impatience effect.” The UE approach yields a long-run rate of time preference that 

converges to the world interest rate in a nondegenerate equilibrium and a well-behaved 

stochastic stationary state in which mean asset holdings are less sensitive to changes in the 

world real interest rate. In fact, in our quantitative experiments, the elasticity of mean 

foreign assets in the UE setup is approximately constant at high or low interest rates. 

 A contribution of our analysis is that the effects of business cycle volatility, financial 

globalization, and Sudden Stop risk on foreign assets are examined within a common 

framework and under alternative preference specifications. The existing literature has 

produced interesting results by examining these factors separately. Fogli and Perri (2006) 

study a two-country model in which the Great Moderation (the decline in relative output 

volatility of the United States vis-à-vis the rest of the world) leads to a buildup of foreign 

assets in U.S. trading partners. The surge in foreign assets, however, is small relative to the 

magnitudes shown in Table 1. The Great Moderation has also resulted in higher output 

volatility of Sudden Stop economies relative to the United States, but that result is driven 

largely by the moderation of U.S. business cycles—not by higher volatility in Sudden Stop 

economies (see Tables 2 and 4 and Figures 2 and 3). In our framework, this pattern of 
-4-

changes in relative volatility is akin to a permanent increase in the risk-free interest rate 

faced by Sudden Stop economies, a change that causes those economies to increase asset 

holdings due to similar effects as those that result from financial globalization. Thus, the 

surge in reserves in this scenario is not the direct result of self-insurance against higher

business cycle volatility inside Sudden Stop countries, but the indirect result of lower U.S. 

volatility on the world interest rate. 

 

 Mendoza, Quadrini, and Rios-Rull (2007) study how financial globalization affects 

foreign asset positions and their portfolio structure in a world composed of countries with 

different degrees of financial development (i.e., asset market incompleteness) and inhabited 

by heterogenous agents that face noninsurable idiosyncratic risk. They find that financial 

integration leads to a large, gradual buildup of assets against the country with the highest 

degree of financial development. We focus instead on the implications of aggregate risk in a 

representative-agent small open economy, but our results are consistent with theirs in 

showing that financial globalization can lead to large increases in foreign asset holdings. 

 Mendoza (2002) and Durdu (2006) examine Sudden Stop models similar to the one 

studied here. Mendoza shows that when the model is calibrated to Mexican data, the shift 

from perfect credit markets to a world with Sudden Stops increases the average foreign 

assets-GDP ratio by 13.8 percentage points. Durdu examines how hedging and self-insurance 

options change with financial innovation in the form of GDP-indexed credit contracts. 

 The rest of the paper is organized as follows. Section 2 presents the one-sector model 

and examines its quantitative implications. Section 3 presents the two-sector model with 

liability dollarization and endogenous Sudden Stops. Section 4 presents our conclusions. 

2. One-Sector Endowment Economy 

2.1 Structure of the Model 

 Consider a small open economy inhabited by a representative agent that consumes a 

composite good c. The agent’s preferences are given by 

1 1

0

0 0

exp ( ) ,

1

( ) ln(1 ) ln(1 )

t

t

t

UE BAH

c

E v c

v c c or

γ

τ

τ

γ

ρ ρ

∞ − −

= =

             − 

       −       

= + +

∑ ∑

 (1) 

Period utility has the form of constant relative risk aversion (CRRA), and γ is the relative 

risk aversion coefficient. The time preference function v(c) takes one of two forms. With the 

UE formulation, the rate of time preference is endogenous and given by ( ) ln(1 ) UE v c c = + ρ , 

where UE ρ >0 measures the elasticity of the rate of time preference with respect to 1+c. 

With the BAH formulation, the rate of time preference is given by the standard constant 

fraction 0< BAH ρ <1 (i.e., the typical exogenous discount factor is 1/(1 ) BAH β ρ ≡ + ). 

 The economy chooses consumption and foreign assets as to maximize (Eq. 1) subject to 

the standard resource constraint: 
-5-

1

(1 ) c y b b r A t t t t = − + + + ε + (2) 

The economy’s mean or trend income, y, is subject to random shocks, εt, which follow a 

first-order, irreducible Markov chain. Foreign assets, b, are one-period bonds traded in a 

frictionless global credit market. These bonds pay a net risk-free real interest rate equal to r

(so the gross interest rate is given by R≡1+r). Given that in the data absorption includes 

investment and government expenditures, and not just private consumption, we introduce a 

constant lump-sum level of exogenous absorption A that will allow us to calibrate the model 

to match output shares of c and b consistent with actual data. 

2.2 Equilibrium 

 

 The optimization problem of this small open economy is analogous to the optimization 

problem of a single individual in the heterogenous-agents models of precautionary savings 

(e.g., Aiyagari, 1994, or Hugget, 1993). As in those models, CRRA utility implies that the 

marginal utility of consumption goes to infinity as consumption goes to zero from above, 

making the economy “extremely averse” to consumption and savings plans that would leave 

it exposed to the risk of “very low” consumption at any date and state of nature. To rule 

out these plans, agents in this economy impose on themselves Aiyagari’s Natural Debt Limit,

by which they never borrow more than the annuity value of the worst realization of income: 

b y A r t t +1 ≥ − + min( )/ ε . In addition, following Aiyagari (1994), we can impose an ad-hoc 

debt limit φ such that b y A r t t +1 ≥ ≥ − + φ ε min( )/ . 

 The optimality condition of the economy’s maximization problem is 

( ) exp( ( )) [ ( 1)] 1[ ] U t v c E U t r c t t c = − + + (3) 

Note that ( ) U t c denotes the lifetime marginal utility of date-t consumption. In the BAH 

setup, ( ) U t c is just the standard period marginal utility of ct. In the UE setup, however, 

( ) U t c includes both the period marginal utility of ct and the impatience effects by which 

changes in ct affect the subjective discounting of all future utility flows after date t. 

 A competitive equilibrium for this small open economy is defined by stochastic 

sequences[ ] 1 0,t t tc b ∞

+ =

that satisfy the Euler equation (Eq. 3) and the resource constraint (Eq. 

2) for all t. The structure of asset markets has important implications for this equilibrium. If 

the economy has access to complete insurance markets to fully diversify away all the risk of 

domestic income fluctuations, the equilibrium would feature a constant consumption stream 

and the economy’s wealth position vis-à-vis the rest of the world would be time and state 

invariant. If the asset market is limited to non-state-contingent bonds, however, the wealth 

position changes over time and across states of nature, and consumption cannot attain a 

perfectly smooth path. With BAH preferences, the economy attains a well-defined long-run 

distribution of foreign assets (i.e., a well-defined stochastic stationary equilibrium), only 

if β [1 1 + < r ] .

3

 With UE preferences, a well-defined long-run distribution of assets exists if 

 

3

 In this case, the marginal benefit of an extra unit of foreign assets follows a non-negative 

supermartingale that converges almost surely to a nonnegative random variable (see Ch. 17 in 

Ljungqvist and Sargent, 2004). Thus, convergence is attained with the marginal benefit of savings 

remaining finite and moving randomly in the long run and, hence, the long-run averages of assets and 
-6-

UE ρ γ ≤ (see Epstein, 1983). That is, UE preferences limit the size of impatience effects by 

requiring the elasticity of the rate of time preference with respect to consumption not to 

exceed the inverse of the elasticity of intertemporal substitution.4

 The competitive equilibrium of the economy can be characterized in recursive form in 

terms of a decision rule for bonds at date t+1, b′(b,ε), as a deterministic function of date-t 

assets b and the date-t realization of income ε, that solves the following Bellman equation: 

1

( , ) max exp( ( )) [ ( , )]

1

subject to

b

c

V b v c EV b

c y b bR A

γ

ε ε

γ

ε

−

′

   

= + −   ′ ′

    −

= − + + ′

 (4) 

In the quantitative analysis that follows, we solve this Bellman equation using value 

function iteration. Foreign assets take values defined over a discrete grid with n nodes: 

( , ) ... b b B b b b ′ ∈ = < < < { 1 2 n

} . We set n = 1000 to reduce numerical approximation error in 

the decision rule. The Markov process of income is defined by a vector of j realizations, 

ε ε ε ε ∈ Ε = < < < { 1 2 ... j } and an jxj transition probability matrix, 1( | ) π ε ε t t + . We use 

Tauchen and Hussey’s (1991) quadrature algorithm (THQA) to transform time-series 

processes of income derived from actual data into Markov processes for model simulations. 

 

2.3 Calibration 

 The baseline calibration of the model is designed so that the deterministic stationary 

equilibrium using UE preferences matches a set of statistics from the Mexican economy, 

including the ratio of net foreign assets to GDP. The calibration to Mexico is not critical for 

our key findings. As we discuss later, the results of sensitivity analysis show that our 

findings are robust to changes in parameters and in the variability and persistence of output 

in the range of those observed in the countries listed in Table 1. 

 The BAH setup does not have a well-defined deterministic stationary equilibrium, 

because without uncertainty, βR < 1 implies that consumption falls at a gross rate of 

( )

1/ Rγ

β until the economy hits the debt limit φ. Hence, to complete the calibration of the 

BAH setup we keep all the parameters as in the UE setup and set φ and β to values such 

that the model with BAH preferences matches the long-run average of foreign assets and the 

cyclical standard deviation of consumption in the data. 

 The baseline calibration parameters are listed in Table 5. The coefficient of relative risk 

aversion is set to γ = 2, which is the standard value in quantitative dynamic general 

equilibrium models. The mean of income is normalized to y = 1 without loss of generality. 

Hence, the steady-state allocations can be interpreted as ratios relative to average GDP. The 

 

consumption also remain finite. In contrast, with β(1 ) 1 + ≥ r , assets diverge to infinity in the long 

run because marginal utility converges to zero almost surely, and with CRRA preferences this implies 

that consumption, and hence assets, diverge to infinity. 

4

 Foreign assets converge to a well-defined long-run distribution because the rate of time preference 

increases (decreases) relative to the interest rate if consumption and assets rise (fall) too much in the 

long run, and this changes incentives for savings in favor of reducing (increasing) asset holdings. 
-7-

steady-state ratio of net foreign assets to GDP is set to -44 percent (b = -.44), which is the 

average of Mexico’s net foreign assets-GDP ratio over the period 1985 to 2004 in the 

database constructed by Lane and Milesi-Ferretti (2006). The consumption-GDP ratio is set 

to 69.2 percent (c = 0.692), in line with the average consumption-output ratio in Mexican 

data. The real interest rate is set to 5.9 percent (R = 1.059), which is the average of Uribe 

and Yue’s (2006) real interest rate including the EMBI spread for Mexico. The model does 

not take into account default risk, but because the real interest rate is constant, it seems 

more reasonable to set it at a constant representative of the effective financing cost of 

Mexico’s foreign debt than to set it equal to the real interest rate on U.S. T-Bills. Given the 

values of y, c, b and R, the resource constraint implies that A = y+b(R-1)-c = 0.282. 

 In the UE setup, the value of the time preference elasticity follows from the steady-state 

condition that sets the rate of time preference equal to R: ln( )/ln(1 ) 0.109 UE ρ = + = R c . This 

implies a subjective discount factor of 0.109 (1 ) 0.944 c

− + = . In the BAH setup, we match 

Mexico’s average net foreign assets of -44 percent and the standard deviation of consumption 

over the business cycle (3.28 percent) by setting φ = -0.51 and β = 0.94, which implies 

0.064 BAH ρ = . Notice that in theory, for any given φ <-0.44, there is a value of β high 

enough so that the model with BAH preferences yields an average of assets of -44 percent. 

However, we found that for φ <-1, the values of β that can yield this mean of assets result 

in stochastic steady states that assign non-trivial probabilities to very high debt ratios larger 

than 100 percent of GDP, and the variability of consumption exceeds the actual measure by 

large margins. The Lane-Milesi Ferretti database shows that emerging economies (defined as 

middle income developing countries) very rarely reach net foreign asset positions larger than 

GDP. On the other hand, with tight ad-hoc debt limits of 50 percent of GDP or less, the 

long-run distribution of assets predicts that the economy spends most of the time at the 

debt limit (i.e., the long-run probability of observing b = φ is “too high”). 

 

 The Markov process of income shocks is set to match the standard deviation and firstorder autocorrelation of the Hodrick-Prescott-filtered cyclical component of GDP in annual 

Mexican data for the 1965-2005 period ( σy = 3.301% and ρy = 0.597 respectively). The HP 

filter ensures that cyclical GDP follows a stationary process, and the AR(1) specification 

(y y e t y t t = + ρ −1 ) cannot be rejected. Thus, the underlying standard deviation of output 

innovations is 2 2 σ σ ρ e y y = − = (1 ) 2.648 percent. Using 5 nodes in the vector of realizations, 

THQA produces a Markov process for ε that yields 3.285 percent standard deviation in 

output with 0.550 autocorrelation and 2.64 percent standard deviation in output 

innovations. Hence, the Markov process is an accurate approximation to the actual timeseries process of Mexico’s cyclical GDP. 

 Before reviewing the quantitative findings, it is important to explain how precautionary 

savings are measured. Precautionary savings are defined as the savings that agents 

accumulate due to the presence of noninsurable idiosyncratic risk. Hence, precautionary 

savings are usually measured as the difference between the long-run average of assets 

predicted by a model and the level of assets that the same model would predict in the long 

run in the absence of uncertainty. In the BAH setup, this is the excess of the average assets 

in the stochastic steady state relative to the debt limit φ, because without uncertainty the 

BAH economy reduces its asset position until it hits φ. In contrast, precautionary savings in 

the UE setup is the excess of the long-run average of assets relative to a well-defined 
-8-

deterministic steady state obtained by equating the endogenous rate of time preference with 

the world interest rate. Because of this difference in the deterministic steady state of assets 

in the BAH and UE setups, it can also be informative to study changes in precautionary 

asset holdings by simply comparing long-run averages of foreign assets. 

2.4 Baseline Results 

 Table 6 lists the statistical moments that characterize the stochastic steady state of the 

model under the baseline calibration for UE and BAH preferences. The table also shows 

results for alternative calibrations with higher output autocorrelation (ρy = 0.7), higher and 

lower variability in output innovations (σe = 4 percent and σe = 2 percent, which yield σy = 

5 percent and σy = 2.5 percent respectively) and higher risk aversion (γ = 5). 

 The business cycle moments listed in the Baseline column of Table 6 are standard 

findings in intertemporal models of small open, endowment economies with incomplete asset 

markets. Consumption behavior is consistent with typical business cycle features: 

consumption is slightly less volatile than output, and it displays positive correlation with 

GDP and positive serial autocorrelation. However, because precautionary asset demand with 

the aim of smoothing consumption is the main driving force of foreign asset dynamics in the 

model, the cyclical behavior of net exports and the current account is counterfactual. In 

particular, both external accounts are strongly positively correlated with output, while 

actual business cycles display countercyclical external accounts. We show in Section 3 that 

this result is reversed in the two-sector model with production. 

 The main result in Table 6 relates to the stock of precautionary savings. The Baseline 

results show that under UE preferences, precautionary savings measure nearly 2.5 percent of 

GDP. Under BAH preferences, however, precautionary savings are nearly 5 times larger at 

9.6 percent of GDP. The Baseline business cycle moments under both preferences 

specifications show important differences as a result. In particular, foreign assets fluctuate 

significantly more, are less correlated with output, and display higher serial autocorrelation 

in the UE setup than in the BAH setup. This result occurs because the BAH setup matches 

Mexico’s average net foreign assets-GDP ratio in the data by imposing an ad-hoc debt limit 

of 51 percent of GDP and the probability of hitting that limit in the long run is 10.2 

percent. Notice also that these significant differences in the characteristics of the stochastic 

steady states of the two setups are obtained with small differences in the behavior of the 

subjective discount factors. On average, the discount factors of the BAH and UE setup are 

virtually identical, and in the UE setup, the standard deviation of the endogenous discount 

factor is very small, at about 4 percent of the variability of output. The endogenous discount 

factor, however, is negatively correlated with GDP (because consumption is procyclical) and 

its fluctuations are highly persistent. 

 Table 6 shows that changes in the variability and persistence of output and in the 

degree of risk aversion preserve the qualitative features of the comparison across the BAH 

and UE Baseline results with small quantitative changes. Increases in ρy

, σe and γ produce 

significant increases in precautionary savings, but the BAH setup always produces a larger 

stock of precautionary savings than the UE specification. With the autocorrelation of output 

at 0.7 or the standard deviation of output at 5 percent, precautionary savings in the BAH 

setup are about 4 times larger than in the UE setup, but the factor is just a little above 2 

with the coefficient of relative risk aversion is set at 5. The high-risk-aversion case is also the 
-9-

one that yields the largest precautionary savings in both setups (10.4 and 23.8 percent of 

GDP in the UE and BAH specifications, respectively). Note also that both the UE and the 

BAH setups can generate outcomes in which consumption variability exceeds income 

variability (by about 8 percent in the simulations when ρy

 = 0.7 and up to 25 percent in the 

UE scenario with γ=5). Kose, Prasad and Terrones (2003) identified consumption variability 

in excess of income variability as a puzzling feature of the data of emerging economies (see 

also Table 2). Our results suggest that self-insurance under incomplete asset markets may 

help explain this puzzle. 

 The high serial autocorrelation coefficients of foreign assets reported in Table 6 (0.99 in 

the UE baseline, 0.959 in the BAH baseline) indicate that the adjustment of foreign assets to 

its long-run average in the stochastic steady state is a slow, gradual process under both 

preference specifications. Figures 4 and 5 illustrate further this slow convergence. Figure 4 

shows the transitional dynamics of the cumulative distribution function (CDF) of foreign 

assets in the BAH and UE Baseline simulations, starting from the lowest asset position with 

nontrivial probability in the stochastic steady state. The intuition is that as the economy’s 

business cycle evolves, this is the lowest asset position that can be reached with positive 

probability in the long run. Once the economy hits this lowest asset position, the plots show 

the evolution of the CDFs of foreign assets as the economy returns to the stochastic steady 

state. The plots show CDFs after 2, 5, 10, and 15 years as well as the long-run CDFs. 

Clearly, even after 15 years the CDFs are still distant from the long-run distributions. 

 Figure 5 shows the transitional dynamics of the foreign assets-GDP ratio in the Baseline 

simulations, plotted in percent relative to long-run averages. The transitional dynamics are 

computed as the forecast functions of the equilibrium Markov process of the foreign assetsGDP ratio conditional on initial conditions for which: (a) foreign assets take the lowest 

value with positive long-run probability; and (b) the income shock is neutral (i.e., ε = 1). 

The plots show that convergence to the long-run average of assets takes about 40 years in 

the BAH setup and more than 80 years in the UE setup. Note, however, that whereas the 

initial condition for the BAH plot is a foreign assets-GDP ratio nearly 10 percentage points 

below the long-run average, with a long-run probability of 10.2 percent (which corresponds 

to the debt limit, φ), the initial condition for the UE plot is a ratio nearly 48 percentage 

points below the long-run average and with a long-run probability of only 0.1 percent. The 

two initial conditions defined by the criterion of having the “lowest positive long-run 

probability” are therefore different in the UE and BAH setups. Hence, Figures 4 and 5, 

therefore, show that convergence is slow in both models, but comparisons across the BAH 

and UE plots need to keep in mind this caveat. 

2.5 Self-Insurance and Business Cycle Volatility 

 How much do changes in the cyclical variability of output affect foreign asset positions 

in the long run via self-insurance motives? Figure 6a shows the increase in precautionary 

savings as σe

 rises so that the standard deviation of GDP rises from 1 to 8 percent (keeping 

the serial autocorrelation of GDP constant). Figure 6b is a similar plot but for increases in 

the autocorrelation of GDP from 0 to 0.8. In this case we keep σe constant but the standard 

deviation of GDP still increases as its autocorrelation rises (since 2 2 2 /(1 ) σ σ ρ y y = − ε ). 
-10-

 Figure 6a shows that increases in output variability produce large increases in 

precautionary demand for foreign assets regardless of the preferences specification (although 

the BAH setup always yields higher precautionary savings than the UE setup). Figure 6b 

shows similar qualitative results when the autocorrelation of GDP rises, but quantitatively 

the effects on precautionary savings are weaker. If we examine the long-run averages of 

foreign asset-GDP ratios instead of precautionary savings, the UE setup produces larger 

(smaller) mean asset positions than the BAH setup at lower (higher) levels of output 

variability, but the elasticity of the average assets-GDP ratio to changes in the variability 

and persistence of output is higher with BAH preferences than with UE preferences. 

 Unfortunately, the data cast serious doubt on the hypothesis that foreign reserves have 

increased sharply in Sudden Stop countries because of increased output variability (see 

Table 2 and Figures 2 and 3). Figure 2 shows that output volatility is lower in the postglobalization period in more than half of the Sudden Stop countries. Figure 3 shows that the 

mean and median standard deviation of output in 20-year rolling windows have changed 

slightly in the 3 to 4.5 percent range, and in fact, that they have been in a steady decline 

since the late 1990s. Table 2 shows that the median standard deviation of GDP in Sudden 

Stop countries for the full 1965 to 2005 period is about 3.5 percent, and this volatility 

measure fell from 3.5 percent before the Globalization period (1965 to 1985) to 3.1 percent in 

the Globalization period (1986 to 2005). Hence, in several countries volatility fell rather than 

increased, and in this case the model predicts a reduction in foreign assets. Even for the 

subset of countries where volatility rose, only in the extreme cases of Peru and Thailand 

(which show the largest increases in volatility of about 3.5 percentage points before and after 

Globalization) we find evidence of volatility increases of the magnitude that can account for 

the observed increases in reserves reported in Table 1. Figure 6b indicates that the model 

with UE (BAH) preferences needs an increase in output volatility of more than 4 (1.5) 

percentage points to account for the observed surge in reserves as a result of self insurance. 

 We conclude from this analysis that although increases in the cyclical volatility of 

output can produce large increases in foreign asset positions, the evidence in the data does 

not show that output variability has increased sharply and systematically across Sudden 

Stop countries. Hence, higher long-run business cycle volatility does not seem a plausible 

explanation of the surge in reserves in these countries. 

2.6 Financial Globalization Effects on Foreign Assets 

 We study next the effects of financial globalization on foreign asset holdings and 

precautionary savings. To that end, we introduce into the model a time-invariant 

distortionary tax on foreign asset returns at rate τ that is intended to represent the 

combined effect of all forms of controls on capital account transactions and global asset 

trading costs proportional to asset returns. The revenue or outlays generated by this 

“effective tax” (depending on whether b is positive or negative) are rebated to agents as a 

lump sum transfer Tt = btrτ, but agents take Tt

 as given. Thus, the agents’ budget 

constraint is now [ ] 1 1 (1 ) c y b b r T A t t t t t = − + + − + + ε τ + . The resource constraint of the 

economy remains as in Eq. (2). 

 For the economy with BAH preferences, we can still write the recursive competitive 

equilibrium as the solution of a single Bellman equation by taking advantage of the fact that 
-11-

the equilibrium of the economy distorted by τ is equivalent to that of an economy without 

distortions but with a lower discount factor. In particular, if we define τ

R

 as the tax on R

equivalent to the corresponding tax on r (i.e., the value of τ

R that satisfies 

(1 ) 1 (1 ) R R r − = + − τ τ , which is /

R

τ τ = r R ), the competitive equilibrium is given by the 

same Bellman equation as in (4) but with the discount factor set as exp( ( ))(1 ) R − − v c τ . This 

equivalence does not hold for the UE setup because of the endogenous discount factor. In 

this case, however, we approximate the recursive competitive equilibrium by solving Eq. (4) 

subject to the following budget constraint: c y b b r r b A = − + + − + + ε τ τ ′ [1 (1 )] . This 

formulation uses a time-invariant lump sum transfer set at r bτ , where b is the long-run 

average of foreign assets. This guarantees that at the average of the stochastic steady state 

the solution of the Bellman equation for the model with UE preferences satisfies the 

optimality conditions of the competitive equilibrium, and for values of b other than b it 

reduces the size of the wealth effects that would be introduced by ignoring the lump-sum 

rebate and simply using the resource constraint as in (2). We checked the accuracy of this 

approximation by applying it to the model with BAH preferences and then comparing the 

results with the exact competitive equilibrium solutions obtained using the tax-adjusted 

discount factor. For values of τ ≤ 0.84 the two algorithms yield average asset-GDP ratios 

that are at most 1/10 of a percent apart. 

 We consider tax rates that range from 0 to 27 percent, and we adjust the calibration so 

that at a zero tax rate the BAH setup approaches the complete-markets equilibrium (i.e., we 

set β = 1/R, so that the domestic rate of time preference matches the world interest rate). 

Hence, in this case the lack of financial integration (which implies nonzero values of τ) 

represents also the “severity” of market incompleteness, as reflected in the gap between the 

tax-adjusted rate of time preference and the world real interest rate. 

 Figure 7a and 7b plot the long-run averages of foreign assets and precautionary savings 

against the tax on capital flows under both BAH and UE preferences. For the BAH setup, 

we show curves for three values of φ: the natural debt limit, φ = -2, and φ = -1 (which 

imply limits of -200 and -100 percent of average GDP respectively). As the tax approaches 

zero, b and precautionary savings go to infinity because we approach βR(1-τ

k

)=1 and agents 

desire an infinite amount of self-insurance. Conversely, at high tax rates the relationship 

between taxes and mean asset holdings vanishes as the economy spends most of the time at 

the debt limit. This occurs at tax rates in excess of 10 percent for all three scenarios of φ. In 

contrast, at tax rates between 0.5 and 10 percent, the BAH setup predicts that the long-run 

average b/y ratio increases sharply as the tax falls even by small amounts. Thus, this setup 

predicts that in the early stages of financial globalization, foreign assets may respond little 

to the opening of the capital account, while later on, further financial integration efforts that 

may imply small changes in effective distortions on asset trading (small changes in τ) 

produce large changes in the long-run average of b/y. These effects are the strongest if the 

only limit on debt is the natural debt limit. In this case, a cut in τ from 8 percent to 0.5 

percent increases the mean b/y ratio from -10 times GDP to about -154 percent of GDP, 

and precautionary savings rise from 81 percent to about 9.4 times GDP. But the effects are 

still large when debt limits are much tighter. With φ = -1, the same tax cut increases 

average b/y from -90 percent to a positive position of about 20 percent of GDP, and 

precautionary savings rise from 10 to 120 percent of GDP. 
-12-

 The effects of financial globalization using UE preferences also show large increases in 

the long-run average of the ratio of foreign assets to GDP. In this case, cutting τ from 8 to 

0.5 percent increases the long-run average of foreign assets from -156 percent of GDP to 

almost -45 percent of GDP. Conversely, precautionary savings are approximately unchanged. 

This result highlights a key difference between the BAH and UE preference specifications: 

when τ changes, the UE setup separates the savings effect resulting from the increase in the 

post-tax return on assets even without uncertainty (i.e., the deterministic steady state of b

rises as τ falls because the return to savings rises and the rate of time preference adjusts 

accordingly), from the effect due solely to precautionary savings (i.e., the effect on the excess 

of mean foreign asset holding in the stochastic model relative to the deterministic steady 

state). In the BAH setup the two effects cannot be separated because the deterministic 

steady state is invariant to the tax (without uncertainty assets fall until they hit φ for any 

τ>0). Thus, although the model predicts large increases in foreign assets as a result of 

financial globalization under both BAH and UE preferences, the results of the UE setup 

suggest that the fraction that can be attributed to precautionary savings per se could be 

small. 

 Two important additional observations should be noted about these results. First, 

whether financial globalization is a good explanation of the observed surge in reserves in 

Sudden Stop economies depends on the timing and magnitude of the removal of barriers to 

international asset trading (i.e., how high τ was when globalization started and how much 

has it fallen). Certainly, the results in Figures 7a-b indicate that if Sudden Stop economies 

have moved closer to a regime of unrestricted global asset trading (as the indicators in 

Figure 1 suggest), the expected increases in foreign assets can be easily as large as those 

observed in the data. Second, the results also depend on the degree of completeness of the 

global capital markets that can be accessed with financial globalization. The exercise in this 

Section assumes that in the BAH setting, τ = 0 implies that those global capital markets 

provide perfect insurance. In contrast, the results under the UE specification maintain 

market incompleteness even when τ = 0. The fact that both specifications predict large 

increases in foreign assets as financial globalization strengthens suggests that the strong 

assumption of market completeness at τ = 0 in the BAH setup is not critical for the result. 

The large differences in precautionary savings across the BAH and UE setups, however, do 

hinge on this assumption. 

 The large increase in the long-run average of foreign assets as a result of financial 

integration, and the slow transitional dynamics of foreign assets documented earlier, echo 

the results of Mendoza, Quadrini, and Rios-Rull (2007). The results, however, are driven by 

different mechanisms in the two studies. Mendoza et al. obtain their results because of the 

adjustments in the within-country and cross-country distributions of wealth of two countries 

with different degrees of market incompleteness. As the countries move from financial 

autarky to financial globalization, the risk-free interest rate rises relative to autarky for the 

less financially developed country, and this rise alters precautionary savings behavior across 

individuals within the country leading to an increase in aggregate net foreign assets. 

Wealthy agents that save reap the gains of this increase, while poor agents that borrow are 

made significantly worse off. There is no aggregate risk but their model is calibrated to the 

observed variability of household earnings and firm profits, which is substantially higher 
-13-

than total GDP variability. In contrast, our results focus only on aggregate uncertainty and 

the savings response of a single representative agent. 

 In summary, we find that financial globalization can be an important force driving the 

sharp increase in foreign reserves in Sudden Stop economies (and in emerging economies in 

general). The role of “pure” precautionary savings, however, is not as clear because we can 

obtain results in the setup with UE preferences when the large increase in foreign assets is 

the result of the increased incentives for saving in foreign assets even in the absence of 

uncertainty. 

3. Two-Sector Production Economy

3.1 Structure of the Model

 The two-sector model differs from the one-sector model in four key respects: 

(I) Consumption includes tradable goods (c

T

) and nontradable goods (c

N

) with aggregate 

consumption defined by a constant-elasticity-of-substitution (CES) function: 

( ) ( )

1

( , ) (1 ) , 0, 1. T N T N c c c a c a c a t t t t

μ μ

μ μ

− − −

= + − > ≥ −     (5) 

The elasticity of substitution between tradables and nontradables is given by 1/(1+μ), and 

the CES weighting factor is given by a. 

 

(II) Nontradable goods are produced by a representative firm using imported intermediate 

goods (m) as the single variable input of a neoclassical production technology: 

, 0 1. Ny z Zm t t t

α = ≤ ≤ α (6) 

Z represents the trend level of total factor productivity (TFP) and it includes the effects of 

any fixed factors: zt is a stochastic TFP shock, and α is the share of imported inputs in 

gross output. The domestic market of nontradable goods and the world market of 

intermediate goods are competitive, and thus the profit-maximizing demand for imported 

inputs is given by a standard marginal productivity rule: 

N m 1

p z Zm p t t t

α α− = (7) 

In this expression, p

m

, which is kept constant for simplicity, represents the world-determined 

price of imported inputs relative to tradable consumer goods and N

pt denotes the price of 

nontradables relative to tradables, which is determined inside the small open economy. At 

equilibrium, this price must also match the household’s marginal rate of substitution 

between tradables and nontradables: 

( )

1

1

T

N t

t N

t

a c

p

a c

+μ

−  

=  

 

   

 (8) 

(III) The economy has new budget and resource constraints. The budget constraint of 

households in the competitive equilibrium is: 
-14-

1

T N N T T T N N N c p c y A p A b b R t t t t t t t t + = + + + − + ε π + (9) 

where N N N m πt t t t = − p y p m represents profits from firms in the nontradables sector. The 

technology in (6) has decreasing returns, and profits are positive and equal to 1-α of the 

value of nontradables output. These profits are also equal to the value of nontradables GDP 

in units of tradables (i.e., gross output minus the cost of intermediate goods). The 

endowment of tradables is stochastic, so the economy now faces two Markov shocks, one 

hitting tradables output and one hitting TFP in nontradables. Given the firm’s optimality 

condition (7), the definition of profits, and market clearing in the nontradables sector, it 

follows that the sectoral resource constraints are as follows: 

( ) 1 1

T T T T m c y b b r A p m t t t t t = − + + + − ε + (10) 

N N N c y A t t = + (11) 

Constraint (10) assumes that foreign assets are denominated in units of tradables, so this 

economy displays liability dollarization (when it borrows, its debt is in units of tradables). 

(IV) We consider the possibility that agents face a collateral constraint in asset markets. In 

particular, lenders limit credit to a fraction κ of the market value of the total income of 

domestic agents in units of tradables, and up to a maximum Ω:

5

 

1

T T N b y t t t + ≥ − + ≥ Ω κ ε π     (12) 

The value of income in the right-hand-side of (12) is equal at equilibrium to the economy’s 

total GDP valued at tradables goods prices, so (12) can be viewed as a constraint on the 

economy’s debt-to-GDP ratio. The constraint is endogenous, however, because the bond 

position and the price and production of nontradables are endogenous, and there is feedback 

between borrowing decisions and the value of nontradables GDP (as explained below). 

 Note that agents take as given the price of nontradables. Hence, they do not internalize 

the effect of their consumption and bond decisions on the equilibrium price, and therefore 

the effects of changes in the equilibrium price and production of nontradables on the ability 

to borrow are also not internalized. As a result, the model features a credit-market 

externality by which individual choices affect the economy’s ability to borrow (see Uribe, 

2006 and Korinek, 2007). 

3.2 Equilibrium and Amplification with Debt-Deflation 

 The characteristics of the equilibrium of the one-sector economy described in 2.2 extend 

to the two-sector economy. In particular, the CES aggregator preserves the Inada condition 

that makes period marginal utility go to infinity as c

T

 or c

N

 go to zero from above. Hence, 

agents will self-insure to rule out stochastic intertemporal sequences that could expose them 

 

5

 This formula rules out equilibria in which the constraint could be satisfied at very high debt levels

that prop up c

T

 and p

N

. For example, Ω could be set at the debt that would be contracted without 

credit constraints (i.e., creditors do not lend more than what they would have without the 

constraint). 
-15-

to the risk of “very low” consumption of tradables. The natural debt limit that guarantees 

this outcome is 1 min( )/ T T T m b y A p m r t t t + ≥ − + − ε .

6

 Because tradables income is only a 

fraction of total income, this natural debt limit is tighter than the one in the one-sector 

economy. The agents may also face a higher degree of market incompleteness inasmuch as 

there is no vehicle to self-insure directly against the risk of fluctuations in nontradables 

income. Endogenous changes in the price and output of nontradables can, however, provide 

implicit insurance depending on how substitutable are tradables and nontradables in 

consumption (i.e., on the value of μ) and on the equilibrium correlation between 

nontradables and tradables income (both valued at tradables goods prices). 

 The collateral constraint (12) introduces a borrowing limit that is “occasionally 

binding.” As Mendoza (2005, 2006a) explains, in the states in which this constraint binds, 

the Euler equation for tradables consumption includes an endogenous Lagrange multiplier ηt

that represents the shadow value of the credit limit: 

T ( ) exp[ ( ( , ))] [ ( 1) ] T

T N U t v C c c E U t R c t t t t c = − + + η (13) 

If we define the effective interest rate as 1

T ( )/ exp[ ( ( , ))] [ ( 1)] ( T )

T N R U t v C c c E U t t t t t + ≡ − + c c

ɶ , it 

follows that the binding credit constraint adds an interest rate premium equal to 

[ ] / exp[ ( ( , ))] [ ( 1)] 1 ( T )

T N E R R v C c c E U t t t t t t t c + − = − + η

ɶ . This premium can be interpreted as the 

premium that lenders would charge borrowers to ensure that the credit constraint is not 

violated. In turn, the justification for the constraint could be limited contract enforcement 

by which lenders can confiscate only κ of a defaulting borrower’s total income. 

 Suppose that the economy has just enough debt that an adverse shock of standard 

magnitude triggers the credit constraint. Tradables consumption falls because agents cannot 

borrow as much as they wanted. In turn, at the “initial” level of nontradables output, the 

decline in tradables consumption makes the price of nontradables fall. But as this price falls, 

so does the value of the marginal product of imported inputs, and hence demand for these 

inputs and the output of nontradables fall. Up to this point, the credit transmission 

mechanism is similar to the one widely studied in Sudden Stop models with balance sheet 

effects (e.g., Calvo, 1998). If the price and output of nontradables fall, however, the value of 

the total income in units of tradables falls, tightening the constraint further and setting in 

motion Irving Fisher’s (1933) classic debt-deflation amplification mechanism.7

 The possibility of endogenous Sudden Stops strengthens precautionary savings 

incentives.8 Sudden Stops and the probability of Sudden Stops are endogenous, so agents 

make optimal self-insurance plans taking into account the endogenous link between choices 

 

6

 Note that agents take profits and the price of nontradables as given, so their individual natural debt 

limit is 1 min( )/ T T T N N N b y A p A r t t t t + ≥ − + + + ε π . With this debt, limit there could still be 

equilibrium sequences where tradables consumption is nonpositive. The resource constraint (10) 

implies that at equilibrium, the natural debt limit must be 1 min( )/ . T T T m b y A p m r t t t + ≥ − + − ε

7

 Mendoza (2005) provides an illustrative deterministic example of this mechanism. He shows that the 

Fisherian deflation converges to a unique equilibrium that amplifies balance sheet effects, resulting in 

much larger adjustments in debt, consumption, and the nontradables price than a setup with balance 

sheet effects but no Fisherian deflation. 

8

 Aizenman and Lee (2007) derive an analytical result with similar features in a Diamond-Dybvigstyle model. They show that a discrete liquidity shock increases the need for precautionary savings. 
-16-

of foreign asset holdings and the magnitude and likelihood of Sudden Stops. As a result, the 

economy builds a buffer stock of savings so as to minimize the risk of landing in states of 

nature in which debt is high enough for a Sudden Stop to trigger massive consumption 

collapses. Hence, we should expect the economy with Sudden Stops to converge to a higher 

average of foreign assets than without Sudden Stops. In the quantitative analysis that 

follows, we explore the differences between the two environments. 

 The equilibrium of the two-sector economy in recursive form is characterized by a 

decision rule for t+1 foreign assets, b′(b, ε

T

, z), as a deterministic function of date-t assets 

and the date-t realization of shocks, that solves the following Bellman equation: 

( ) ( )

( ) ( )

1 1

,

1

(1 )

( , , ) max 1

exp (1 ) [ ( , , )]

subject to 

T N

t t

T

b m

T N T

t t

T T T T m

a c a c

V b z

v a c a c EV b z

c y b bR A p m

γ

μ μ

μ

μ μ

μ

ε γ

ε

ε

−

− − −

′

− − −

   

   

   + − 

     

 

   + 

  =  − 

 

 

    

    

 − + −     ′ ′ ′        

   

= − + + − ′

 

 (1 )

N N

T T N

c zZm A

b y p zZm

α

α κ ε α

= +

′ ≥ − + −    

 (14) 

As in the case of the one-sector model, we solve this Bellman equation for both the BAH 

and UE discount factors using value function iteration.9 The grid of bond values B has 1000 

evenly spaced nodes. The Markov process of shocks includes shocks to tradables GDP, which 

are in the set { 1 2 ... }

T T T T ε ε ε ε ∈ < < < j

 with j realizations, and shocks to nontradables TFP, 

which are in the set z z z z ∈ < < < { 1 2 ... h } with h realizations. Thus, the transition 

probability matrix of these shocks, (( , ) | ( , ) 1 1 )

T T π ε ε t t t t + + z z , has dimensions (jxh)x(jxh). 

 

 When the model is solved assuming a frictionless credit market (i.e., without the credit 

constraint), the equilibrium relative price of nontradables is determined after the Bellman 

equation has been solved. In particular, the policy function b′(b,ε

T

,z) and the resource 

constraints can be used to determine the equilibrium consumption allocations of tradables 

and nontradables, and the equilibrium price of nontradables follows from Eq. (8). 

 The case with the credit constraint is more complicated because the equilibrium price of 

nontradables needs to be solved for together with the Bellman equation. The algorithm we 

use evaluates each candidate choice b B ′ ∈ for each triple in (b,ε

T

,z) in the state space. A 

first pass assumes that the constraint does not bind, takes a particular set of values 

(b′,b,ε

T

,z) and computes the corresponding c

T

, c

N

, m, and p

N

 that satisfy jointly the resource 

constraints and conditions (7)-(8). If the credit constraint does not bind, these solutions are 

 

9

 It is straightforward, but lengthy, to show that the solution to the Bellman equation (14) 

corresponds to the competitive equilibrium. In particular, the solution satisfies the Euler equation 

(13), the resource constraints (10) and (11), the optimality conditions for demand of imported inputs 

(Eq. (7)) and sectoral consumption allocations (Eq. (8)). Note also that, when the credit constraint 

binds, the algorithm uses a pricing function for nontradables that does not depend on b′ . 
-17-

passed on to the Bellman equation’s maximization step, which searches for the valuemaximizing choice of b′ in B. If the constraint binds, we take the corresponding “current” 

state triple (b,ε

T

,z) and solve for b′, c

T

, c

N

, m and p

N

 that satisfy jointly the resource 

constraints, conditions (7)-(8) and the credit constraint with equality. Note that this 

nonlinear simultaneous equation system yields, for a given triple (b,ε

T

,z) and all b B ′ ∈ for 

which the collateral constraint is violated in the first pass, the same constrained value of b′ . 

 

3.3 Calibration

 The baseline calibration parameters for the two-sector economy are listed in Table 5. 

Mendoza (2002) calibrated a model with tradables and nontradables for Mexico using 

sectoral data, and our calibration follows closely his. We keep the values γ = 2, b = -0.44 

and R = 1.059 from the calibration of the one-sector model. The steady-state relative price 

of nontradables, the world price of intermediate goods and total GDP in units of tradables 

are normalized to p

N = 1, pm = 1 and yT

 +pNy

N

-p

mm = 1. Hence, the steady-state allocations 

can be interpreted as ratios relative to total GDP in units of tradables. We use the same 

elasticity-of-substitution parameter as Mendoza’s, μ=0.316, which corresponds to an 

estimate for Mexico obtained by Ostry and Reinhart (1992). We also set several steady-state 

sectoral consumption and production ratios to match Mendoza’s averages obtained from 

Mexican data at current prices. The ratio of nontradables-to-tradables GDP is (p

N

y

N

-

p

mm)/yT = 1.543, which given pN = 1, pm = 1 and yT+pN

y

N = 1 implies that yT = 1/(1+1.543) 

= 0.393 and (p

N

y

N

-p

mm) = 1.543/(1+1.543) = 0.607. 

 The share of imported input costs to gross output of nontradables is α = 0.2. In the 

deterministic steady state, this factor share yields a ratio of imported inputs to total GDP of 

13 percent, which matches the ratio for Mexico reported in Mendoza (2006b). With α = 0.2 

and the GDP of nontradables calculated above, gross output of nontradables (i.e., 

nontradables GDP plus imported inputs) is equal to 0.71/(1-α) = 0.758. It follows then that 

the trend level of TFP in nontradables is equal to Z = 0.7581-α/α

α=1.106 and then condition 

(7) implies that the steady-state level of imported inputs is m = (αZ)1/(1-α) = 0.152. 

 Mendoza’s (2002) estimates of the sectoral consumption-GDP ratios in Mexican data 

are c

T

/y

T=0.665 and pN

c

N

/(p

N

y

N

-p

mm) = 0.71. Given these ratios and the values of tradables 

and nontradables GDP, it follows that c

T = 0.261 and cN = 0.431. The value of the 

coefficient a in the CES aggregator is set so that p

N = 1 given the values of μ, cT

, and c

N

. 

This requires a = 0.341. As in the one-sector model, we introduce constant lump-sum levels 

of exogenous absorption A

T

and A

N

 to make the model consistent with the observed 

expenditure shares of investment and government expenditures. In addition, these transfers 

redistribute income across sectors so that the private sectoral consumption shares match 

those in the data. Hence, A

T

 is set so that the calibrated values of y

T

, c

T

, m, b and R are 

consistent with the steady-state resource constraint for tradables: AT = y

T+br-m-cT = -0.046. 

Similarly, A

N

is set so that y

N

 and c

N

 are consistent with the nontradables resource 

constraint: A

N = yN

-cN = 0.328. 

 

 The time preference elasticity in the UE setup is ln( )/ln(1 ( , )) 0.1867 UE T N ρ = + = R C c c . 

This implies the same steady-state subjective discount factor as in the one-sector model 
-18-

(0.944). In the BAH setup, we set φ =-0.7 and β=0.94395 (or 0.0594 BAH ρ = ) so as to match 

Mexico’s mean of net foreign assets and variability of aggregate consumption. 

 The data necessary to construct a reliable estimate of TFP shocks in the nontradables 

sector are not available. Hence we followed Mendoza (2006b) in using a two-stage 

“identification-by-simulation” strategy: In the first stage, we proxy TFP shocks in 

nontradables with nontradables GDP, and use THQA to construct a Markov process for ε

T

and z to approximate a VAR(1) estimated with the HP-filtered cyclical components of 

tradables and nontradables GDP from Mexican data for the 1965 to 2005 period. The 

nontradables sector is defined as services plus industry minus manufacturing. The VAR 

model is y y e t t t = ⋅ + ρ −1 where yt′ = [yt

T

yt

N

 ], ρ is a 2x2 matrix of autocorrelation 

coefficients, and et

′ = [et

T

et

N

 ] is a vector of error terms with variance-covariance matrix 

cova(e). The estimates of ρ and cova(e) are: 

1.088 * 0.564 * 0.000601 0.000472

, cova( ) 0.655 * 0.154 0.000472 0.000572 ρ e

   

= =    

−   

 (15) 

An asterisk denotes coefficients that are statistically significant at the 99 percent confidence 

level. Interestingly, the VAR results indicate that most of the persistence in the fluctuations 

of nontradables GDP follows from spillovers from tradables GDP (because the 

autocorrelation term for nontradables GDP is not significantly different from zero). 

 The unconditional standard deviations of tradables and nontradables output in the data 

are T 0.0336 yσ = and N 0.0327 yσ = , the first-order autocorrelations are T 0.575 yρ = and 

N 0.603 y

ρ = , and the correlation between the two is 

,

T N 0.772 y y ρ = . Thus cyclical 

fluctuations in the output of the two sectors have similar magnitude and persistence, and 

they are positively correlated with each other. Passing the estimates in (15) to the TQHA 

program and using three realizations for ε

T

 and z (which imply 9 (ε

T

,z) pairs) we obtain a 

Markov process that, after solving the model, produces standard deviations of 0.0301 and 

0.0246 for GDP of tradables and nontradables respectively; serial autocorrelations are 0.539 

for tradables and 0.577 for nontradables, and correlation between the two is equal to 0.791. 

Thus, using nontradables GDP as a proxy for nontradables TFP we obtained a Markov 

process that approximates well the actual cyclical behavior of each sector’s GDP, except 

that the variability of nontradables GDP (which is endogenous in the model) is 

underestimated—the model yields 2.46 percent instead of 3.27 percent in the data. 

 The second stage of the identification process adjusts the elements of ρ and cova(e) that 

involve nontradables TFP so as to produce a baseline simulation of the model that yields a 

closer approximation to the unconditional moments of tradables and nontradables GDP in 

the data (particularly the standard deviation of the latter). We leave unchanged the 

elements of ρ that were statistically significant in the results reported in (15). The closest 

approximation to the moments in the data produced the following unconditional moments: 

ˆ T 0.0334 y

σ = , ˆ N 0.0305 yσ = , ˆ T 0.587 yρ = , ˆ N 0.483 yρ = , and 

,

ˆ T N 0.516 y y ρ = . The 

identifying restrictions that produced these results imply the following VAR structure for 

tradables GDP and nontradables TFP: 
-19-

1.088 0.564 0.000601 0.00055

, cova( )

0.655 0.300 0.00055 0.0012

ρ e

   

= =    

−   

 (16) 

3.4 Baseline Results

 Table 7 reports the business cycle moments that characterize the stochastic steady state 

of the two-sector model with production under BAH and UE preferences. The table shows 

moments for foreign assets, the current account-output ratio, the price of nontradadables, 

imports of intermediate goods, and sectoral and aggregate consumption and output. 

Consumption of tradables is less volatile than tradables output, and consumption of 

nontradables, for which there is no storage technology that can be used to smooth 

consumption, is more volatile than nontradables output. Aggregate consumption in units of 

tradables is less volatile than output (also in units of tradables) with UE preferences, but 

more volatile than output with BAH preferences. Both consumption and output in units of 

tradables are more volatile with BAH preferences than with UE preferences. 

 In terms of matching key characteristics of actual business cycles, the two sector model 

improves upon the one-sector model in two important respects: First, it produces 

countercyclical fluctuations in the current account and net exports. The correlations of the 

current account-output ratio with output in units of tradables are -0.14 and -0.48 with UE 

and BAH preferences, respectively. Hence, agents in this model economy buildup debt in the 

expansion phase of the business cycle, which is an important feature of the dynamics of debt 

observed in emerging economies. Second, the model is also in line with the data in predicting 

large, procyclical, and persistent fluctuations in the relative price of nontradables (i.e., the 

real exchange rate). The coefficients of variation of the price of nontradables are 6.7 and 8.1 

percent with UE and BAH preferences respectively, more than twice the variability of 

output in units of tradables, and the correlations with output in units of tradables are 0.75 

and 0.88 respectively. The first-order autocorrelation of the price of nontradables is almost 

0.5 with both UE and BAH preferences. 

 The two-sector model is consistent with the one-sector model in predicting that 

precautionary savings are larger in the BAH setup than in the UE setup (25.3 percent of the 

long-run average of GDP in the former vs. 1.6 percent in the latter). Moreover, comparing 

across the one-sector and two-sector models, the UE setup shows that precautionary savings 

are lower in the two-sector economy (2.5 percent in the one-sector model vs. 1.6 percent in 

the two-sector model). Precautionary savings in the BAH one-sector and two-sector baseline 

economies are not easily comparable because they were calibrated with different values of φ. 

We show below, however, that when the comparison is made for common debt limits, the 

BAH setup also predicts that precautionary savings are generally smaller in the two-sector 

model than in the one-sector model. We will also show that this result is related to an 

implicit hedge provided by the equilibrium co-movement between the incomes from 

tradables and nontradables. 

3.5 Revisiting the Effects of Volatility and Financial Globalization on Foreign Assets 

 The conclusions derived from the one-sector model regarding the effects of business 

cycle volatility and financial globalization on foreign assets extend to the two-sector 

production economy. Increases in the volatility of tradables output and/or nontradables 

TFP can lead to large increases in the long-run average of foreign assets. The data, however, 
-20-

do not show evidence of systematic volatility changes in this direction in Sudden Stop 

countries (in light of this result and to save space, we do not add tables and charts showing 

the results of our numerical analysis for the effects of volatility changes in the two-sector 

model). Table 3 shows that the volatility of tradables GDP increased in about half of the 

Sudden Stop countries, but only in five of them are the increases of the size that the model 

with either BAH or UE preferences would need to predict large changes in reserves. 

Volatility in the nontradables sector has increased only in a few of the Sudden Stop 

countries, with the median showing virtually no change. Hence, we conclude again that, 

while the model predicts that increases in output volatility could explain large increases in 

foreign asset positions, there is no systematic evidence in the data showing that output 

volatility has increased nearly as much the model would require, and in several countries it 

has fallen or remained about the same (particularly in the nontradables sector). 

 Figure 8 shows the effects of financial globalization on the long-run average of foreign 

assets and precautionary savings in the two-sector model with BAH and UE preferences. We 

also include plots for the one-sector model for comparison. The two main results from the 

one-sector model are preserved: (1) financial globalization produces large increases in the 

foreign asset position, which increase at a linear rate with UE preferences, and at a sharply 

increasing rate in the BAH setup; (2) precautionary savings also rise at a fast increasing rate 

in the BAH setup while they are nearly invariant to the degree of financial globalization in 

the UE setup. 

 Comparing across one- and two-sector models we find that with BAH preferences both 

the long-run average of foreign assets and precautionary savings are uniformly smaller in the 

two-sector economy. With UE preferences, however, mean foreign assets are smaller in the 

one-sector model at levels of barriers to capital mobility equivalent to taxes of 10 percent or 

more. At lower taxes the one- and two-sector models with UE preferences predict about the 

same long-run averages of foreign assets. By contrast, precautionary savings are uniformly 

smaller in the two-sector UE setup. Despite these differences, the effects of financial 

globalization on mean foreign assets and precautionary saving remain large, particularly as 

financial globalization approaches the full removal of barriers to international asset trading. 

 The smaller precautionary savings effects in the two-sector model reflect the fact that 

the equilibrium correlation between tradables income and nontradables income valued at 

tradables goods prices is negative (at about -0.4 in both UE and BAH setups). Hence, when 

the economy suffers a negative shock to tradables GDP, the value of nontradables income 

rises, thereby, providing an implicit hedge that reduces the need for precautionary savings. 

Since y

T

 and y

N

 are positively correlated, the negative correlation between y

T

 and p

N

y

N

is 

driven by the negative correlations between the price of nontradables and the output of each 

sector. In turn, these price correlations depend on the elasticity of substitution between 

tradadables and nontradables in consumption and on the response of the supply of 

nontradable goods, which depends on technology parameters and the price of imported 

inputs. 

 In conclusion, in our two-sector model, financial globalization remains an important 

factor that could account for large increases in the foreign asset positions observed in 

Sudden Stop economies. In this case, however, the increases are less pronounced than in the 
-21-

one-sector model because the endogenous fluctuations in the price and output of notradables 

provide an implicit hedge that reduces the need for precautionary savings. 

 

3.6 Self-Insurance against Sudden Stops: How large should the war chest be? 

 We move now to quantify the changes in precautionary demand for foreign assets 

induced by the introduction of Sudden Stop risk. We consider a baseline scenario in which 

the collateral constraint limits debt not to exceed 50 percent of GDP valued in units of 

tradables (κ = 0.5). Table 7 shows the business cycle moments of the stochastic steady 

states under BAH and UE preferences. A standard finding from the debt-deflation models of 

Sudden Stops is that long-run business cycle statistics are not altered significantly by creditmarket imperfections. A comparison of the moments with and without credit constraints in 

Table 7 shows that that is the case here as well. Hence, the model’s endogenous Sudden 

Stops are infrequent (but positive probability) events nested within “normal” business 

cycles, and those business cycles are approximately the same as those that would be 

observed in the absence of credit constraints. 

 Considering the long-run average of foreign assets and precautionary savings, the longrun average of assets in the BAH (UE) setup increases from -44.7 (-42.4) percent without 

Sudden Stop risk to -24.3 (-37.8) percent when Sudden Stops are possible. Since in the UE 

setup the deterministic stationary state of foreign assets is the same with or without credit 

constraints, precautionary savings with UE preferences increase by the same amount as the 

long-run average of foreign assets: -37.8-(-42.4) = 4.6 percent. Thus, with UE preferences the 

risk of Sudden Stops leads to an increase in precautionary asset holdings of about 4.6 

percent of long-run GDP. 

 Quantifying the change in precautionary savings with BAH preferences is less obvious 

because the debt limit (which is also the debt to which the economy converges in the long 

run in the absence of uncertainty) changes from -0.7 without Sudden Stop risk to -0.5 with 

Sudden Stop risk. The long-run average of foreign assets rises 20.4 percentage points of GDP 

from one scenario to the other, but since the credit limit itself rises by 20 percentage points, 

the overall change in precautionary savings is only about 1⁄2 of a percentage point. Hence, 

relative to the corresponding credit limits, precautionary savings are about the same with or 

without Sudden Stop risk in the BAH setup. However, if we consider the buildup of 

precautionary asset holdings from the perspective of an economy where Sudden Stop risk has 

just been introduced, the model predicts that agents would need to buildup their war chest 

of foreign assets by 20.4 percentage points of GDP in the long-run. This “transitional” 

measure of the extra precautionary savings that need to be accumulated as a result of the 

structural change implied by the introduction of Sudden Stop risk is perhaps a better 

measure of the effect that Sudden Stops have on self-insurance in the BAH setup. 

 Figure 9 illustrates Sudden Stop dynamics under the two preference specifications. The 

plots illustrate the amplification and persistence of the response of the model’s endogenous 

variables to shocks of standard magnitude when the credit constraint binds. To be precise, 

the plots show the differences in percentage deviations from long-run averages in the 

economy with credit constraints relative to the economy with perfect credit markets in 

response to initial negative shocks to tradables output and nontradables TFP, and 

conditional on an initial debt ratio at which the credit constraint binds. The shocks are the 

pair (ε

T

,z) in the Markov chain that yields the closest approximation to one-standard-
-22-

deviation shocks.10 The initial foreign asset position is -48.7 percent of long-run GDP. The 

probability of reaching this debt ratio in the long run is 0.9 (1.1) percent in the BAH (UE) 

setup. 

 The foreign assets plot illustrates the dynamics of precautionary savings. Initially, both 

BAH and UE setups show negative values because the initial foreign assets (-0.487) are 

smaller than the corresponding long-run averages by a larger gap when credit constraints are 

present than when they are absent. In the UE setup the gap is about -4 percentage points of 

GDP initially, whereas in the BAH setup the gap is nearly five times larger in absolute 

value. This result reflects the smaller change in mean foreign assets relative to perfect credit 

markets in the UE setup. In terms of persistence, foreign assets take a long time to converge. 

In the BAH setup, a gap of about -4 percentage points of GDP still remains after 50 years. 

In contrast, the negative gap closes in about 5 years in the UE setup, but after that point 

the gap climbs to 1.5 percent and returns to zero very slowly. This moderate “overshooting” 

of the asset position is an implication of the endogenous rate of time preference. 

 The plots for the current account-output ratio, nontradables consumption, the price of 

nontradables, aggregate consumption, and total output in units of tradables illustrate the 

ability of the debt-deflation mechanism to produce endogenous Sudden Stops in response to 

one-standard-deviation shocks. Qualitatively, the features of the Sudden Stops are identical 

in the BAH and UE setups: a current account reversal, a collapse in the price of 

nontradables, and declines in sectoral and aggregate consumption and output, all of which 

represent amplified responses to the shocks induced by the Fisherian debt deflation. 

Quantitatively, the BAH setup produces larger Sudden Stops than the UE setup. In the 

BAH (UE) setup, the current account reversal reaches 3.5 (2.5) percentage points of GDP, 

the fall in nontradables output is about 2.5 (1.5) percent, the real depreciation is 9 (6) 

percent, the decline in aggregate consumption is 5 (3) percent and the drop in output is 7 

(5) percent. 

 The transitional dynamics in Figure 9 show that Sudden Stop economies can go through 

prolonged periods in which they buildup foreign assets, display persistent current account 

surpluses, and maintain undervalued real exchange rates. These “imbalances” gradually 

grow smaller over time. The qualitative features of this adjustment are consistent with the 

recent experience of several Sudden Stop countries, particularly in Asia. Moreover, the 

current account surplus and undervalued real exchange rate are by-products of the buildup 

of precautionary savings in the aftermath of Sudden Stops, or following financial 

globalization. They do not require intentional exchange rate management by central banks. 

 The plots in Figure 9 illustrate Sudden Stops from a particular initial condition of 

foreign assets (i.e. -48.7 percent of long-run GDP). Many other debt positions, however, also 

trigger Sudden Stops. Figure 10 shows the current account reversals and price collapses that 

occur on impact when negative one-standard-deviation ε

T

 and z shocks hit the economy at 

different initial conditions of foreign assets. For values of b ≥ -0.482, the credit constraint 

does not bind and hence there is zero amplification and no Sudden Stops. For -0.578<b <-

0.482, the constraint binds and there are Sudden Stops. This Sudden Stop region can be 

 

10 Because the discrete Markov chain is not a perfect approximation, the shocks are equivalent to 1.25 

and 1.05 times the standard deviation of tradables output and nontradables TFP, respectively. 
-23-

split into two parts. One part includes Sudden Stops that are so large (with current account 

reversals of up to 100 percentage points of GDP) that precautionary savings rules them out 

in the long run. In Figure 10, this part of the Sudden Stop region is defined by foreign asset 

positions to the left of the long-run probability borders of the UE and BAH setups (which 

are located at about the 50 percent debt ratio in both cases). The second part of the Sudden 

Stop region is on the right side of these borders. That is the “long-run Sudden Stop region,” 

where Sudden Stops with positive long-run probability occur. This region includes the case 

illustrated in Figure 9 as well as other Sudden Stop episodes that produce effects more than 

twice as large.11 The cumulative probability of all long-run Sudden Stop states is 3.88 (7.85) 

percent in the BAH (UE) setup. 

 Once the economy hits a debt position at which standard shocks trigger a Sudden Stop, 

how long does it take for precautionary accumulation of foreign assets to provide enough 

self-insurance to minimize the probability of returning to that debt position? The answer to 

this question is illustrated in Figure 11, which plots the transitional CDFs of foreign assets 

for the BAH and UE setups starting from the initial asset position that generated the 

Sudden Stop shown in Figure 9 (-48.7 percent of GDP). The probability of reaching this 

debt ratio is minimized when the economy reaches the stochastic steady state (as noted 

before, the long-run probabilities of b = -0.487 are 1.1 and 0.9 percent in the UE and BAH 

setups respectively). The CDF plots for the BAH and UE setups show that this process 

takes more than 15 years. After two years, the probability of hitting again the -48.7 percent 

debt ratio is 21 percent in the UE setup and 40 percent in the BAH setup. After 15 years, 

the probability is 3.4 percent in the UE setup and 4.7 percent in the BAH setup. Thus, the 

model predicts that, although precautionary savings provide substantial self-insurance to 

reduce the probability of Sudden Stops sharply in the long run, the process of building this 

war chest of assets is long and gradual. 

4. Conclusions 

 This paper examines the recent surge in foreign reserves in Sudden Stop countries from 

the perspective of a dynamic, stochastic general equilibrium framework of optimal 

precautionary demand for foreign assets in a small open economy. The framework allows for 

two formulations of preferences with different implications for self-insurance behavior (the 

Bewley-Aiygari-Hugget setup and the Uzawa-Epstein setup). We use this framework to 

study a one-sector endowment economy and a two-sector production economy with “liability 

dollarization” (i.e., debt denominated in units of tradables but leveraged partly on the 

income from the nontradables sector). The two-sector model features a credit constraint that 

produces endogenous Sudden Stops driven by Irving Fisher’s debt-deflation mechanism. The 

constraint limits debt not to exceed a fraction of total income in units of tradables. When 

the constraint binds, the price and income of the nontradables sector collapse, thereby 

reducing further the agents’ ability to borrow. As a result, the responses of macroeconomic 

variables to shocks of standard magnitude show significant amplification and persistence. 

 

11 The initial condition b/y = -0.487 used in Fig. 9 is almost at the border of the long-run Sudden 

Stop region with the region where the credit constraint does not bind, so it produces a relatively 

moderate Sudden Stop (which is also closer to the magnitudes observed in the data). 
-24-

 The self-insurance framework studied in this paper features three key mechanisms that 

drive precautionary demand for foreign assets: increases in the cyclical volatility of output, 

international financial integration, and Sudden Stop risk. Our quantitative analysis shows 

that all three mechanisms can produce large changes in foreign asset holdings. Output 

volatility cannot explain the observed surge in reserves, however, because output volatility 

has not increased in Sudden Stop countries. By contrast, financial integration and Sudden 

Stop risk produce large increases in foreign asset holdings that are comparable to observed 

surges in reserves, and the data do show that the process of far-reaching financial 

globalization and the emergence of Sudden Stop risk coincided with the buildup of reserves. 

 We also found that the adjustments in foreign assets and key macroeconomic aggregates 

triggered by financial globalization and Sudden Stop risk follow a protracted and gradual 

process. This process features persistent current account surpluses and undervalued real 

exchange rates that are reverted slowly. The probability of Sudden Stops declines slowly, 

because it takes more than 50 years to attain its minimum level in the long run. Financial 

globalization also progresses in nonlinear fashion in the economy with BAH preferences. In 

its early stages, large changes in barriers to global asset trading produce small changes in 

foreign asset positions, but as a regime of perfect capital mobility is approached, small 

changes in restrictions affecting global asset markets produce large changes in asset 

positions. 

 The two-sector production economy has interesting implications for business cycle comovements and precautionary savings. The model yields countercyclical fluctuations in the 

current account and net exports, along with large, persistent fluctuations in the real 

exchange rate. In addition, the model features an implicit hedging mechanism because 

tradables income and nontradables income in units of tradables are negatively correlated at 

equilibrium. Hence, when an adverse, nondiversifiable shock hits tradables income, the value 

of nontradables income rises, and the higher consumption of nontradables offsets some of the 

adverse effect of the fall in tradables income. As a result, the buffer stocks of assets implied 

by precautionary savings are smaller in the two-sector model than in the one-sector model. 

This outcome, however, depends on the elasticity of substitution between tradables and 

nontradables in consumption and on features of preferences and technology that drive the 

equilibrium of the market of nontradables. 

 One caveat of our analysis of financial globalization is that we abstract from efficiency 

gains. If an emerging economy that opens the capital account has relatively little capital, its 

marginal return will be high; this would trigger an investment boom that gives an incentive 

to borrow instead of saving. The investment boom, however, should be directly attributed 

not to financial integration but to the removal of distortions on capital accumulation. 

Mendoza and Tesar (1998) and Gourinchas and Jeanne (2006) showed that the efficiency 

and welfare gains of removing these distortions, even when the economy is in financial 

autarky, are much larger than the extra gains that result from the shift from financial 

autarky to financial globalization. A second caveat is that we did not consider the possibility 

of sovereign default. Alfaro and Kanczuk (2006) showed, however, that given the choice of a 

portfolio of “defaultable debt” and reserve assets, the optimal choice is to hold zero reserves. 

Default considerations, therefore, do not seem useful for explaining the surge in reserves. 
-25-

 Precautionary demand for foreign assets arises from distortions on financial markets 

(incomplete insurance markets, credit constraints, etc.), and hence it is suboptimal from a 

normative standpoint. This raises two important normative questions that we did not 

address in this paper: What are the welfare costs that result from these distortions? And, 

are there policy strategies that can yield outcomes sufficiently superior, relative to 

precautionary savings under the status quo, to outweigh their implementation costs? 

 

 Results in the literature shed some light on those questions. On one hand, the classic 

Lucas result showing that the welfare cost of consumption volatility in representative-agent 

models with CRRA utility is negligible, regardless of the asset market structure and as long 

as growth and business cycles are unrelated, suggests that the overall welfare cost of 

precautionary savings should be small. Indeed, international RBC models predict that the 

cost of moving from perfect world capital markets to financial autarky is negligible on 

average over the long run. On the other hand, Durdu and Mendoza (2006) showed that 

welfare costs conditional on a Sudden Stop state are high. They also found, however, that 

attempting to prevent these Sudden Stops with a credit facility that aims to defuse the debtdeflation process can result in distortions with even larger costs, unless the facility functions 

with a complex state-contingent policy. Caballero and Panageas (2006) showed that 

precautionary savings can be highly undesirable for emerging economies that hope to attain 

higher long-run growth rates, and in this case arrangements that can provide financing when 

Sudden Stops hit are welfare-improving. 

 If we move away from the representative-agent paradigm, the costs of precautionary 

savings and financial globalization for countries with underdeveloped financial systems can 

be large, and distributed regressively across the population (see Mendoza et al. (2007)). The 

best policy response is to promote the development of domestic financial systems in 

emerging economies. Beyond this obvious response, the class of policy recommendations that 

are effective for dealing with the adverse distributional implications of financial globalization 

without financial development are yet to be identified. 

 In the final analysis, we conclude that the argument behind the New Mercantilism is 

only partially correct. On one hand, our findings do show that the aim to minimize Sudden 

Stop risk can lead to a surge in foreign asset holdings. On the other hand, the New 

Mercantilism cannot be defended by arguing that business cycle volatility has increased, 

because this is not observed in the data. The New Mechantilism also fails inasmuch as 

frameworks like ours, or the one developed by Mendoza, et al. (2007) predict that financial 

globalization can be a strong driving force of the rise in reserves. Self-insurance behavior is 

part of the mechanism by which globalization affects foreign asset holdings in those models, 

but is unrelated to a desire to build a war chest for defense against Sudden Stops. Instead, it 

is a consequence of the removal of barriers to global asset trading given the 

underdevelopment of the financial markets that agents in Sudden Stop economies can access. 
-26-

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Table 1. International Reserve Position in Sudden Stop Economies 1

Country Year of Sudden Before 3/ After 4/ Difference

Stop 2/

Argentina I 1994 3.20 8.62 5.42

Argentina II 2001 5.04 11.54 6.51

Brazil 1998 4.36 7.65 3.30

Chile 1998 16.93 20.49 3.57

Colombia 1998 9.24 12.21 2.97

Ecuador 1999 7.35 3.89 -3.46

Hong Kong 1998 34.16 68.85 34.69

Indonesia 1997 6.53 18.69 12.17

Korea 1997 5.03 21.26 16.23

Mexico 1994 4.64 7.29 2.65

Malaysia 1997 25.18 39.54 14.36

Pakistan 1998 1.90 8.51 6.61

Peru 1998 9.25 16.66 7.41

Philippines 1997 6.05 16.69 10.65

Russia 1998 3.05 12.46 9.41

Thailand 1997 14.84 28.01 13.17

Turkey 2001 5.67 13.57 7.90

Uruguay 2002 7.18 20.06 12.87

Median 6.29 15.12 7.66

Median Asian Countries 6.53 21.26 13.17

Source: Authors' calculations

1/ Refers to the emerging market economies that experienced a Sudden Stop during the

past two decades. 

2/ We include Sudden Stop episodes that are included in various empirical studies of 

Sudden Stops, such as Calvo, Izquierdo, and Mejia (2004), Cavallo and Frankel (2004), 

and Rothenberg and Warnock (2006).

3/ Covers the period from 1985 to the year before the sudden stop.

4/ Covers the period from the year after the sudden stop until 2004.

% of GDP
Table 2. Business Cycles and Financial Globalization

Output Consumption Relative to Output

Full Preglobalization 

(1)

Globalization 

(2) Ratio (1)/(2) Full Preglobalization 

(1)

Globalization 

(2) Ratio (1)/(2)

Industrial Countries

United States 1.90 2.38 1.57 1.52 0.89 0.83 0.98 0.85

United Kingdom 1.90 2.01 2.00 1.01 1.21 1.15 1.28 0.90

France 1.22 1.13 1.49 0.76 0.97 0.94 0.95 0.99

Germany 1.68 2.02 1.51 1.34 0.94 1.02 0.77 1.32

Japan 2.62 3.29 1.98 1.66 0.70 0.68 0.72 0.95

Mean Industrial Countries 2.14 2.33 2.10 1.11 1.03 1.08 1.00 1.08

Median Industrial Countries 1.90 2.01 1.90 1.06 0.99 1.11 0.96 1.15

EME

Sudden Stop Countries 1

Argentina 5.48 3.46 7.20 0.48 1.68 3.44 1.00 3.45

Brazil 4.14 5.28 2.70 1.95 1.34 0.99 2.30 0.43

Chile 4.88 6.79 2.98 2.28 1.98 1.95 1.46 1.34

Colombia 2.26 2.33 2.47 0.94 1.23 1.00 1.34 0.74

Ecuador 3.15 3.93 2.62 1.50 0.89 0.89 0.87 1.03

Mexico 3.27 3.67 3.06 1.20 1.14 0.99 1.38 0.72

Peru 5.19 3.51 7.13 0.49 1.03 0.77 1.00 0.77

Uruguay 5.45 6.48 5.25 1.24 1.29 1.17 1.41 0.84

Hong Kong 3.50 3.78 3.12 1.21 1.09 1.20 1.08 1.11

Indonesia 4.11 2.81 5.32 0.53 1.51 2.51 1.12 2.25

Korea 3.19 3.36 2.90 1.16 1.24 1.10 1.53 0.72

Malaysia 3.54 2.64 4.66 0.57 1.61 1.75 1.54 1.13

Pakistan 2.18 2.63 1.72 1.53 2.25 2.39 1.76 1.36

Philippines 3.33 4.16 2.88 1.44 0.54 0.52 0.58 0.89

Thailand 4.23 2.44 5.97 0.41 1.03 1.09 1.02 1.07

Turkey 3.52 3.40 4.06 0.84 1.43 1.13 1.60 0.71

Mean SSC 3.84 3.79 4.00 1.11 1.33 1.43 1.31 1.16

Median SSC 3.53 3.48 3.09 1.18 1.26 1.12 1.36 0.96

Mean EMEs 3.77 3.75 3.64 1.03 1.30 1.42 1.26 1.12

Median EMEs 3.53 3.57 3.09 1.15 1.27 1.18 1.19 0.99

Source: Authors' calculations. Preglobalization and globalization refer to the 1966 to 1985 and 1986 to 2005 periods, respectively.

1/ See Table 1 for a definition of Sudden Stop Countries (SSC).
Table 3. Sectoral Volatility and Financial Globalization

Tradable Output Nontradable Output

Full Preglobalization 

(1)

Globalization 

(2) Ratio (1)/(2) Full Preglobalization (1)

Globalization 

(2) Ratio (1)/(2)

Industrial Countries

United States 3.93 4.52 3.50 1.29 1.47 1.30 1.62 0.81

United Kingdom 4.91 6.43 3.54 1.81 2.07 2.37 1.81 1.30

France 4.19 4.97 3.62 1.37 1.24 0.88 1.48 0.60

Germany 2.82 2.54 3.07 0.83 1.44 1.57 1.38 1.14

Japan 3.68 4.06 3.75 1.08 2.51 3.23 1.68 1.92

Mean Industrial Countries 3.39 3.68 3.25 1.13 1.03 1.08 1.00 1.08

Median Industrial Countries 3.34 3.84 3.25 1.18 0.99 1.11 0.96 1.15

EME

Sudden Stop Countries 1

Argentina 5.48 3.93 6.92 0.57 5.24 3.81 6.54 0.58

Brazil 4.44 4.50 4.39 1.02 4.03 4.85 3.12 1.55

Chile 5.99 n.a. 5.99 n.a. 3.74 n.a. 3.74 n.a.

Colombia 4.82 2.83 6.34 0.45 3.15 2.21 3.99 0.55

Ecuador 6.80 4.74 8.60 0.55 5.45 5.80 5.07 1.14

Mexico 3.36 3.07 3.70 0.83 3.27 3.53 3.00 1.18

Peru 5.74 4.72 7.38 0.64 5.05 3.32 6.94 0.48

Uruguay 5.98 6.41 6.46 0.99 5.80 6.88 5.59 1.23

Hong Kong 5.33 n.a. 5.33 n.a. 2.06 n.a. 2.06 n.a.

Indonesia 3.02 2.55 3.56 0.72 5.30 3.91 6.74 0.58

Korea 4.59 5.11 4.17 1.22 2.79 2.74 2.84 0.97

Malaysia 4.17 3.33 4.81 0.69 4.58 3.23 5.28 0.61

Pakistan 2.62 3.27 2.22 1.47 2.49 3.02 1.47 2.06

Philippines 3.08 3.78 2.74 1.38 3.98 5.06 3.25 1.56

Thailand 4.03 2.88 5.42 0.53 4.76 2.96 6.59 0.45

Turkey 3.44 3.12 3.75 0.83 3.72 4.06 3.50 1.16

Mean SSC 4.56 3.87 5.11 0.85 4.09 3.95 4.36 1.01

Median SSC 4.52 3.56 5.07 0.77 4.00 3.67 3.87 1.06

Mean EMEs 4.51 4.04 4.82 0.84 4.22 4.37 3.67 1.19

Median EMEs 4.43 3.78 4.28 0.88 3.93 3.91 3.38 1.16

Source: Authors' calculations. Preglobalization and globalization refer to the 1966 to 1985 and 1986 to 2005 periods, respectively.

1/ See Table 1 for a definition of Sudden Stop Countries (SSC).
Table 4. Volatility Relative to the United States

Output Consumption

Full Preglobalization 

(1)

Globalization 

(2) Full Preglobalization (1)

Globalization 

(2)

Industrial Countries

United States 1.00 1.00 1.00 1.00 1.00 1.00

United Kingdom 1.00 0.85 1.27 1.36 1.18 1.66

France 0.64 0.47 0.95 0.69 0.54 0.92

Germany 0.88 0.85 0.96 0.93 1.05 0.76

Japan 1.38 1.38 1.26 1.07 1.14 0.93

Mean Industrial Countries 1.13 0.98 1.34 1.03 1.08 1.00

Median Industrial Countries 1.00 0.85 1.21 0.99 1.11 0.96

EME

Sudden Stop Countries 1

Argentina 2.88 1.45 4.59 5.42 6.04 4.68

Brazil 2.18 2.22 1.72 3.25 2.65 4.04

Chile 2.56 2.86 1.90 5.67 6.71 2.83

Colombia 1.19 0.98 1.57 1.63 1.18 2.16

Ecuador 1.66 1.65 1.67 1.65 1.77 1.48

Mexico 1.72 1.54 1.95 2.18 1.85 2.74

Peru 2.73 1.48 4.54 3.13 1.37 4.63

Uruguay 2.86 2.73 3.35 4.12 3.86 4.80

Hong Kong 1.84 1.59 1.99 2.25 2.30 2.20

Indonesia 2.16 1.18 3.39 3.65 3.58 3.86

Korea 1.68 1.41 1.85 2.32 1.88 2.89

Malaysia 1.86 1.11 2.97 3.35 2.35 4.68

Pakistan 1.14 1.11 1.10 2.88 3.20 1.97

Philippines 1.75 1.75 1.84 1.05 1.09 1.08

Thailand 2.23 1.03 3.81 2.57 1.35 3.97

Turkey 1.85 1.43 2.59 2.96 1.96 4.24

Mean SSC 2.02 1.59 2.55 3.00 2.70 3.27

Median SSC 1.86 1.47 1.97 2.92 2.13 3.37

Mean EMEs 1.98 1.58 2.32 2.93 2.68 2.90

Median EMEs 1.86 1.50 1.97 2.80 2.25 2.78

Source: Authors' calculations. Preglobalization and globalization refer to the 1966 to 1985 and 1986 to 2005 periods, respectively.

1/ See Table 1 for a definition of Sudden Stop Countries (SSC).
Table 5. Calibration of the One- and

Two-Sector Models

Notation Parameter/Variable Value

1. One-Sector Model

ρ

BAH Rate of time preference in the BAH setup 0.064

ρ

UE Rate of time preference elasticity in the UE setup 0.109

γ Coefficient of relative risk aversion 2.000

★ Ad hoc debt limit -0.510

R Gross world interest rate 1.059

y Mean output 1.000

c Consumption-output ratio 0.692

b Net foreign assets-output ratio -0.440

σ e Standard deviation of output innovations 0.026

✣y Autocorrelation of output 0.597

A Lump-sum absorption 0.282

2. Two-Sector Model

ρ

BAH Rate of time preference in the BAH setup 0.059

ρ

UE Rate of time preference elasticity in the UE setup 0.187

γ Coefficient of relative risk aversion 2.000

μ Elasticity of substitution 0.316

a CES weight of tradable consumption 0.341

★ Ad hoc debt limit -0.700

α Share of imported inputs 0.200

R Gross world interest rate 1.059

b Net foreign assets-output ratio -0.440

p

N

Relative price of nontradables 1.000

p

m

Price of imported input 1.000

y

T

 + p Ny

N

Output in units of tradables 1.000

c

T

/y T

Tradable consumption-output ratio 0.665

c

N

/y N

Nontradable consumption-output ratio 0.710

p

N

y

N

/y T

Nontradable-tradable output ratio 1.543

A

T

Lump-sum absorption of tradables 0.106

A

N

Lump-sum absorption of nontradables 0.176

Note: BAH refers to Bewley-Aiyagari-Hugget, UE refers to Uzawa-Epstein, and CES refers to 

constant-elasticity of substitution.
Table 6. Statistiscal Moments of the Stochastic Stationary State 

of the One-Sector Economy

UE BAH UE BAH UE BAH UE BAH UE BAH

Precautionary savings 1/ 0.02 0.10 0.04 0.12 0.05 0.22 0.01 0.05 0.10 0.24

Means

Output 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Consumption 0.69 0.69 0.69 0.70 0.70 0.70 0.69 0.69 0.70 0.70

Foreign assets -0.42 -0.42 -0.41 -0.39 -0.39 -0.30 -0.43 -0.46 -0.34 -0.28

Trade balance 2/ 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.02 0.02

Discount factor 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94

Coefficients of variation (in 

percent)

Output 3.28 3.28 3.63 3.63 4.97 4.97 2.49 2.49 3.28 3.28

Consumption 3.13 3.26 3.92 3.92 4.72 4.66 2.38 2.59 4.11 3.11

Foreign assets 24.41 10.11 29.73 13.39 36.97 20.28 18.52 6.33 40.92 20.10

Current account 2/ 2.68 2.02 2.77 2.08 4.08 3.42 2.03 1.40 2.81 2.48

Trade balance 2/ 3.04 2.11 3.27 2.23 4.62 3.66 2.30 1.44 3.72 2.78

Discount factor 0.14 0.00 0.18 0.00 0.21 0.00 0.11 0.00 0.18 0.00

Normalized coefficients of 

variation (relative to output)

Consumption 0.95 0.99 1.08 1.08 0.95 0.94 0.96 1.04 1.25 0.95

Foreign assets 7.43 3.08 8.19 3.69 7.43 4.08 7.44 2.55 12.46 6.12

Current account 2/ 0.82 0.62 0.76 0.57 0.82 0.69 0.82 0.56 0.86 0.75

Trade balance 2/ 0.92 0.64 0.90 0.61 0.93 0.74 0.93 0.58 1.13 0.85

Discount factor 0.04 0.00 0.05 0.00 0.04 0.00 0.04 0.00 0.06 0.00

Output correlations

Consumption 0.42 0.75 0.48 0.78 0.42 0.67 0.42 0.81 0.26 0.54

Foreign assets 0.32 0.56 0.34 0.53 0.32 0.44 0.32 0.62 0.19 0.33

Current account 2/ 0.97 0.85 0.97 0.83 0.97 0.89 0.97 0.81 0.99 0.93

Trade balance 2/ 0.76 0.70 0.68 0.63 0.76 0.73 0.76 0.67 0.66 0.74

Discount factor -0.42 0.00 -0.48 0.00 -0.42 0.00 -0.42 0.00 -0.26 0.00

Autocorrelations

Output 0.59 0.59 0.69 0.69 0.59 0.59 0.59 0.59 0.59 0.59

Consumption 0.97 0.84 0.97 0.88 0.97 0.88 0.97 0.81 0.99 0.93

Foreign assets 0.99 0.96 0.99 0.98 0.99 0.98 0.99 0.94 1.00 0.99

Current account 2/ 0.57 0.51 0.67 0.62 0.57 0.54 0.57 0.49 0.59 0.56

Trade balance 2/ 0.67 0.55 0.76 0.67 0.67 0.59 0.67 0.52 0.76 0.64

Discount factor 0.98 0.00 0.98 0.00 0.98 0.00 0.97 0.00 0.99 0.00

Source: Authors' calculations.

1/ Precautionary savings are measured as defined in the text. 

2/ Current account and trade balance are measured in percentage of output.

Baseline Auto Corr 0.7 Std Dev. 5% Std Dev. 2.5% Risk Aver. 5.0
Table 7. Statistical Moments of the Stochastic Stationary State

of the Two-Sector Economy

Econ w/ perfect 

credit markets

Econ w/ binding 

credit constraints

Econ w/ perfect 

credit markets

Econ w/ binding 

credit constraints

Precautionary savings 1/ 0.02 0.06 0.25 0.26

Means

Consumption of tradables 0.262 0.264 0.261 0.269

Consumption of nontradables 0.431 0.432 0.430 0.435

Consumption 0.360 0.362 0.359 0.366

Price of nontradables 1.005 1.011 1.003 1.027

Net foreign assets -0.424 -0.378 -0.447 -0.243

Current account-GDP ratio 0.000 0.000 0.000 0.000

Tradables GDP 0.393 0.393 0.393 0.393

GDP in units of tradables 1.002 1.006 1.000 1.019

Nontradables GDP 0.607 0.608 0.606 0.610

Imported input 0.152 0.153 0.152 0.156

Coefficients of variation (in 

percent)

Consumption of tradables 1.643 1.523 3.139 2.993

Consumption of nontradables 5.369 5.379 4.926 4.896

Consumption 3.626 3.634 3.169 3.109

Price of nontradables 6.622 6.550 8.099 8.053

Net foreign assets 19.824 16.436 42.960 76.574

Current account-GDP ratio 1.453 1.416 1.950 1.939

Tradables GDP 3.345 3.345 3.345 3.345

GDP in units of tradables 2.213 2.184 3.292 3.271

Nontradables GDP 3.050 3.059 2.797 2.793

Imported input 3.805 3.688 5.846 5.754

Correlation with GDP

Consumption of tradables 0.374 0.341 0.804 0.803

Consumption of nontradables -0.579 -0.579 -0.562 -0.591

Consumption -0.471 -0.482 -0.247 -0.293

Price of nontradables 0.749 0.740 0.867 0.873

Net foreign assets 0.266 0.151 0.437 0.408

Current account-GDP ratio -0.141 -0.142 -0.474 -0.497

Nontradables GDP -0.579 -0.579 -0.562 -0.591

Imported input 0.833 0.826 0.929 0.931

First-order autocorrelation

Consumption of tradables 0.961 0.920 0.908 0.897

Consumption of nontradables 0.483 0.484 0.505 0.502

Consumption 0.528 0.525 0.663 0.650

Price of nontradables 0.460 0.451 0.489 0.485

Net foreign assets 0.985 0.974 0.995 0.994

Current account-GDP ratio 0.489 0.477 0.456 0.455

Tradables GDP 0.587 0.587 0.587 0.587

GDP in units of tradables 0.674 0.653 0.673 0.660

Nontradables GDP 0.483 0.484 0.505 0.502

Imported input 0.514 0.484 0.577 0.565

Source: Authors' calculations.

Note: BAH refers to Bewley-Aiyagari-Hugget preferences, UE refers to Uzawa-Epstein preferences.

1/ Precautionary savings are measured as defined in the text. 

Baseline

UE BAH
Figure 1. Emerging Market Economies: Financial Integration

Source: Authors' calculations. 

1/ See Table 1 for a definition of sudden stop countries. 

Emerging Market Economies

Mean

Chinn-Ito

Edwards

Right Axis

-1.0

0.0

1.0

2.0

1970 1975 1980 1985 1990 1995 2000

40

50

60

70

80

Sudden Stop Countries 1

Mean

Chinn-Ito

Edwards

Right Axis

-1.0

0.0

1.0

2.0

1970 1975 1980 1985 1990 1995 2000

40

50

60

70

80
Figure 2. Output and Consumption Volatility: Sample of Countries

Source: Authors' calculations.

Preglobalization and globalization refer to the 1966 to 1985 and 1986 to 2005 periods, respectively. 

SSC refers to Sudden Stop countries. See Table 1 for a definition of Sudden Stop countries.

Industrial Countries: 

Output Volatility

NZL

AUS

ESP

PRT

IRL

GRC

FIN

JPN

CAN

CHE

SWE

NOR

NLD

ITA

FRA DEU DNK

AUT BEL

GBR

USA

0

2

4

6

8

0 2 4 6 8

Preglobalization (%)

Globalization (%)

SSC: Output

 Volatility

ARG

BRA

CHL

COL ECU

MEX

PER

URY

HKG

IDN

KOR

MYS

PAK

PHL

THA

TUR

0

2

4

6

8

0 2 4 6 8

Preglobalization (%)

Globalization (%)

Industrial Countries: Consumption

Volatility relative to Output Volatility

USA

GBR

AUT

BEL

DNK

FRA

DEU

ITA

NOR NLD SWE

CHE JPN CAN

FIN

GRC

PRT IRL

ESP

AUS

NZL

0

1

2

3

4

0 1 2 3 4

Preglobalization (%)

Globalization (%)

SSC: Consumption Volatility

 relative to Ouput Volatility

TUR

THA

PHL

PAK

MYS

KOR

IDN

HKG

URY

PER

MEX

ECU

COL

CHL

BRA

ARG

0

1

2

3

4

0 1 2 3 4

Preglobalization (%)

Globalization (%)
Figure 3. Sudden Stop Countries: Rolling Standard Deviation of Output Volatility 

1

(Percent)

Source: Authors' calculations.1/ Twenty-year rolling standard deviation. SS refers to Sudden Stops. See Table 1 for a definition of Sudden Stop countries.

Mean and Median Volatility

Median

Mean

012345678

1980

1985

1990

1995

2000

2005

Median Volatility By Regions

SS 

countries

Asian 

Countries

Latin 

American 

Countries

012345678

1980

1985

1990

1995

2000

2005

Mean Volatility By Regio

SS 

countries

C

Latin 

American 

Countries

012345678

1980

1985

1990

1995

20
Figure 4. One-Sector Model: Transitional Cumulative Distributions Functions

Source: Authors' calculations.

NFA refers to Net Foreign Asset.

A. Bewley-Aiyagari-Hugget Preferences

0.00

0.20

0.40

0.60

0.80

1.00

-0.51 -0.47 -0.44 -0.40 -0.36 -0.32 -0.29 -0.25 -0.21 -0.17

Net Foreign Assets (as percentage of mean GDP)

Probability

B. Uzawa-Epstein Preferences

(Starting from lowest 1% probability NFA position)

0

0.2

0.4

0.6

0.8

1

-1.27 -1.14 -1.02 -0.89 -0.77 -0.64 -0.52 -0.39 -0.27 -0.14 -0.02 0.11

Net Foreign Assets (as a percentage of mean GDP)

Probability

2 years 5 years 10 years 15 years Long-run distribution
Figure 5. One-Sector Model: Transitional Dynamics of Foreign Assets

Source: Authors' calculations

Note: Forecast functions are conditional on lowest positive

probability asset position and neutral shocks at date 1.

-50

-40

-30

-20

-10

0

10

1 10 19 28 37 46 55 64 73 82 Time

Percent of GDP

Uzawa-Epstein Bewley-Aiyagari-Hugget
Figure 6. Effects of Variability and Persistence of Output on

Precautionary Demand of Foreign Assets

Source: Authors' calculations

0%

10%

20%

30%

40%

50%

1 2 3 4 5 6 7 8

Standard deviation of output (%)

Foreign assets in Percent of GDP

A. Variability

0%

10%

20%

30%

40%

50%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Autocorrelation of output

Foreign Assets in Percent of GDP

Uzawa-Epstein Bewley-Aiyagari-Hugget

 B. Persistence
Figure 7. One-Sector Model: Financial Globalization, Foreign Assets,

 and Precautionary Savings

Source: Authors' calculations.

Note: NDL refers natural debt limit, ADL refers to ad hoc debt limit.

 A. Foreign Assets

(Long-Run Average)

-1200%

-1000%

-800%

-600%

-400%

-200%

0%

200%

0 5 10 15 20 25 30

Tax on Capital Flows (Percent)

Percent of GDP

B. Precautionary Savings

0%

200%

400%

600%

800%

1000%

1200%

0 5 10 15 20 25 30

Tax on Capital Flows (in percent)

Percent of GDP

0%

2%

4%

6%

8%

10%

Percent of GDP

Bewley-Aiyagari-Hugget: NDL Bewley-Aiyagari-Hugget: ADL=-2

Bewley-Aiyagari-Hugget:ADL=-1.0 Uzawa-Epstein

Right Axis
Figure 8. One- and Two-Sector Models: Financial Globalization, Foreign Assets 

and Precautionary Savings.

Source: Authors' calculations.

Note: BAH refers to Bewley-Aiyagari Hugget preferences, UE refers to Uzawa-Epstein preferences,

ADL refers to ad hoc debt limit.

A. Foreign Asset (Long-Run Average)

-5

-4

-3

-2

-1

0

1

0 5 10 15 20 25 30

Tax on Capital Flows (in percent)

Percent of GDP

B. Precautionary Savings

0%

20%

40%

60%

80%

100%

120%

140%

0 5 10 15 20 25 30

Tax on Capital Flows (in percent)

Percent of GDP

0%

2%

4%

6%

8%

10%

12%

14%

Percent of GDP

BAH two sectors:ADL=-1.0 BAH one sector:ADL=-1

UE two sectors UE one sector

Right Axis
-24

-20

-16

-12

-8

-4

0

4

5 10 15 20 25 30 35 40 45 50

-1

0

1

2

3

4

5 10 15 20 25 30 35 40 45 50

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

5 10 15 20 25 30 35 40 45 50

-10

-8

-6

-4

-2

0

2

5 10 15 20 25 30 35 40 45 50

-7

-6

-5

-4

-3

-2

-1

0

1

5 10 15 20 25 30 35 40 45 50

-8

-7

-6

-5

-4

-3

-2

-1

0

1

5 10 15 20 25 30 35 40 45 50

Bewley-Aiyagari-Hugget Preferences Uzawa-Epstein Preferences

Figure 9. Sudden Stops under Alternative Preference Specifications

(excess deviations from long run average relative to frictionless economy)

Foreign Assets Current Account-Output Ratio

Output of Nontradables Price of Nontradables

CES Consumption Total Output in Units of Tradables

Note: The plots show forecast functions of equilibrium Markov processes in response to initial negative shocks to tradables

output and TFP in nontradables, and an initial ratio of foreign assets to long-run GDP of -48.7 percent. The data are

plotted as differences in percent deviations from long-run averages in the economies with credit constraints relative to those

in the economies with perfect credit markets. Foreign assets are in percent of the long-run average of GDP.
Figure 10. Amplication Effects on Impact in the Sudden Stop Region 

(Differences in deviations from mean with and without credit constrainst in response to one standard deviation s

-100

-75-50-25

0

255075

100

-0.578 -0.566 -0.554 -0.542 -0.530 -0.518 -0.506 -0.494 -0.482 -0.4

Foreign assets as a percent of long-run GDP

Percent

Price of Nontradables: UE setup Current account-GDP ratio: UE sePrice of Nontradables: BAH setup Current account-GDP ratio: BAH 

BAH long run probability border

UE long run probability b

long-run Sudden Stop reg
Figure 11. Transitional Cumulative Distributions in the Binding Economy

Source: Authors' calculations.

A. Uzawa-Epstein Preferences

0.00

0.20

0.40

0.60

0.80

1.00

-0.70 -0.59 -0.48 -0.37 -0.26 -0.14 -0.03 0.08 0.19 0.30 0.41 0.52 0.63 0.74

Net foreign assets as a share of mean GDP

Probability

B. Bewley-Aiyagari-Hugget Preferences

0.00

0.20

0.40

0.60

0.80

1.00

-0.70 -0.59 -0.48 -0.37 -0.26 -0.14 -0.03 0.08 0.19 0.30 0.41 0.52 0.63 0.74

Net foreign assets as a share of mean GDP

Probability

2 years 5 years 10 years 15 years Long-run distribution