modelling international capital structure under foreign macroeconomic volatility

Mathematical and Computer Modelling 46 (2007) 151–162www.elsevier.com/locate/mcm

Modelling international capital structure under foreignmacroeconomic volatilityI

Constantina Kottaridia,∗, Gregorios Siourounisb

a School of Management and Economics, Department of Economics, University of Peloponnese, Greeceb Department of Economics, London Business School, United Kingdom

Received 18 May 2006; accepted 15 December 2006

Abstract

In the last decade we have witnessed a significant change in the structure of capital flows to developed as well as to developingcountries. We construct a simple econometric framework where country-specific random effects and macroeconomic monetaryvolatility are linked to the probability distribution of liquidity shocks hitting an international investor. A “volatility augmented”gravity equation is then estimated to provide empirical evidence that as the probability of getting a bad liquidity shock increases,investors switch to safer assets but with a pecking order: they seem to damp equities for more bonds and more direct investments.A flight to quality!c© 2007 Elsevier Ltd. All rights reserved.

Keywords: Foreign bond investments (FBI); Foreign direct investments (FDI); Foreign equity investments (FEI); Liquidity shocks; Monetarymacroeconomic volatility

1. Introduction

The unprecedented increase in worldwide capital flows in the past few years and the consequent changes in the‘globalized market’ have allured scholars’ interest to examine both theoretically and empirically the influencingfactors and implications of international capital structure.

Capital structure theory (regarding domestic markets) is rooted in the seminal work of Modigliani and Miller(M&M) back in 1958 and has, since then, been expanded in various ways by prominent authors [5,18,29]. Withaugmented financial integration and observed massive capital movements, domestic capital structure theory wasextended to account for international capital flows and composition ([17,15]; to name a few).

This paper aims at empirically testing for the information-based approach of capital flows. On these grounds,we model agents’ international investment choices as depending on the probability distribution of foreign liquidity

I We thank Wouter Denhaan, Christos Genakos, Itay Goldstein, Stefan Nagel, Sergey Sanshar, Assaf Razin, and the participants of the EEFS2003 Conference (Bologna, Italy) for useful comments and suggestions.

∗ Corresponding author.E-mail address: [email protected] (C. Kottaridi).

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152 C. Kottaridi, G. Siourounis / Mathematical and Computer Modelling 46 (2007) 151–162

shocks,1 which we hereby claim that is greatly linked to country-specific random effects and macroeconomic monetaryvolatility. Volatility of macroeconomic monetary aggregates is documented to raise the probability of a bad liquidityshock, inducing a shift in the composition of capital worldwide, implying, thus, a pecking order.

International capital flows can take various forms, the characteristics of which differ markedly in terms of e.g., cost,risk bearing, vulnerability to capital flows reversal, conditionality, access to intellectual property [34]. Conventionalwisdom classifies international capital flows into three major categories, namely, debt flows (FBI), portfolio equityflows (FPI) and foreign direct investment (FDI). A common feature of these lies in the blending of foreign savingswith domestic savings to finance domestic investment [28]. FDI is nonetheless differentiated from the rest in its origin,since, by default, it entails ownership control2 of a domestic firm.3

We use an extensive database, representative of the global environment, of gross cross-border transactions of debt,portfolio equity and direct flows among 15 countries over a period of 13 years. This allows for robust empiricalevaluation of the theoretical prediction that international investors’ liquidity canter associated with the foreigninvestment will have a direct impact on the composition of her international portfolio. The sample coverage, bothin terms of time series and cross-country, enables the distinction of alternative capital structure.4,5

The remainder of the paper is organized as follows. Section 2 reviews related literature. Section 3 develops a“volatility” augmented gravity equation. Section 4 reports the results. Section 5 addresses some data issues andSection 6 concludes.

2. Related literature

Research on capital flows has generated a number of different strands in the literature, which treat each flow inisolation. Financial crises such as that of Latin America in the 1980s, Mexico in 1994–95, South and South–EastAsia in 1997–98, Turkey in 2000–2001 and more recently Argentina in 2001–2002 revealed the resilience of FDI insharp contrast to FPI and FBI reviving the interest on the properties of capital flows with regards to macroeconomicenvironments. In particular, Krugman [19] argues that sometimes the transfer of control occurs in the midst of a crisiswhen other types of international investment dry up.6 FDI is widely considered as means of risk diversification in theabsence of equity markets [16], although there are studies that testify that the weaker the institutions of a country, theless the FDI share in total liabilities [32,33,8].

Despite the overwhelming number of studies conducted to date on the external capital structure of countries, thedebate over an accurate theory that explains the observed international capital composition still goes on. However,it is possible to distinguish between two fully fledged theories: one that discusses expropriation risk and financialconstraints as key explanations for capital flows [35] and the other that relates to non-symmetric information problems,either between managers and owners of a firm [25] or between domestic and foreign investors [13,26,27].

A major body of the literature though builds on the second strand7 with the well-documented phenomenon of the‘lemons’ problem as originally described by Akerlof [1].8 Based on asymmetric information, Razin and Sadka [25]and Goldstein and Razin [12] support the ‘pecking order’ theory of international capital structure, in accordance withhypotheses holding in corporate finance. Nevertheless, more recent studies on the preference ranking of financingmodes reject this kind of financing behavior [9] or propose a modified pecking order [21].

Other potentially influential aspects that have been explored in empirical studies refer to economic size, financialdevelopment, and openness, whilst particular attention has been given to the ‘diversified’ – with respect to the othertwo financing means – nature of FDI.

1 We define a “foreign liquidity shock” as a shock that is associated with an investment in a foreign country.2 Conventionally, the cut-off point, which enables control of a domestic firm by a foreigner, is assumed to be the ownership of 10% of its equity.3 Froot [10], however, notes that FDI actually requires neither capital flows nor investment in capacity.4 For a recent theoretical documentation, see Goldstein and Razin [12].5 See for example [36,23] and [24].6 Related work with regards to the FDI resilience belongs to Hausman and Fernando-Arias [14], Albuquerque [3], Razin and Sadka [25].7 Informational asymmetries are distinguished as those regarding foreign and domestic investors [13,26–28] and the ones between insiders and

outsiders of firms [25,12].8 FDI may serve to reduce or eliminate the ‘lemons’ problems insofar as purchasing a controlling equity stake in a firm allows the foreign investor

to eliminate informational asymmetries [20].

C. Kottaridi, G. Siourounis / Mathematical and Computer Modelling 46 (2007) 151–162 153

The present paper enriches the empirical evidence of the pecking order of international capital flows byeconometrically modeling and testing for the association of macroeconomic volatility and the composition of capitalflows. The aim henceforth is to derive a structural equation from the information-based approach of internationalcapital flows and empirically assess the sensitivity of these flows on the probability distribution of liquidity shocks.

3. A “volatility” augmented gravity equation

3.1. International liquidity constrains

This paper builds on the information-based trade off model between foreign direct investments and foreign portfolioinvestments of Goldstein and Razin [12], whilst interesting insights are borrowed from the work of Secru andUppal [30].

The latter develop a general equilibrium stochastic endowment economy with imperfect international commoditymarkets where they model exchange rate volatility with various shocks hitting an economy. Depending on the sourceof the shock, the model predicts a decrease/increase in the volume of capital flows (increase/decrease in the volumeof trade).

Goldstein and Razin [12] claim that direct investors are better informed about the fundamentals of their projectsand this enables their more efficient management. Yet, this fact entails an asymmetric effect in case they want to selltheir projects early, resulting in a lower price than they would otherwise get. Because of this asymmetry, Goldstein andRazin [12] prove that, for some parameter values, investors who are aware that they are more likely to get a liquidityshock, which will force them to sell before maturity of the project, would rather make portfolio investments. On theother hand, investors that know they are less likely to face a liquidity shock choose direct investments.

Within an international context, the above prediction is equivalent to saying that a representative investor in countryi decides whether she will invest in FDI or FPI in country j according to her sensitivity in any future liquidity shockassociated with her foreign investment when this is realized. At time T = 1, the investor decides on the type of capitaland the country j she will invest in. At time T = 2, she has already decided on the location and composition of herportfolio, but now, she is subject to liquidity shocks associated with random specific characteristics of her foreigninvestment and macroeconomic characteristics of the foreign destination country. To this end, and on the grounds ofrationality [7], the investor solves the problem backwards; depending on her sensitivity relative to foreign liquidityshocks, she selects direct or portfolio investment in the country that best serves her needs!

Under the assumption that the liquidity shock of a country’s i representative investor who invests in a foreigncountry j is normally distributed, we hereby introduce two sources of uncertainty. The first one is connected toforeign firm’s specific characteristics such as productivity shocks, corporate governance, etc., whereas the secondone is associated with the foreign country’s specific monetary characteristics such as money market rates’ volatility,inflation volatility, exchange rate volatility, etc. Based on the above, the liquidity shock may be modeled as below:

λi j t = fi j t + mi j t (1)

where λi j t stands for country i’s liquidity shock of her investment in country j that forces her to sell early, fi j tis the component due to firm-specific characteristics from investing in a particular firm in country j , and mi j t is thecomponent associated with monetary macroeconomic factors in country j . We assume that the corporate component israndomly distributed with 0 mean and constant variance σ 2. The second component may be any measure of country’sj monetary macroeconomic conditions. The implication of (1) is that the sensitivity of an investor’s liquidity shock,λ, is merely but intuitively given by the dispersion of the density function of λ, or equivalently, by the variance of therandom variable λi j t .

More explicitly, let us assume that country i is populated by two types of investor, one with high sensitivity in λi j tand one with low sensitivity in λi j t . Based on the variance of λi j t in the destination country j , the two investors willselect different investment projects. If the destination country j has high volatility of λi j t (i.e., the distribution of λi j tis not concentrated around the mean value), then this country would attract more portfolio equity investors comparedto direct investors since the probability of getting a bad liquidity shock is high (i.e., the left tail of the distribution ofλ is thick). If the host country is of low volatility of λi j t (i.e., the distribution of λi j t is not concentrated around themean value), then it would primarily attract direct investments and few, if not at all, portfolio equity investments, sincethe probability of getting a bad liquidity shock is lower compared to the previous case. Rational investors anticipate


the ex post vulnerability of their position given their liquidity constraints, and thus form their portfolio based on theprobability density of their individual foreign liquidity shock [12]. This follows because an increase in the dispersionof the probability density of liquidity shocks would drive out direct investors and attract portfolio investors.

In an information-based framework, the ratio of gross cross-border transactions between FDI and FPI will be afunction of the joint uncertainty of a liquidity shock to sell early. That is:

T(FDIi j /FPIi j )t = f (Var(λi j t + λ j i t )) + g(Si t , S j t , Di j ) (2)

where T(FDIi j /FPIi j )t represents the gross cross-border transactions in FDI relative to FPI between country i andj , f [Var(λi j t + λi j t )] is a function (probably non-linear) of the variance of the two liquidity shocks that are notidentical.9 The last right-hand term g(Si t , S j t , Di j ) is a function of the conventional gravity variables of country sizesand distance. Hence, the above equation implies that gross cross-border transactions depend on the joint volatility ofthe individual processes of the liquidity shocks.

The effect of a liquidity shock on the ratio of FDI to FPI depends greatly on the individual sensitivity of direct andportfolio investments in that shock. Dropping time for simplicity:

Var(λi j + λ j i ) = Var(λi j ) + Var(λ j i ) ± 2Cov(λi j , λ j i ) (3)

which is equivalent to:

Var(λi j + λ j i ) = σ 2λi + σ 2

λ j + Var(m j i ) + Var(m j i ) ± 2Cov(λi j , λ j i ). (4)

Consequently, the variance of the liquidity shock between two countries depends on the variances of the randomcorporate components, the variances of the country-specific monetary macroeconomic variables and the interactionbetween them.

3.2. Identification and estimation

In order to estimate (2), we make a number of assumptions that provide sufficient identifying restrictions. The firstassumption points to an anchor country, the US. In such a case, the liquidity shock inducing an early sale of a foreigndirect or portfolio investment in the anchor country (US) is subject only to a random corporate component:

λus j = fus j . (5)

On these grounds, (2) may be written as follows:

Ti j t = f(Var(λi j t + λ j i t )

)+ g(Si , S j , Di j )

= f{Var

[(fi j t + mi j t

)+

(f j i t + m j i t

)]}+ g(Si , S j , Di j ) (6)

= f [(σ 2λi + σ 2

λ j + Var(mi j t ) + Var(m j i t ) − (7)

± 2Cov(mi j t , m j i t ) ± 2Cov( fi j t , f j i t ))] + g(Si , S j , Di j ) (8)

= f[υi j + Var(mi j t ) + Var(m j i t )

]+ g(Si , S j , Di j ).

10 (9)

On the additional assumption that the functions f ( ) and g( ) are linear in their components then (9) can be writtenas:

T(FDIi j /FPIi j )t = a + υi j + b1i j Var(mus j )t + b2i j Si t + b3i j S j t + b4i j Di j + εi j t . (10)

Intuitively, a representative US investor is subject to liquidity shocks that are affected both by the corporate randomcomponent related to foreign firms and the monetary macroeconomic component of the foreign country. The foreigninvestor’s liquidity shock depends only on the corporate random component associated with the investment in the US.

9 This is because λi j t = fi j t + mi j t is the liquidity shock that an investor in country j faces when investing in country i .10 Cov( fi j t , f j i t ) = 0, given that f ∼ i id N (0, σ 2

λ ), and σ 2λi + σ 2

λ j = υi j is a Gaussian individual random effect, independent of time, for eachcountry pair i j .


The above road map for identification combined with panel data estimation techniques will enable estimation ofthe model’s coefficients.

The basic model may be summarized in the following regression equation:

Yi t = ai + B ′ X i t + ζi t (11)

where X i t contains K regressors excluding the constant term, ai captures individual effects assumed to be constantover time t and specific to the individual cross-section unit i . In this setting it is more appropriate to view individualspecific constant terms as randomly distributed across cross sectional units. Thus (11) can be written as:

Yi t = ηi + B ′ X i t + ζi t (12)

where now ηi is the random disturbance of the i th observation. Using (12), (10) may be estimated on thefollowing mappings:

X i t = [Var(mus j )t , Si t , S j t , Di j ]

B = [b1i j , b2i j , b3i j , b4i j ]

ηi = υi j

ζi t = εi j t

where ui is in fact the individual random corporate component of the sum of the variances of the individualidiosyncratic components of the liquidity shock of two countries engaging in cross-border capital transactions.

3.3. Implementation

As already mentioned, a new data set on bilateral flows between the US and a set of 15 advanced and developingcountries is used to estimate the above model, extracted from the Treasury International Capital system (TIC) data setof the US Treasury Office regarding the mode of firms’ financing.

The panel expands over 13 years, 1988–2000, and the data is in annual frequency for all variables. The present dataregard bilateral flows of government bonds, corporate bonds, corporate equities, foreign (from the US perspective)bonds, and foreign stocks.

It is worthwhile mentioning at this point that whilst foreign residents’ transactions in US bonds are broken downin the data between corporate and government, the converse does not hold since the data aggregates US residents’transactions in foreign bonds to include all bonds for each foreign country. This limits the number of capital forms tothree categories, i.e., total bonds, total equities and total direct investments.

In all regressions the gross variable incorporated is defined as the sum of total sales and total purchases. The US isthe anchor country and the set of the rest are both host and source countries. There are some missing observations, sothe panel has a maximum of 13 × 15 × K observations. The countries were selected in order to represent the globalenvironment:

North America: United States (anchor), Canada.Latin America: Argentina, Brazil, Mexico.East Asia: Japan, Singapore, Hong Kong.Euro currency zone: France, Germany, Italy, Netherlands, Spain.Non-euro currency zone: Switzerland, United Kingdom.Pacific: Australia.The share of the above 15 countries in global equity market capitalization in 2002 was 72%. All theoretical models

emphasize the role of the level of development. Financial development likely means that asymmetric informationproblems are diminished, encouraging thus equity structures.

Also, less developed countries with many small businesses do not issue equities; hence capital inflows might bebiased towards government debt. Another consideration is the issue of debt/equity split. Firms might grow largeenough to be less exposed to bankruptcy risk, stimulating debt over equity. We therefore include a measure of real percapita GDP in order to proxy the degree of financial development [20,22].

Asymmetric information problems are also of great concern in respective literature and are likely more severe thegreater the ‘difference’ between investors and the location of investments. Portes and Rey [23] and Portes et al. [24]


Fig. 1(a). Ratio of direct to equity investments (eqnew) vs. per capita foreign GDP.

Fig. 1(b). Log real per capita foreign GDP (lrgdpfnew) and money market rate volatility.

document that geographical distance is a good proxy for information. The difference may be related to factors such asproximity, cultural elements and legal systems. Therefore, we also include distance to proxy information asymmetriesas well as several dummies for adjacency, language proximity, common borders, etc.

The innovative part of the empirical specification relies on the association of liquidity shocks with country-specificvolatility of monetary macroeconomic aggregates, which in turn affect the composition of capital flows. In ourempirical investigation we focus on the volatility of inflation, exchange rates and money market rate.11 Abstractingfrom any monetary volatility variables that provide a measure of the stability of the macroeconomic environment afirm operates, we might run into the following fallacy: the estimated gravity equation (without risk consideration)may result in the same quantity and quality of capital transactions between any two countries that are identical inlevels. This, however, is not necessarily true in practice, because high volatility of country-specific inflation, forexample, might prevent a risk-averse agent from investing in an inflationary volatile country since it might not be arisk improvement strategy for her portfolio given the increased implied market price of risk that directly enters herprobability of receiving a bad liquidity shock to sell early.

In order to elucidate this point, Fig. 1(a) portrays the ratio of direct to portfolio equity investments in these countries.Richer countries demonstrate larger direct–portfolio equity investment ratios. On the other hand, Fig. 1(b) shows thatricher countries have lower interest rate volatility and thus, according to Goldstein and Razin [12], should attract moreFPI than FDI or the ratio of FDI to FPI would be lower in these countries.

In addition, exchange rate volatility seems to play a significant role in the time horizon of an investment andis definitely relevant for optimal international resource allocations in the long run. Individual investors as well asinstitutions, though not fully informed about the country-specific level of risk, do use some indicators to back up the

11 See also Alfaro et al. [2], Goldberg and Kolstad [11] and Bacchetta and Wincoop [4] for discussion on these measures as reflectingmacroeconomic stability.


Table 1Traditional gravity equation estimates

Coeff \ Dep Var Yi t = a + B′ Xi t + ui + εi tFDI FEI FBI

Dist −0.195 (0.01) −0.003 (0.04) −0.240 (0.03)Foreign GDP 2.519 (0.02) 0.042 (0.03) 6.903 (0.00)US GDP 3.229 (0.00) 0.104 (0.00) 16.067 (0.00)

R2 all 0.4387 0.3392 0.3782Wald − χ2 134 (0.00) 108 (0.00) 632 (0.00)aBPL test:H0 : Var(u) = 0 188 (0.00) 178 (0.00) 568 (0.00)

Note: The estimates are based on a panel of foreign direct investments (FDI), foreign bond investments (FBI), and foreign equity investment (FPI).P-values are in parentheses. The sample is 1988–2000. “a” is the Breusch–Pagan Lagrangian (BPL) multiplier test for the presence of randomeffects.

relative risk of the economy they plan to invest in. In order to capture this second order effect we proceed to theestimation of the following equation:

log(FPIi j,t ) = β1 log(GDPi,t ) + β2 log(GDP j,t )

+ β3 log(distancei j ) + β4(macro volatilityi j,t )

+ β5(Adjacency Dummiesi j ) + constant + ui + εi t . (13)

The equation above is estimated with iterated Feasible Generalized Least Squares (FGLS hereafter), whichconverges to the Maximum Likelihood Estimator in this case, correcting also for heteroscedasticity. We also perform anumber of robustness tests by including additional dummies that account for region-specific characteristics to concludethat there are no significant departures from the results reported in the next section.

4. Results

Results of the standard gravity equation are depicted in Table 1. All coefficients turn out to be with the right signand are statistically significant at the 5% level.

The risk augmented gravity equations for FBI, FPI and FDI are presented in Table 2. What is of interest here isthat once we control for monetary macroeconomic volatility, distance is relevant only for direct flows whilst moneymarket rate volatility gains significant only at the 10% level. With respect to portfolio equity flows, the only significantdeterminant that emerges is the size of the US economy, and although money market rate volatility of the foreigncountry and foreign exchange volatility deter equity investments (as supported in Goldstein and Razin [12]), they failto achieve significance even at the 10% level.

On the other hand, the volatility augmented gravity equation for transactions in bonds reveals an interesting pattern!Financial development, as captured by domestic and foreign GDP per capita, is significant at the 1% level, and on topof that, all monetary volatility variables appear strongly significant at the 1% level! All three of them exert a strongpositive effect on transactions in bonds, a result that manifests a pecking order of capital flows when the monetaryside of an economy becomes more turbulent. This outcome however is in odds with what Goldstein and Razin [12]predict. To further investigate the above result, we next regress the ratio of FDI to FPI with the volatility augmentedgravity variables.

As it is evidently presented in Table 3, all “traditional” gravity variables turn out as expected and are significantas well. In addition, all the “volatility” variables come out with the right sign with money market rate volatility beingsignificance at the 10% level. This gives support to the theoretical argument above that as the probability of a liquidityshock increases worldwide, investors switch to FDI relative to FPI and not the other way around.

The picture is quite different with respect to the ratio of FDI to FBI in the same table (column 2). In the firstplace, it seems awkward that FDI/FBI shows a completely disparate pattern than that of FDI/FPI. Yet, this outcomeis consistent with Goldstein and Razin [12], who get the same behavior of bond and portfolio equity investmentsrelative to direct investments, even though they do not model bond investments explicitly. The negative relationship


Table 2Risk augmented gravity equation estimates

Coeff \ Dep Var Yi t = a + B′ Xi t + ui + εi tFDI FEI FBI

Dist −1.297 (0.10) 0.002 (0.84) 1.289 (0.65)Foreign GDP 2.466 (0.00) 0.018 (0.16) 6.883 (0.00)US GDP 3.439 (0.00) 0.054 (0.00) 13.27 (0.00)Money rate volatility 0.937 (0.09) −0.001 (0.83) 1.99 (0.03)Inflation volatility 0.040 (0.02) 0.000 (0.17) 0.098 (0.04)Forex volatility −0.12 (0.32) −0.000 (0.82) 0.00 (0.63)

R2 all 0.4738 0.3399 0.7804Wald − χ2 113.55 (0.00) 76.12 (0.00) 513.16 (0.00)aBPL test:H0 : Var(u) = 0 324 (0.00) 419 (0.00) 699 (0.00)

Note: The estimates are based on a panel of foreign direct investments (FDI), foreign bond investments (FBI), and foreign equity investment (FEI).P-values in parentheses. The sample is 1988–2000. “a” is the Breusch–Pagan Lagrangian (BPL) multiplier test for the presence of random effects.

Table 3Estimates of capital flows’ ratios

Coeff \ Dep Var Yi t = a + B′ Xi t + ui + εi tFDI/FEI FDI/FBI FEI/FBI

Dist −1.307 (0.06) −2.32 (0.32) −1.276 (0.65)Foreign GDP 2.440 (0.00) −5.187 (0.00) −6.866 (0.00)US GDP 2.758 (0.00) −9.236 (0.00) −13.22 (0.00)Money rate volatility 0.939 (0.08) −0.919 (0.08) −1.99 (0.03)Inflation volatility 0.033 (0.28) 0.034 (0.28) −0.098 (0.04)Forex volatility −0.012 (0.32) −0.012 (0.32) −0.007 (0.68)

R2 all 0.5266 0.6288 0.7811Wald − χ2 90 (0.00) 238 (0.00) 515 (0.00)aBPL test:H0 : Var(u) = 0 248 (0.00) 595 (0.00) 698 (0.00)

Note: The estimates are based on a panel of foreign direct to foreign equity investments (FDI/FBI), foreign direct to foreign bond investments(FDI/FBI), and foreign equity to foreign bond investment (FEI/FBI). P-values in parentheses. The sample is 1988–2000. “a” is the Breusch-PaganLagrangian (BPL) multiplier test for the presence of random effects.

between monetary volatility and FDI/FBI ratio suggests that investors start transacting more in bonds than in directinvestment when the probability of getting a bad liquidity shock increases. At the same time, the ratio of FPI/FBIdecreases regarding the monetary volatility variables, as displayed in the third column of Table 3.

Noting now the following relationship:(FDIFEI

)∗

(FEIFBI

)=

(FDIFBI

)(14)

we observe that if we know that(FDI

FEI

)↑,

( FEIFBI

)↓ and

(FDIFBI

)↓ then it must be true that:

↑

(FDIFPI

)<

(FPIFBI

)↓ . (15)

This is an indication that the decrease in the ratio of portfolio equity to bond flows is greater than the increase inthe ratio of direct investments to equity flows when the volatility of the liquidity shock increases. Intuitively, as theprobability of getting a bad liquidity shock increases, investors switch to safer assets but with a pecking order: theyseem to damp equities for more bonds and more direct investments!


5. Robustness

A core issue when using the TIC data on long-term securities is that it captures portfolio transactions between USand foreign residents, but apparently US residents also acquire stocks through merger-related stock swaps.12 When acompany, based on a foreign country (from the US perspective), acquires a US firm, one form of financing the deal isan exchange of equity instead of direct financing in which shareholders of the target (US) firm are given stocks in theacquiring (foreign) firm. Such acquisitions of foreign stocks are not reported to the TIC system but the acquisition ofthe US company is recorded as a foreign direct investment in the US.

If the acquisition of foreign stocks through swaps results in a portfolio with higher risk due to the inclusion offoreign equities or if US residents are reluctant to hold foreign stock for psychological or other reasons,13 thenUS residents will subsequently sell foreign equities to rebalance their portfolios, and such sales are reported to theTIC system. The shortcoming is that since the TIC system does not record the initial acquisition, but does capturesubsequent sales, measures of stock swaps or foreign direct investments must be included in any analysis of capitalflows or asset holdings.

However, this form of financing cross-country mergers and acquisitions is a recent strategy that evolved inimportance in 1998 and 1999. Some of the most important mergers and acquisitions took place in these years andinclude that of Daimler Chrysler, BP Amoco, and Airtouch Vodafone. Prior to 1998 there was only one deal thatinvolved a substantial exchange of stocks, the 1989 Beecham–SmithKline merger. Warnock and Mason [31] andBrooks, Edison, Kumar and Slok [6] document that equity swaps are insignificant and do not entail any large bias inthe reported TIC data.

Empirical estimates might also be extremely sensitive to outliers that drive the results. We repeated all theestimations by excluding the Hadi outliers from the sample and there is no departure from the results reported here.A more pervasive shortcoming of our analysis is the endogeneity issue associated with capital flows and country-specific volatility. The intuition is that an investor will anticipate the fact that a country is volatile and will chooseex ante the investment strategy she will follow. Table 4 reports the three stage least square estimates for the FDI/FPIratio. Instruments for money market rate volatility are foreign exchange rate volatility and inflation volatility as wellas four lags of the dependent variable. If there was a feedback rule of the choice between FDI and FPI, then laggedvalues of the dependent variable would enter with a significant sign. However, there is no empirical evidence that thishappens.

6. Conclusions and final remarks

Within a highly integrated environment and the recently evidenced financial crises, the composition and directionof capital flows magnetizes the interest not only of international economists but also of political agents. Thoughthe majority of studies addresses the observed volatility of capital flows [2] and discusses problems evolving fromasymmetric information, no empirical work has been conducted thus far on the effects of macroeconomic variables’volatility on capital structure. The present paper fills in this vacuum and hence enriches existing literature by testingfor the information-based approach of capital flows. Constructing a simple econometric framework where country-specific random effects and macroeconomic monetary volatility are directly linked to the probability distributionof foreign liquidity shocks hitting an international investor, we hereby document a shift in foreign capital structurewhenever such liquidity shocks occur. A “volatility augmented” gravity equation is then estimated to check the validityof theoretical predictions. Overall, there is empirical evidence of a pecking order of foreign investment choices.As the probability of getting a bad liquidity shock to sell early, due to firm specific random shocks and monetarymacroeconomic volatility of a country, increases, agents switch to safer assets than portfolio equities. They seem todamp equities for bonds and direct investments.

The main implication and value added stemming from this analysis is that asymmetric information problems arenot only related to the distance between any two countries, as demonstrated in the literature, but also to the volatility ofmonetary macroeconomic aggregates of the host country. What is more, obtained results suggest that even when two

12 Richard Portes kindly pointed out the inefficiency of the TIC data and generated this discussion.13 For an excellent study of risk attitudes and portfolio choice, see Charness and Gneezy [7].


Table 43SLS estimates of FDI/FEI ratio

Coeff \ Dep Var Yi t = a + B′ Xi t + εi tFDI/FEI Money rate vol

Dist −0.618 (0.10)Foreign GDP 1.292 (0.01)US GDP 3.280 (0.02)Money rate volatility 13.890 (0.00)Instruments

Inflation volatility −0.007 (0.54)Forex volatility −0.001 (0.27)lag 0. FDI/FEI −0.020 (0.77)lag 1. FDI/FEI 0.036 (0.33)lag 2. FDI/FEI 0.018 (0.36)lag 3. FDI/FEI 0.213 (0.30)aWald −χ2 26.63 (0.000) 16.78 (0.01)

Note: The estimates are based on a 3SLS technique of foreign direct to foreign equity investments (FDI/FEI). P-values in parentheses. The sampleis 1988–2000. “a” is the Wald test for the joint significance of all coefficients included in the model.

countries are close enough, the asymmetric information problem is not alleviated once we encounter macroeconomicrisk attitudes related to monetary aggregates. Our results, though not exhaustive, show that the increased transactionsin direct investments and bonds relative to equity, in the presence of an increase in the probability of foreign liquidityshocks, is a sign of macroeconomic and capital-market institutional weaknesses in the host country [37,12]. This isa first attempt to develop an empirical road map in order to investigate an information-based model of internationalcapital structure. Much more work is needed to better understand the structure and direction of international flows inthe empirical as well as theoretical front.

Future research may relax the assumption of random corporate characteristics. An interesting extension wouldbe to incorporate firm traits in the present framework to encounter for the effect of this component’s volatility inthe composition of international capital flows. Including a larger number of countries, and an extended time span,would definitely illuminate the argumentation developed here and would allow for a possible differentiated attitude ofadvanced vs. developing countries in monetary risk!

Appendix A. Data sources

Capital Flows: The data series is based on submissions of monthly TIC Form S, “Purchases and Sales of Long-Term Securities by Foreigners.” These reports are mandatory and are filed by banks, securities dealers, investors, andother entities in the US who deal directly with foreign residents in purchases and sales of long-term securities (equitiesand debt issues with an original maturity of more than one year) issued by US or foreign-based firms (available athttp://www.treas.gov/tic).

Foreign direct investment (FDI): is defined as an investment involving a long-term relationship and reflecting alasting interest and control of a resident entity in one economy (foreign direct investor or parent enterprise) in anenterprise resident in an economy other than that of the foreign direct investor (FDI enterprise or affiliate enterpriseor foreign affiliate) [2]. FDI implies that the investor exerts a significant degree of influence on the management of theenterprise resident in the other economy. Such investment involves both the initial transaction between the two entitiesand all subsequent transactions between them and among foreign affiliates, both incorporated and unincorporated.FDI may be undertaken by individuals as well as business entities. Obtained from UNCTAD, Division on Investment,Technology and Enterprise Development (DITE), FDI Statistics.

Real per capita GDP: obtained from Penn World Table 6.1.Inflation: Consumer Price Index changes obtained from IFS CD-Rom.Money Market Rate: Is the rate on short-term lending between financial institutions obtained from IFS CD-Rom.Exchange Rates: Bilateral exchange rates obtained from the IFS CD-Rom.Distance and Adjacency Dummies: Available at http://www.nber.org/˜wei (see also Appendix B).

http://www.treas.gov/tic

http://www.nber.org/~wei


Appendix B. Robustness

The following variables were used for robustness check:Distance: in kilometers between the capital citiesD1ADJ = 1 if the two share common land borders and 0 otherwiseD2AP = 1 if both are in Asia PacificD2NA = 1 if both are in North AmericaD1AP = 1 if either exporter or importer is in Asia PacificHSA1 = 1 if exporter/importer is Hong Kong/SingaporeWH2 = 1 if both are in Western HemisphereMOO = a numerical analogue for the Moody’s credit rating scale.14

For a measure of country-specific risk we calculate the standard deviation of the monthly money market rate,bilateral exchange rate and inflation rate in foreign country j in year t :

SDV j t =

√√√√ 1N − 1

N∑k=1

(X jk − X̄k)2 (16)

where X̄ t is the monthly average for year t , X jk is the value of the monetary variable of country j in month k andN = 12.

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