metadata: authors: 0: author: Calderon,Cesar
1: author: Loayza,Norman V.
2: author: Serven,Luis
count: World
docna: 0: docna: Do capital flows respond to risk and return?
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Macroeconomic Management,Fiscal & Monetary Policy,Consumption
teratopic: Macroeconomics and Economic Growth
abstracts: cdata!: This paper explores empirically the role
            of risk and return in the observed evolution of net foreign
            asset positions of industrial and developing economies. The
            paper adopts a dynamic approach in which investors'
            portfolios adjust gradually to their long-run equilibrium,
            defined by a standard Tobin-Markowitz framework. The
            parameters characterizing the long-run equilibrium are
            estimated using data on foreign assets and liabilities of a
            large number of industrial and developing countries spanning
            the period from 1965 to 1997. The paper employs a dynamic
            panel estimation procedure allowing for unrestricted
            short-run heterogeneity across countries, using the pooled
            mean group estimator recently developed by Pesaran, Shin,
            and Smith (1999). The empirical results lend considerable
            support to the model when applied to countries with low
            capital controls and/or high and upper-middle income. The
            results for countries with either high capital controls or
            low per capita income are less supportive of the stock
            equilibrium model for net foreign asset positions.
repnme: repnme: Do capital flows respond to risk and return?
display_title: Do capital flows respond to risk and return
listing_relative_url: /research/2003/05/2390970/capital-flows-respond-risk-return
topicv3: Capital Account m1326277 1134,Current Account m1326278 1135,Balance of Payments m1326965 441,Macroeconomic and Structural Policies
            m1327380 439,Investment Entry m1504647 2544
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth
origu: Development Research Group (DECRG)
no_of_pages: 52
author_keys: 0: author_key: 166739
1: author_key: 97109
2: author_key: 93728
model_extractions: POLICY RESEARCH WORKING PAPER **_3059_**

# **Abstract**

This paper explores empirically the role of risk and return in the observed evolution of net foreign asset positions of industrial and developing economies. The paper adopts a dynamic approach in which investors' portfolios adjust gradually to their long-run equilibrium, defined **by** a standard Tobin-Markowitz framework. The parameters characterizing the long-run equilibrium are estimated using data on foreign assets and liabilities of a large number of industrial and developing countries spanning the period from **_1965_** to **1997.** The paper employs a dynamic panel estimation procedure allowing for unrestricted short-run heterogeneity across countries, using the pooled mean group estimator recently developed **by** Pesaran, Shin, and Smith **(1999).** The empirical results lend considerable support to the model when applied to countries with low capital controls and/or high and upper-middle income. The results for countries with either high capital controls or low per capita income are less supportive of the stock equilibrium model for net foreign asset positions.

This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the group to understand international capital flows. Copies of the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Tourya Tourougui, room **MC3-301,** telephone 202-458-7431, fax **202-522-** **_3518,_** email address ttourougui@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at nloayza@worldbank.org or Iserven@worldbank.org. May **2003.** _(45_ pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings_ **_out_** _quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced **by** Partnerships, Capacity Building, and Outreach
# **Do CAPITAL FLOWS RESPOND**<sup>**TO RISK ANDRETURN?***</sup>

C6sar Calder6n Central Bank of Chile Norman Loayza The World Bank Luis Serven The World Bank

**JEL** classification codes: **F32, F37, GIl**

This work was supported **by** the Latin<sup>American Regional StudiesProgram of theWorld Bankand the</sup> Reseach Program of the Central Bank of<sup>Chile.Wearegrateful to Gian Maria Milesi-Ferreti,PhilLane,</sup> Klaus Schmidt-Hebbel, Raimundo<sup>Soto,andAlanStockman for valuable discussions.Wealsothank</sup> participants at the **1999** Latin America<sup>Meetings of theEconometricSociety,the</sup><sup>**1999 LACEA**Meetings</sup> and the 2000 Winter Camp on International<sup>Financeforvaluablecomments. GeorgeMonokroussos and</sup> Rashmi Shankar provided excellent<sup>research assistance.</sup> Corresponding author. Address:<sup>TheWorldBank,</sup><sup>**1818**HStNW,Washington</sup><sup>**DC**20433.E-mail address:</sup> _Iserven(a)lworldbank.org._<sup>Phone 202-4737451</sup>
**Do CAPITAL FLOWS RESPOND TO**<sup>**RISK AND.RETURN?**</sup>

# **1. INTRODUCTION**

One of the major puzzles in international<sup>economicsis the failure of standard portfolio</sup> models to explain the observed patterns<sup>of cross-countrycapitalallocation.Thesearch for</sup> solutions to this puzzle has<sup>attracteda great dealof theoreticalandempirical work.</sup><sup>**1**Most of this</sup> effort has focused on explaining<sup>the 'home-bias'effect,according to whichdomesticinvestors</sup> favor domestic<sup>over foreignasset holdings.As the literature hasamply</sup> disproportionately documented, individuals do not appear<sup>to do agood</sup><sup>**job**at diversifying risks acrosscountries:</sup> they hold too little of their<sup>wealth in foreign assets,much less than predicted</sup><sup>**by**conventional</sup> risk-return portfolio equilibrium<sup>models.2</sup> Rather than attempting to explain<sup>the well-documenteddivergencebetween observed</sup> portfolio shares and those<sup>predicted</sup><sup>**by**theory,thispaper examines the empiricalvalidityof a</sup> weaker theoretical prediction, namely<sup>thatinternational asset positions should systematically</sup> respond to risk and return<sup>conditions.Thus,theaim of the paper isto check whether</sup><sup>**--**and how</sup> much **--** international capital<sup>flowsreflectmarket incentives,and if the effectsof thelatter are</sup> similar across the world or there<sup>aresignificant differences among countriesand/or specific</sup> country groups. In following<sup>this positiveapproach, we implicitlytakeas given the'home bias'</sup> of international portfolios **-**<sup>i.e.,weallow for unobservedcountry-specificcharacteristics that</sup> may affect net foreign<sup>asset(henceforth</sup><sup>**NFA)**positionsand,more generally,we allow for</sup> heterogeneity across countries<sup>in theresponseof</sup><sup>**NFA**positionsto risk and return fundamentals.</sup> The paper's framework is guided<sup>**by**a Tobin-Markowitzmodel of portfolio</sup> diversification in which the share<sup>of domestic investors'wealthallocatedto foreign assets</sup> depends on four factors: investment<sup>returnsinthehomecountry relativeto the restof the world,</sup> investment risk in the home country<sup>relative to the rest of the world, the degreeof co-movement</sup> between investment returns at home<sup>and abroad,and the ratioof foreign-ownedto domestic-</sup> owned wealth.

> **1** Lewis **(1999)** provides a comprehensive<sup>overview of this literature.</sup>

> **2** See, for example, French and Poterba **(1991)** for the case of intemational equity portfolios. Tesar and Werner **(1995)** show that the same<sup>puzzle arises with bonds.</sup>

2
> This framework characterizes long-run portfolio equilibrium. However, costs and frictions

> to instantamous portfolio reallocation **-** arising from sources such as investors' imperfect

> information, congestion effects or investment adjustment costs **-** may drive a wedge between short-run and long-run portfolio equilibrium.<sup>3</sup> Further, these frictions, and hence portfolio

> dynamics, may differ across countries. The paper's empirical analysis focuses on the estimation of the long-run portfolio equilibrium condition, while allowing for unrestricted cross-country heterogeneity in the short-run dynamics.

The paper extends previous literature along three dimensions. First, it builds on a recent strand of the literature that adopts an international portfolio equilibrium approach to the analysis of the current account (Ventura 2002).4 Our paper shares with this literature the emphasis on risk and adjustment costs as essential ingredients for explaining the observed patterns of international asset portfolios. However, that literature has focused primarily on the impact of wealth changes on capital flows (what has been termed the _portfolio growth_ effect). In contrast, the present paper also brings into focus the determinants of international investors' portfolio shares, for given levels of their wealth (the _portfolio rebalancing effect)._

> Second, the paper implements empirically the portfolio diversification model using a

> comprehensive data set on foreign assets and liabilities that covers a large number of developing and industrial countries and spans the years from **_1965_** to **1997.** Importantly, the data encompass not only industrial economies, which have been the focus of previous empirical literature, but also a large number of emerging markets and developing economies. Using this information, we can assess the empirical robustness of the portfolio model across different country groups and

> alternative measures of risk and return.

Third, the paper follows a novel econometric approach to the estimation of the long-run portfolio equilibrium condition in a heterogeneous dynamic panel setting, using the Pooled-Mean Group estimator recently developed **by** Pesaran, Shin, and Smith **(1999).** This approach combines the efficiency gains from restricting long-run parameters to be the same across

> See Bacchetta and van Wincoop **(1998)** for a theoretical discussion of portfolio dynamics arising from these and other sources.

> _4_ Along similar lines, the paper's portfolio equilibrium approach to capital flows also brings it close to a strand of the literature on 'current account sustainability' that underscores the role of international investors' portfolio choices in shaping the sustainable current account (see Mann 2002 for references). **By** shedding light on the factors that shape

> international portfolio diversification and its time path, the analysis in this paper could be readily adapted to identify

> current-account trajectories consistent with portfolio equilibrium.

**3**
countries (the units in the panel) with<sup>theflexibility and consistencygainsof country-specific</sup> short-run adjustment. Further,<sup>theapproachallows formaltesting of the long-run pooling</sup> restrictions imposed **by** the model<sup>**-**i.e., thehomogeneity across countries of the parameters</sup> describing the long-run portfolio<sup>equilibriumcondition.</sup> The paper's plan is as follows.<sup>Section2describes the analyticalframeworkand presents</sup> the econometric strategy for estimation<sup>of the long-runrelationship implied</sup><sup>**by**themodel.</sup> Section **3** briefly summarizes<sup>themainfeaturesof the</sup><sup>**NFA**dataand themeasuresof investment</sup> returns and risks used in the empirical<sup>analysis.Section 4 presents theempirical results from</sup> estimation of the model for<sup>variousgroupsof countries. The modelisfirst implementedon the</sup> full country sample, and then<sup>separatelyon countrygroups that differ in per capitaincome level</sup> and restrictions to international<sup>portfoliodiversification.Section</sup><sup>**_5_**concludes.</sup>

# **2. METHODOLOGY**

**2.1 A portfolio-diversification**<sup>**approach to external asset positions**</sup>

Our analytical framework<sup>follows recentliteratureunderscoring therole of investment</sup> risk and adjustment costs in the allocation of agents' wealth<sup>between domesticand foreignassets,</sup> and thus in the determination<sup>of capitalflows (Ventura2002).This literatureshowsthat those</sup> two ingredients are needed to<sup>reconciletheoreticalpredictionsand observedfactson the</sup> dynamics of countries' asset<sup>portfolios.</sup> Specifically, we adopt<sup>a portfolio-diversificationapproachaccording to whichexternal</sup> asset positions are driven **by**<sup>portfolio equilibrium inthelong runand</sup><sup>**by**thedynamicforces</sup> shaping asset reallocation<sup>in theshortrun.Long-runequilibriumobtains when domesticand</sup> foreign investors achieve the desired<sup>allocationof their asset portfolioacrosscountries.</sup> However, imperfections and<sup>frictionsinrealand financialmarkets mayprevent theinstantaneous</sup> achievement of the optimal portfolio.<sup>Short-runexternalequilibriumis then given</sup><sup>**by**the</sup> adjustment path towards investors'<sup>long-run equilibrium portfolio.</sup> In our framework, the optimal<sup>portfolioallocationfollowsalongthe lines of the standard</sup> Markowitz-Tobin model of mean-variance<sup>investors.As is wellknown, the modelcan be derived</sup> under fairly standard assumptions<sup>fromintertemporaloptimization</sup><sup>**by**forward-looking, risk-</sup> averse agents. Such procedure<sup>can be shownto yield an optimal saving/consumptionplan</sup> _4_
characterized **by** the permanent income hypothesis, and an optimal allocation of wealth between domestic and foreign assets characterized **by** mean-variance portfolio optimization. **_5_**

> The key property of mean-variance investors is that the desired share of each asset in their total wealth depends only on the distribution of asset returns and not _directly_ on the level of wealth.<sup>6</sup> In our context of international diversification, the optimal portfolio share allocated to assets in a given country can be divided into two pieces, namely, the 'speculative' component and

> the 'minimum variance' component (using the terminology in Adler and Dumas **1983).** An increase in mean returns in the country leaves unaffected the 'minimum variance' piece of the

> portfolio but raises the 'speculative' component and thus leads to an expansion of investors' portfolio share in that country. Analogously, a decrease in the variance of investment returns in the country, holding constant the 'speculative' component, raises the 'minimum variance' piece, thus producing an increase in investors' portfolio share in the country. The same effect occurs when the co- variation of country investment returns with those in the rest of the world decreases -holding constant the 'speculative' component, lower co- variation with the world economy implies that investments in the country provide a better hedge against systemic (world-wide) risk. Formally, let **_A_** represent world assets and _W_ the wealth of world residents. Obviously, **_A_** = _W._ Let **At** represent the assets located in country _i_ and _W_ represent the wealth of country i's residents. The assets located in foreign countries and the wealth of foreigners are respectively represented **by** **_Ap-_** _A-Ai and W_ **_=_** _W-W. Let_ aj; be the share of wealth of country _i's_ residents that they desire to allocate to country i's assets, and let af, represent the share of foreigners'

> wealth that they desire to allocate to country i's assets. Hence when actual and desired portfolio shares coincide, we have that **_Ai =_** _a, W_ **_+_** _aft_ **_Wf._**

As explained above, desired portfolio shares are assumed increasing in the anticipated return of country _i's assets_ relative to those abroad, decreasing in their perceived riskiness relative to external assets, and decreasing in the co-movement of country i's returns with those in the rest of the world. We denote these three factors _REgl, Rif, and_ **_COv,_** respectively. In (long-

> The analytical derivations are standard, and thus for brevity they are not reproduced here. For the general case, the details can **be** found in Merton **(1971).** For an application similar to ours, see Kraay and Ventura (2000).

> **6 Of** course, in the intertemporal optimization framework these results require (standard) simplifying assumptions such as log utility or homothetic preferences and lognormal returns (see Merton **1971).** Even under such conditions, wealth and capital stocks may still affect indiiectly the return characteristics of available assets.

**_5_**
run) portfolio equilibrium, the desired holdings of country i's assets **by** domestic plus foreign residents should be equal to the country's total _existing_ assets; that is, _ail REilf,RT,I,CO,,1f-W_ **_+_** _afi(RE,l ,RIsI ,COif }Wf_ **_= A,_** where the sign over each argument corresponds to the sign of the partial derivative.

It is important to keep in mind that the aj, **0** and at() functions above may embody different preferences of domestic and foreign investors, including differential attitudes towards domestic and foreign assets **-** i.e., home-bias effects (Lewis **1999).** The net foreign asset position of a country is the difference between the wealth owned **by** its residents and the assets located in the country. Therefore, in long-run equilibrium the net foreign asset position of country _i_ will be given **by:**

**_NFA =_** _W_ **_-_** _(a,, W,_ **_+ aft W)_** _(2)_

For given portfolio shares art and _afi,_ equation (2) highlights the dependence of the net foreign asset position on wealth stocks, which is at the core of Kraay and Ventura's (2000) analysis of the current account. Normalizing **by** dividing both sides of (2) **by** country i's wealth, we get:

> **_NF,_** -=1 - a,- - _Wr_

_(3)_

> We can then express equation **(3)** as follows:

**_NF4_ -** **_+ + =_** _f(RE,,fRil,_ **_CO,lf,Wf /W)_** _(4)_

Equation (4) defines the long-run equilibrium relationship resulting from optimal asset allocation across countries. Note that the ratio of net foreign assets to wealth depends on the relative wealth o( domestic residents, even though portfolio shares are themselves independent of wealth.

For empirical implementation we shall take a linear approximation to (4): _(NoFAl +I~I_ **_fS)_** _W P+P'RE*lf+J*lf_ **_+f36_**

**6**
where the stars denote long-run values, and the idiosyncratic intercept _Po_ captures countryspecific factors that we do not model exp licitly.<sup>7</sup>

> We view the above equations as characterizing long-run portfolio equilibrium, and hence expressions _(4)-(5)_ describe the wealth share of net foreign assets in the long run. However, the dynamics of **NFA** along the adjustment path may show temporary departures from these long-run equilibrium rules, reflecting existing constraints to immediate portfolio adjustment.<sup>8</sup> These may arise from various sources (Bacchetta and van Wincoop **1998;** Ventura 2002): (i) investors' imperfect information (e.g., gradual learning about the state of the world, or about the permanence of reforms which affect asset returns but may initially suffer from imperfect credibility); (ii) congestion effects, such as increasing marginal costs to foreign investment due for example to its use of internationally immobile labor inputs; (iii) costs of adjusting the capital stock **-** such as investment irreversibility **--** that make investment respond sluggishly to aggregate disturbances (Caballero **1998,** Dixit and Pindyck **1996).** While we do not model explicitly such dynamic effects here, in our empirical implementation we take them into account **by** employing a suitably expanded version of **_(5)_** allowing for lagged effects of risk, return and relative wealth. This is discussed in the next subsection

2.2 Econometric Estimation

> Empirical implementation of the model outlined in the previous section on a large crosscountry time-series sample poses two main issues. First,- the model defines a long-run relationship between the ratio of net foreign assets, wealth shares, and expected returns and risks. However, given the imperfections in international financial and factor markets, stock equilibrium does not hold at every point in time but is achieved gradually in the long run. Therefore, in the empirical analysis, the process of short-run adjustment must complement the long-run equilibrium model.

Second, it seems reasonable to assume that countries can differ regarding the market imperfections and barriers to portfolio reallocation that govern short-term dynamics **-** and perhaps even in the parameters characterizing the long-run equilibrium. Thus, we must allow for parameter heterogeneity across countries. We deal with each of these two issues in turn.

> For example, it could reflect the effects of home bias on long-run net foreign asset holdings.

> **8** Kraay and Ventura (2000) underscore the discrepancies between the short- and long-run patterns of change of **NFA.** Ventura (2002) stresses the need to take into consideration adjustment costs to account for these differences.

**_7_**
# **_Single-country estimation_**

The challenge we face is to estimate<sup>long-and short-run relationshipswithout being able</sup> to observe the long- and short-run<sup>componentsof the variables involved.Overthelast decadeor</sup> so, a booming cointegration<sup>literaturehas focusedon the estimationof long-runrelationships</sup> among **I(1)** variables (Johanssen<sup>**_1995,_**Phillipsand Hansen</sup><sup>**1990).**From thisliterature,two</sup> common misconceptions have been<sup>derived.Thefirst oneis that iong-runrelationshipsexist</sup> _only_ in the context of cointegration<sup>among integratedvariables.Thesecondone isthat standard</sup> methods of estimation and inference<sup>areincorrect.</sup>

**A** recent literature, represented<sup>in Pesaran andSmith</sup><sup>**_(1995),_**Pesaran</sup><sup>**(1997)**and Pesaran</sup> and Shin **(1999),** has argued against<sup>both misconceptions. These authors showthat simple</sup> modifications to standard methods<sup>can render consistentand efficient estimatesof the parameters</sup> in a long-run relationship between<sup>both integrated and stationary variablesand thatinferenceon</sup> these parameters can be conducted<sup>using standard tests.Furthermore,these methods avoid the</sup> need for pre-testing and order-of-<sup>integration conformabilitygiven that theyare valid whether the</sup> variables of interest are 1(0) or<sup>I(1).The main requirementsfor the validity of this methodology</sup> are that, first, there exist a long-run<sup>relationshipamong the variablesof interest and,second, the</sup> dynamic specification of<sup>themodel be augmentedsuch that theregressorsarestrictly exogenous</sup> and the resulting residual is serially<sup>uncorrelated. 9Pesaran and co-authorslabel this the</sup> "autoregressive distributed lag (ARDL)<sup>approach"to long-runmodeling.AppendixBpresents</sup> an illustration of the main assumptions<sup>and propertiesof the ARDL approach.</sup>

In order to comply with the requirements<sup>for standard estimationand inference, weembed</sup> the long-run portfolio equilibrium<sup>condition</sup><sup>**_(5)_**intoan</sup><sup>**ARDL(p,q)**model.In error-correction</sup> form, this can be written as<sup>follows:</sup>

**9** It is worth noting that the assumption<sup>of a unique long-runrelationship underlies implicitlythe various single-</sup> equation based estimators of<sup>long-runrelationships commonly found in the cointegration literature. Without such</sup> assumption, these estimators would<sup>at best identifysome linear combinationof all the long-runrelationships present</sup> in the data.

**8**
where <p is the speed of adjustment, i7, is a time-varying disturbance and the term in square brackets in the second. line contains the long-run equilibrium condition **_(5)._** As just discussed, it is critical that the order of the ARDL process be appropriate. Pesaran and Shin **(1999)** recommend a two-step procedure, whereby the lag order of the ARDL is first selected using a consistent information criterion, and then the corresponding error-correction model is estimated and tested **by** standard methods. As explained later, we use the Schwartz-Bayesian Criterion **(SBC)** to select appropriate values ofp and **q** in equation **(6)** on a country-by-country basis.

# **_Multi-country estimation_**

Our empirical samples below are characterized **by** time-series (T) and cross-section **(N)** dimensions of roughly similar magnitude. In such conditions, there are a number of alternative methods for multi-country estimation, which allow for different degrees of parameter heterogeneity across countries. At one extreme, the fully heterogeneous-coefficient model imposes no cross-country parameter restrictions and can be estimated on a country-by-country basis **--** provided the time-series dimension of the data is sufficiently large. When, in addition, the cross-country dimension is also large, the mean of long- and short-run coefficients across countries can be estimated consistently **by** the unweighted average of the individual country coefficients. This is the mean group **(MG)** estimator introduced **by** Pesaran, Smith, and Im **(1996).** At the other extreme, the fully homogeneous-coefficient model requires that all slope and intercept coefficients be equal across countries. This is the simple pooled estimator.

> In between the two extremes, there are a variety of estimators. The dynamic fixed effects estimator restricts all slope coefficients to be equal across countries but allows for different country intercepts. The pooled mean group (PMG) estimator, introduced **by** Pesaran, Shin and Smith **(1999),** restricts the long-run coefficients to be the same across countries but allows the

> short-run coefficients (including the speed of adjustment) to be country specific. The PMG estimator also generates consistent estimates of the mean of short-run coefficients across countries **by** taking the unweighted average of the individual country coefficients (provided that the cross-sectional dimension is large).

The choice among these estimators faces a general trade-off between consistency and efficiency. Estimators that impose cross-country constraints dominate the heterogeneous estimators in terms of efficiency if the restrictions are valid. If they are fa lse, however, the

**9**
restricted estimators are inconsistent. In particular, imposing invalid parameter<sup>homogeneityin</sup> dynamic models typically leads to downward-biased estimates of<sup>the</sup> speed<sup>of adjustment</sup> (Robertson and Symons **1992,** Pesaran and Smith **1995).** For our purposes, the pooled mean group estimator offers the best<sup>availablecompromise</sup> in the choice between consistency and efficiency. This estimator<sup>is particularlyusefulwhen,as</sup> in our case, the long run is given **by** country-independent equilibrium conditions<sup>while the short-</sup> run adjustment depends on country characteristics **--** such as,<sup>e.g., financial development and/or</sup> relative price flexibility. Furthermore, the PMG estimator is sufficiently<sup>flexible to allowfor</sup> long-run coefficient homogeneity over only a subset of variables and/or<sup>countries.</sup> Therefore, we use the PMG method to estimate a long-run relationship that is common across countries (i.,e, **_fik'_ =P** for all **ij** and _k=1,...,4)_ while allowing for unrestricted country heterogeneity in the adjustment dynamics. The interested reader is referred<sup>to Pesaran,Shinand</sup> Smith **(1999)** where the PMG estimator is developed and compared with<sup>the</sup><sup>**MG**estimator.</sup> Briefly, the PMG estimator proceeds as follows. The estimation<sup>of the long-run coefficientss</sup> done jointly across countries through a (concentrated) maximum likelihood<sup>procedure.Thenthe</sup> estimation of short-run coefficients (including the speed of adjustment<sup>**9),**country-specific</sup> intercepts **_Poi,_** and country-specific error variances is done on<sup>a country-by-countrybasis, also</sup> through maximum likelihood and using the estimates of the long-run<sup>coefficientspreviously</sup> obtained.'o An important assumption for the consistency of our PMG estimates<sup>is the independence</sup> of the regression residuals across countries. In practice, non- zero error<sup>covariancesusuallyarise</sup> from _omitted_ common factors that influence the countries' ARDL processes.<sup>Weseek to</sup> eliminate these common factors and, thus, ensure the independence<sup>condition through two means.</sup>

**10** The comparison of the asymptotic properties of PMG and<sup>**MG**estimates canbe put also in termsof the general</sup> trade-off between consistency and efficiency noted in the text. **If**<sup>the long-run coefficientsarein fact equalacross</sup> countries, then the PMG estimates will be consistent and efficient, whereas<sup>the</sup><sup>**MG**estimates will only be consistent.</sup> **If,** on the other hand, the long-run coefficients are not equal<sup>acrosscountries, thenthe PMGestimates will be</sup> inconsistent, whereas the **MG** estimator will still provide<sup>aconsistentestimate ofthemeanof long-runcoefficients</sup> across countries. The long-run homogeneity restrictions can be tested using<sup>Hausman orlikelihood ratio teststo</sup> compare the PMG and **MG** estimates of the long run coefficients. In<sup>turn, comparisonof the smallsample properties</sup> of these estimators relies on their sensitivity to outliers.<sup>In small samples (low T and</sup><sup>**N),**the</sup><sup>**MG**estimator, being an</sup> unweighted average, is very sensitive to outlying country estimates<sup>(for instancethose obtained withsmallT).The</sup> PMG estimator performs better in this regard because it produces<sup>estimatesthat are similar</sup><sup>_to weighted_averagesof</sup> the respective country-specific estimates, where the weights are given according<sup>to</sup> their<sup>precision(that is, the</sup> inverse of their corresponding variance-covariance matrix).

**10**
First, as explained below, we construct the indices for return and risk in a way such that each observation represents the value for a country/year relative to the corresponding mean for the whole world in all time periods. Second, we allow for time-specific effects in the estimated regression; this is equivalent to a regression in which each variable enters as deviations with respect to the cross-sectional mean in a particular year.

# **3. DATA**

# **3.1 NFA** and Wealth

The cornerstone of our data is a set of wealth, foreign asset and foreign liability stocks for a large group of industrial and developing countries spanning the period from the 1960s to the present. Construction of this data set is documented in Kraay _et al._ (2000), so for the sake of brevity here we limit our remarks to a few key issues. The total wealth of country i's residents at time t is defined as _W,_ **_=_** _NFAil +K,_ **_+Gi_** where **NFA** denotes the country's net foreign assets, K is the capital stock, and **G** denotes the Central Bank's gold holdings." In turn, net foreign assets are defined as

**_NFA,, = E,,f, -_ E,1,** **_+_** _Lif.,_ **_- Lft., (8)_**

> where Eifdenotes local residents' holdings of capital abroad, **_Ef_** denotes domestic capital owned

> **by** foreigners, **Liy** are loans issued **by** domestic residents to foreigners (inclusive of foreign

> currency reserves held **by** the domestic Central Bank) and **_Lf,_** are loans from foreigners to domestic residents. **All** quantities are measured in **1995 US** dollars at PPP.

The various wealth components shown above are constructed in two steps. First, we use the limited available information on stocks of these assets to determine an initial value. The second step involves the use of flow data and estimates of changes in the value of these assets to

> ' Thus, we abstract from other components of wealth such as natural resources and human capital.

11
extend the initial stocks forward<sup>and backwardover time. 12Therequired informationis drawn</sup> from a number of standard sources:<sup>initialstocks of domesticcapital are taken from the Penn</sup> World Tables, and combined with<sup>_flow_dataon grossdomestic investmentto build up capital</sup> stock series. For foreign holdings of<sup>domesticequity and domestic holdings of foreign equity, we</sup> rely primarily on data<sup>on stocksand flows of direct and portfolio equity investmentreportedin</sup> the IMF's Balance of Payments Statistics Yearbook.<sup>Finally, stocks of borrowing andlending</sup> are obtained **by** combining<sup>stock data on the debt of developingcountriesreportedin the World</sup> Bank's Global Development<sup>Financewith data ondebt stocksand flowsfrom the Balanceof</sup> Payments Statistics Yearbook.<sup>To account for mismeasurementof capitalflows (and hence</sup> stocks) and in order to capture<sup>unrecordedassets, we augment our measuresof loanassets</sup><sup>**by**</sup> adding to them the cumulative<sup>errors and omissionsof the Balanceof Payments.Putting together</sup> all these pieces, we arrive at estimates<sup>of the wealth stock of the countries in thesample.Using</sup> these estimated wealth stocks,<sup>weconstructtheforeignwealth</sup><sup>**/**domesticwealth ratios of each</sup> country _i._ This procedure yields data<sup>on wealthand itscomponents for a largegroupof industrial</sup> and developing countries.1<sup>3For the empiricalexperimentsin this paper, we restrict the sample to</sup> those economies possessing<sup>a numberof annualobservations in the periodfrom the1960s to the</sup> present sufficient to allow<sup>country-specifictime-series econometricestimation.We setsuch</sup> minimum at 20 (consecutive)<sup>years.Thisresults in anunbalanced panelof</sup><sup>_54_countrieswith time</sup> coverage ranging from<sup>20 to</sup><sup>**32**years.</sup>

**12** The main exceptions to<sup>this procedureare gold holdings,on which completestockdata areavailablefromthe</sup> IMF's International Financial<sup>Statistics, and somespecificitems of loanassetsand liabilities.These are foreign</sup> currency reserves of the central<sup>bank,available from IMF sources,andforeign debt of developingcountries,</sup> available from the World Bank's<sup>GlobalDevelopmentFinance.For theremaining wealth components,complete</sup> stock data are unavailable,<sup>andhencewe rely on the method of cumulatingflows evenfor those countrieswith more</sup> abundant stock data in order<sup>to avoid a potential bias that couldresult from applying different methodsto construct</sup> stocks in different countries:<sup>as longer time seriesof stock dataare available for afew rich countries, using these as</sup> the primary source would<sup>essentially result in different methodsbeing used to construct stocksfor rich and poor</sup> countries. These differences<sup>would then contaminate our inferences regarding, for example, how net foreign assets</sup> vary with wealth.

**13** We begin with a sample of<sup>**98**countries with populationgreater than one million andper capita</sup><sup>**GDP**greater than</sup> **1000 US** dollars at PPP<sup>in</sup><sup>**1990.Of**thesewe discard</sup><sup>**25**countrieswith missing,incomplete,or inconsistentbalance</sup> of payments data. Next,<sup>we alsodrop</sup><sup>**_5_**former socialist economies,whose datawe view asof uncertain reliability,</sup> and a handful **of** developing countries<sup>that haveexperienced prolongedwar episodes over the sampleyears.Finally,</sup> we also remove a few country-year<sup>observationscharacterized</sup><sup>**by**very small (or even negative)estimatesof wealth,</sup> corresponding to countries<sup>with verylarge external debt. We exclude these observationsof doubtful quality</sup><sup>**by**</sup> limiting the sample to<sup>those wherethe ratioof wealth to</sup><sup>**GDP**isgreater than</sup><sup>**_0.5._**</sup>

12
The countries in this sample are admittedly very diverse. As already explained, for some of them return and risk considerations may not be the only or most important factor behind the changes in their net foreign asset positions. Non-market forces **--** related to, for instance, geopolitical interests, humanitarian aid, or developmental purposes **--** may drive to some extent the transfer of capital resources across countries. In addition, some countries use capital and current account restrictions to prevent market forces from 'undoing' net foreign asset positions based on non-market factors. These considerations have the practical implication that the longrun impact of risk and return on net foreign assets may not be the same for all countries (which in turn would imply that the long-run restrictions imposed **by** the PMG estimator would hold only for specific country groupings).

> To explore this issue, we break the overall country sample according to two criteria. First,

> we separate high- and upper-middle-income countries from lower-income countries. Specifically, using the World Bank's World Development Report income classification we form one group

> consisting of **29** industrial, high- income and upper- middle income developing economies, and a matching group of **25** low and lower- middle income developing economies.

> Second, we separate countries that feature low capital controls from those that have high capital controls. The only source of data on this topic with broad time-series and cross-country coverage is the IMF's Exchange Rate Restrictions, which includes qualitative information on

> four kinds of measures that hamper international portfolio diversification. 14 We combine them into a summary measure **by** adding them up, and compute the average for each country over the period **_1965-97._ If** for a country the average is greater than or equal to three (implying that, on

> average, restrictions exist in at least three of the four categories throughout the sample period), we classify the country as having high capital controls. This procedure yields a subsample of 20

> countries with low capital controls and 34 with high capital controls. The countries included in

> each subsample are listed in Table **Al** in the Appendix. An inspection of the list of countries in each group shows that almost all countries with low capital controls belong to the group of high and upper-middle income countries (the exception is Thailand).

> **14** These are: (a) multiple exchange rate practices, **(b)** current account restrictions, (c) capital account restrictions, and **(d)** mandatory surrender of export proceeds.

**13**
Table 1 presents some descriptive statistics on the net foreign asset **/** wealth ratios for the full sample and the various country groups just defined. For the overall country sample, both the mean and median of country averages are negative, an indication of the fact that few countries possess net creditor positions. However, the figures reflect some systematic differences across country groups. As just noted, rich countries, as well as countries with less restricted capital accounts, tend to possess higher NFA/wealth ratios than poor ones. Among higher income countries, as well as countries with moderate capital account restrictions, the median NFA/wealth ratio is below the mean, reflecting the existence of a small group of large creditors. The opposite happens among lower income countries and countries with high capital controls, where the mean is below the median. Dispersion of the **NFA** ratios to wealth is also much higher for low-income than for high- income countries. Finally, NFA/wealth ratios of rich countries (as well as those of countries with low capital account restrictions) show only modest variation over time, while those of low-income countries display a pronounced decline in the 1980s followed **by** a recovery in the 1990s. The group of countries with high capital account restrictions shares this pattern.

**3.2** Measures of return and risk

Apart from wealth ratios, the key explanatory variables in our model of net foreign asset positions are the measures of relative risk and return for each country. In practice, these likely depend on a large variety of underlying variables reflecting relative prices, total factor productivity, transaction costs, property rights, tax regimes and so on. In order to consider as many relevant underlying variables as possible and assess the robustness of the results, we use _three alternative_ sets of indices for the categories introduced in the theoretical discussion namely, expected returns _(REq,),_ perceived risks _(RI[q),_ and co-movement with other countries' returns **_(COil)._** The first and most ambitious set of indices is constructed as a weighted average of several indicators of economic performance, as described below. The second set is exclusively based on the level and variance of real **GDP** growth per capita. The third set focuses on the profitability of the domestic stock market, that is, on the level and variance of stock returns (calculated from constant **U.S.** dollar prices). The first two sets of indices reflect overall economic activity, while the third one accounts mostly for the activity of those firms traded in organized equity markets.

> The motivation for the composite indices is -that they summarize the information provided **by** several macroeconomic variables regarding the performance of investment projects in the

14
country. In contrast, the second and third sets<sup>of indices takean alternative,minimalist approach</sup> as they are based on a single-variable proxy.<sup>The advantage of the compositesetof indicesis its</sup> comprehensiveness while the others' advantage<sup>is</sup> their<sup>simplicity and clarity.Using all of them,</sup> we can examine whether the estimation results<sup>arerobust to changes in returnand risk</sup> measurement. Therefore, their respective results<sup>should be regardedascomplementary.Chart</sup><sup>**I**</sup> summarizes the three alternatives. In all three<sup>cases, co-movementwasmeasured</sup><sup>**by**the</sup> correlation of the relevant return index in a<sup>country and the rest of theworld.'5</sup>

|**Chart1:alternative**|**measuresofreturnandrisk**<br>|
|---|---|
|**Expectedreturn**|**Perceivedrisks**|
|<br>**_1.Co_**|**_mpositeindexb_**<br>|
|Overallproductivity:realpercapita**GDP**<br>th|Generalmacroinstability:standarddeviation<sup>ofreal</sup><br>percapita**GDP**<sup>growth</sup>|
|grow<br>Absenceofpricedistortions:inverseofthe<br>blackmarketPremium**-** i.e.,**_1/(1_**_+bmp)_|<br>Lackofinteational risk-sharing: ratioofexternal<sup>debt</sup><br>todebt +equityexternalliabilities|
|<br>Financialdepth:quasi-liquidliabilities/GDP|Nominalinstability:averageandstandarddeviationof<br>inflation|
|Openness:realimports<sup>plusexports</sup><sup>**/GDP**</sup>|External instability:standard<sup>deviationofreal</sup><br>exchangeratechanges,standard deviationoftermsof<br>tradeshocks,standarddeviationof(imports**+**exports)<br>**/****_GDP_**|
|Institutionalquality:Indicesofgovernance<br>(KaufmnannetaL)and<sup>Gastil civil liberties</sup>|Lowinstitutional quality:negativesofindicesof<br>governance(Kaufmann<sup>et.aL)andGastilcivil liberties</sup>|
|_. <br>Lowtaxburden:negativeofgovernment<br>consumption**/GDP**|<br>Lackoffinancial depth:negativeofquasi-liquid<br>liabilities/GDP|
|<br>Sizeandscaleeconomies:population<sup>size</sup>||
|_2._|_GDP-based_|
|Realpercapita**GDP**growth|Standarddeviationofrealpercapita**GDP**<sup>growth</sup>|
|<br>**_3._**_Sto_|<br>_ckmarket-based_|
|Realstock market return|Standard deviationofrealstockmarketreturn|

Notes: **aAll** standard deviations are computed over the<sup>currentand four preceding years.</sup>

**b** The components listed were aggregated giving<sup>**_50%_**weight to</sup><sup>**GDP**growthin thecaseof return,andits standard</sup> deviation in the case of risk. In **both** cases,<sup>**the**</sup> remaining<sup>componentsreceived equal weights.</sup>

Sources: World Bank World Development Indicators;<sup>IMF InternationalFinancial Statistics;Freedom House;</sup> Kauffmann _et al._ **(1999);** Standard and<sup>Poor's EmergingMarketsDatabase:Shiller</sup><sup>**(1999,**2001).</sup>

> **'** Specifically, we computed rolling correlations<sup>of the return indexina countryand theaverage for the rest of the</sup> world, considering overlapping periods spanning the current<sup>and four preceding years.</sup>

**15**
In the case of the composite indices<sup>of risk and return, the underlyingcomponents were</sup> selected on the basis of both<sup>theirrelevancein previous theoretical and empirical workand their</sup> data availability (see Milesi-Ferreti<sup>and Razin</sup><sup>**1996, 1998;**Easterly, Islam,and Stiglitz</sup><sup>**1999;**and</sup> Rodrik **1999).6** Each individual<sup>componentwasstandardizedusing its respectivepooled(time-</sup> series, cross-section) mean<sup>and variance.Apart fromhomogenizingunits acrossindicators, this</sup> standardization procedure allows<sup>us to control forcommon factorsand yields measures for the</sup> performance of a<sup>country</sup><sup>_relative_to the world.</sup> An issue is how to weigh<sup>the underlyingindicators toconstruct thecompositeindices.</sup> Since there is no obvious<sup>weighingscheme,we decided to favor theindicatorsrelated tothelevel</sup> and variance of per capita<sup>**GDP**growth ratesandassign them a large weightin thereturn and risk</sup> indices, respectively. The following<sup>two reasons justifythis choice.Thefirst ismotivated</sup><sup>**by**the</sup> new growth literature and<sup>arguesthat</sup><sup>**GDP**growth per capita reflectsthe most important</sup> elements of economic policy<sup>and performance.The secondreason is statistical and basedon the</sup> fact that when stock- market<sup>returns areregressedon all of our underlyingindicators,per capita</sup> **GDP** growth takes the lion<sup>share of explained variance.In practice, weassigned a</sup><sup>**50%**weight</sup> to the level and standard<sup>deviation of the per capita</sup><sup>**GDP**growth rate in the returnand risk</sup> indices, respectively; all<sup>remainingvariablesreceived equalweights.</sup><sup>**17**</sup> Combining the risk and return<sup>data with the wealthand foreignasset data,we obtainan</sup> unbalanced panel covering<sup>theyears</sup><sup>**1966-97.**In thecase of the composite and growth-based</sup> indices, the panel includes<sup>_54_countries.In turn,fortheset of indices derivedfrom stock market</sup> returns the sample size is considerably<sup>smaller</sup><sup>**-**just</sup><sup>**33**countries</sup><sup>**--**and with a large</sup> representation of industrial<sup>economies.</sup> Tables A2-A4 in the appendix<sup>show the correlationsbetweenthe composite indices,their</sup> underlying indicators, and<sup>thesingle-variableindices. Also,Tables</sup><sup>**A5-A10**provide descriptive</sup> statistics on the three sets of<sup>indices for selectedsamplesof countries and time periods,in aform</sup> analogous to Table **1.** It<sup>isimmediatelyapparentfrom the tables thathigher- income countries and</sup>

> **16** Note that some variables (such as financial depth and governance quality) enter in both the return and risk measures. The reason is that<sup>they may affectboth thelevel and thedegree of uncertainty of the returnon the</sup> country's assets.

**17** We also constructed indices<sup>giving equal weights toall variables underlying each compositeindex.Furthermore,</sup> we experimented with indices<sup>constructed as principalcomponentsof the underlyingindicators.Theempirical</sup> results (not reported to save<sup>space) were qualitativelysimilar to thoserelated to ourmain weighingscheme.</sup>

**16**
countries with low capital account restrictions typically possess higher returns and lower risks **than.** lower- income countries and countries with high capital account restrictions.

# **4. EMPIRICAL RESULTS**

The main objective of our empirical analysis is to examine whether long-run movements in the ratio of NFA/wealth for a given country are related to long-run changes in the risk, return and wealth characteristics of the country relative to the world, as a portfolio-diversification model would predict. We want to test if a country's NFA/wealth responds negatively to its (differential) mean returns and the ratio of foreign to domestic wealth, and positively to its (differential) perceived risks and co-movement with the world economy. Furthermore, we would like to explore whether these predictions hold for all countries or for particular groups of them.

We use the econometric methodology outlined in section 2 based on the pooled mean group (PMG) estimator to obtain the coefficients of the long- and short-run relationships between NFA/wealth and its proposed determinants. As noted earlier, the PMG estimator forces the longrun coefficients to be homogenous across countries in the sample but allows the short-run parameters to vary from country to country. Given that we expect the portfolio-diversification model to drive the allocation of external assets mostly in the long run (that is, after an adjustment period), our focus is on the steady-state relationship.

In the estimation we also allow for intercept heterogeneity **by** including country-specific constants. These will account for unobserved time-persistent factors that are specific to each country **--** such as home-bias effects. Furthermore, in order to eliminate common factors across countries -- which would induce cross-sectional correlation of the residuals--, we also allow for time (year) effects.

> The inclusion of country- and time-specific intercepts modifies the interpretation of the estimated coefficients. Including country-specific intercepts means that we allow the NFA/wealth ratio to vary across countries for factors not totally captured **by** the explanatory variables. In turn, including time-specific intercepts implies that the change in each variable should be interpreted as a change relative to the mean of all countries, as already noted.

Two other important specification assumptions are that the regression residuals be serially uncorrelated and that the explanatory variables can be treated as strictly exogenous. As noted in section 2, we seek to meet these requirements **by** appropriately selecting the lag order of the

**17**
ARDL process- for NFA/wealth in each country. We use the Schwartz Bayesian Criterion **(SBC)** to determine the dynamic _specificationfor each country,_ subject to a maximum of two lags for each of the five variables in the model (NFA/wealth ratio, return, risk, co-movement, and foreign/domestic wealth ratio). The specification selected in this way varies across countries; however, for most of them the information criterion selected at least one lag for NFA/wealth and foreign/domestic wealth. In a number of cases the **SBC** also retained lags of the return, risk, and co-movement indicators.

The PMG estimator does not require the variables to be stationary or have the same order of integration. Nevertheless, given the novelty of this estimator, there may be some lingering doubts as to whether its properties prevail in the presence of integrated series. These doubts, however, do not apply in our case given that all the series involved in our econometric model appear to be stationary. First, on conceptual grounds, we work with ratios, rates of growth, and normalized indices that are naturally bounded (see Cochrane **1991).** Second, on statistical grounds, we conduct panel unit-root tests whose results reject the null hypothesis of nonstationarity for each of the series included in our empirical model (see Table 2). This strongly suggests that we are working with stationary series.

Tables **_3-5_** present the estimates of the long-run coefficients for different groups of countries. In Table **3** we use the composite indices of risk and return, in Table 4 we use the indices based on the growth rate of **GDP** per capita, and in Table **_5_** we use the indices derived from stock- market returns. In all cases, the results are broadly supportive of the empirical specification when the model is estimated -on the high-income and/or low-capital-control samples. When using the composite indices of risk and return (Table **3),** all the explanatory variables carry the expected sign and their coefficients are statistically significant for the sample of high- income countries; the results are similar for the sample of low-capitalcontrol countries, except that the comovement index is no longer significant. In turn, when using the indices based on per capita **GDP** growth and stock market returns (Tables 4 and **_5),_** we find that the return and risk measures as well as the relative wealth ratio carry significant coefficients of the expected

> **18** We also experimented with imposing common dynamic specifications across countries; this obviously alters the short-run estimates but has a relatively minor effect on the long-run parameters.

**18**
sign for the samples of high-income<sup>and low-capital-controlcountries;on theother hand,the</sup> comovement index is not<sup>statistically significant.</sup> For these samples of countries, the<sup>main resultsare not only statistically significant but</sup> also economically relevant. Focusing<sup>on thesampleof high and upper-middleincome countries,</sup> we can draw some estimates and<sup>comparisonsfor the long-run effecton net foreignasset</sup> positions of changes in the portfolio<sup>indicesand relative wealth.</sup><sup>**A**one-standard-deviation</sup> increase in the composite return index<sup>leads to a reductionin the ratio of</sup><sup>**NFA**to domestic wealth</sup> of about **0.28** standard deviations,<sup>andan analogousincreasein the compositerisk index</sup> produces a decline of twice that<sup>magnitude.</sup><sup>**A**correspondingincrease in the composite</sup> comovement index produces a rise of<sup>about</sup><sup>**0.11**standard deviations of NFA/domesticwealth;</sup> thus, the effect of the comovement<sup>index isnot only statisticallyweak but also economically</sup> small in relative terms. An increase of<sup>one-standarddeviation in the ratioof foreignto domestic</sup> wealth leads to a decrease in NFA/domestic<sup>wealthof about</sup><sup>**0.28**standard deviations,quite</sup> similar to the corresponding effect<sup>of thereturn index.Whenwe use the indices based on</sup><sup>**GDP**</sup> growth, the results are similar with<sup>twoexceptions.First,the effect of the risk index drops but</sup> still remains above that of the return<sup>index;andsecond,the effect of the comovementindex falls</sup> to one-tenth of the effect of the other<sup>variables.Finally, when weuse the indices basedon stock</sup> returns, the ratio of foreign to domestic<sup>wealth becomesthe mostimportant variable.The effects</sup> of return and risk are smaller than<sup>in thepreviouscases, but the rankingof their relativestrength</sup> (first risk, then return) is preserved.

Focusing on the economic impact on<sup>NFAldomestic wealth, wecan draw the following</sup> conclusions. First, the return and<sup>risk indices based onseveral macroeconomicvariables and</sup> **GDP** growth rate have a larger<sup>effecton the net foreignasset position than those basedsolely on</sup> stock-market returns. Second, changes<sup>in therisk index appear tocausestrongereffectsthan</sup> those of changes in the return index.<sup>Third,theeffectof the comovementindex isof a much</sup> smaller magnitude than those of<sup>the other variables.Andfourth,although the effectof relative</sup> wealth varies somewhat with the<sup>typeof indices used,its magnitudeisalways of the same order</sup> as the effect of the return and<sup>risk indices.</sup> The results change considerably<sup>when weconsider other samplesof countries.In thefull</sup> sample, as well as for the groups<sup>of low andlower- middle incomeand high capital control</sup> countries, the risk and return proxies<sup>arein mostcasesinsignificantand in somecasescarrythe</sup>

**19**
wrong sign. The same occurs with the co-movement<sup>indicator.Only the coefficient on theratio</sup> of foreign to domestic wealth remains consistently<sup>negative and significantforall groupsof</sup> countries and for the three types of return/risk<sup>measurements.1 9</sup> For countries with high capital controls, the weaker<sup>performanceof the model might be</sup> viewed as evidence that capital controls<sup>achieve some degreeof success</sup><sup>**-**theydampen the</sup> effects of risk and return factors on portfolio<sup>decisions.For the lower income countries,the</sup> likely reason is the limited role that optimal<sup>diversification decisionsplay in the observed</sup> evolution of net foreign assets, which<sup>may be dominatedinstead</sup><sup>**by**otherconsiderationssuchas</sup> the willingness of donor governments<sup>toextend,andforgive,concessionallending.</sup> In summary, our portfolio-diversification approach<sup>seems to apply for some,but not all,</sup> groups of countries. For countries<sup>wheremarketforces arelikely to dominateother</sup> considerations, our results indicate that<sup>whena countrybecomes more productive (greater mean</sup> returns) and more stable (lower perceived<sup>risk),its netforeignasset positionrelativeto wealth</sup> declines. The effect of providing a better<sup>hedge forworldwide risks (lower co-movement)</sup> appears to go in the same direction, but<sup>our resultsinthisrespect arelesssignificant and robust.</sup> Finally, note that the effects of return,<sup>risk,and co-movementon the</sup><sup>**NFA**ratiohold when we</sup> control for relative wealth. Wealth per<sup>se</sup> has.a<sup>significantinfluence over the NFA ratio in the</sup> sense that when domestic residents'<sup>wealthgrowsfaster</sup><sup>**than**that of foreigners,thefractionof net</sup> foreign assets in wealth<sup>increases.</sup> Tables **6-8** display additional results for the samples<sup>of highand upper-middleincome</sup> countries and low capital control countries,<sup>for which the empirical model ismoresuccessful.In</sup> Table **6** we use the composite indices of risk<sup>and return,whilein Tables</sup><sup>**7**and</sup><sup>**8**weuse the</sup> indices based on per capita **GDP** growth<sup>and stock market returns, respectively. In these tables</sup> we present the estimation of the full error-correction<sup>model using both the Pooled MeanGroup</sup> estimator and its Mean Group counterpart<sup>thatallowsfor unrestrictedlong-runparameter</sup> heterogeneity across countries. Comparison<sup>between both sets of estimates allows the</sup> construction of formal tests of the long-run<sup>pooling restrictionsimposed</sup><sup>**by**the Pooled Mean</sup> Group estimator. As explained in section<sup>2, wecantest the maintainedassumptionin the PMG</sup> estimator that the long-run coefficients are<sup>the sameacross countriesthrough Hausman-typetests.</sup>

**19** This robust effect of wealth is in agreement<sup>with the stylizedfact underscored</sup><sup>**by**Kraay</sup><sup>_etal.(2000)_thatforeign</sup>

20
Specifically, we can compute individual test statistics for each one of the long-run coefficients. These are reported, along with the associated p-values, in columns **3** and **6** of Tables **6-8.** We find that the cross-country homogeneity of long-run coefficients is never rejected in the cases of the return, co-movement, and relative wealth variables. This is also the case for the risk index in four out of the six instances considered. Cross-country homogeneity of the riskrelated coefficient is rejected in the sample of low capital controls with composite indices and in the sample of high-income countries with stock-return indices.

It is also apparent from Tables **6-8** that the long-run coefficients estimated with the alternative Mean Group method suffer from very poor precision. **Of** 24 coefficients **(6** exercises with 4 explanatory variables each), only six are statistically significant, and only the coefficient on relative wealth shows a consistent (negative) sign across all exercises. This lack of precision and robustness across different samples and return/risk measures reflects the sensitivity of the **MG** estimator to outliers in the country-specific estimates.

> The bottom half of Tables **6-8** reports the average estimates of the speed of adjustment (denoted as <p in equation **(6)** above) and the short-run parameters. As required for dynamic stability, the coefficient on the error-correction term (i.e., the speed of adjustment) is negative and significant in all six exercises. It is also somewhat smaller in magnitude in the PMG than in the **MG** specification, in accordance with the theoretical prediction that pooling in the presence of heterogeneity tends to increase inertia (Robertson and Symons **1992).** Focusing on the PMG estimates, the average short-run parameters obtained for the two samples and three sets of return/risk indices reveal significant lagged effects of the dependent variable and contemporaneous effects of the foreign/domestic wealth ratio. In addition, there are also significant contemporaneous effects of the return variable when the composite indices are used and lagged effects of the foreign/domestic wealth ratio for the sample of low capital controls.

On the whole, the explanatory power of the PMG estimates is rather satisfactory, and the average of the country-specific adjusted 1l is over **30%** for the high and upper-middle income countries and over 40% for the low capital-control countries (R<sup>2</sup> s are larger for the **MG** estimates). This is encouraging particularly in view of the large sample size **(828** and **_577_** assets show a strong positive association with wealth levels.

21
observations for high income and low capital control samples, respectively) and the simplicity of the model.

# **_5._ CONCLUSIONS**

> The determinants of international portfolio diversification have attracted considerable attention in the literature. Empirical studies have examined mostly equity holdings across a small number of industrial economies, and in most cases conclude that the extent of international diversification falls short of what would be predicted **by** standard portfolio equilibrium models the home bias puzzle.

This paper explores empirically the role of risk and. return factors in the observed evolution of net foreign asset positions of a large number of industrial and developing economies. Its objective is to examine whether international capital flows respond to market incentives and, if so, whether this conclusion can be generalized to all countries in the world or only particular subsets of them. Thus, the paper does not attempt to reconcile the observed extent of diversification with theoretical predictions, but instead tries to assess empirically the role of changing fundamentals in the actual evolution of international portfolios, taking implicitly as given their 'home bias'.

> The paper adopts a dynamic approach accoting to which international and domestic investors achieve in the long run their desired portfolio allocation of assets across countries. Frictions and adjustment costs, however, can make short-run portfolios differ from their long-run counterparts.

Based on a standard Markowitz-Tobin portfolio diversification framework, the paper develops a reduced-form model of net foreign asset positions. The model yields a long-run equilibrium condition in which the ratio of **NFA** to the total wealth of domestic residents depends on four factors: investment returns in the home country relative to the rest of the world, investment risk in the home country relative to the rest of the world, the degree of co- movement between investment returns at home and abroad, and the ratio of foreign-owned to domesticowned wealth.

> The paper fo'cuses on the empirical estimation **of** this long-run equilibrium condition, using data on foreign asset and liability stocks for a large number of industrial and developing countries spanning the period from the 1960s to the present. With these data and capital stock estimates, the wealth of each country's residents can be computed. In addition, the paper

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develops measures of country returns<sup>and risks-inthree versions:compositeindices constructed</sup> using a comprehensive set of macroeconomic,<sup>policy,and institutionalvariables;indices based</sup> on the rate of economic growth; and<sup>indices based onstock market returns.</sup> The econometric approach is derived from the Pooled Mean<sup>Groupestimator recently</sup> developed **by** Pesaran, Shin, and Smith **(1999).** This<sup>approachis well-suitedto the paper's</sup> objective, as it provides a dynamic<sup>setting imposing along-run relationship commonto all</sup> countries but allows for heterogeneous<sup>short-runadjustment acrosscountries.</sup> On the whole, the estimation results lend support to<sup>themodel when appliedto high and</sup> upper- middle income countries and/or countries<sup>with moderatecapitalaccount restrictions. The</sup> estimated long-run parameters on relative wealth<sup>andthe two alternative measuresof risk and</sup> return are correctly signed and always significant.<sup>Thus,as predicted</sup><sup>**by**the theoreticalmodel,net</sup> foreign assets (as a ratio to total wealth)<sup>are negatively relatedto the measuresof domestic</sup> investment returns and the ratio offoreign to domestic<sup>wealth,and positivelyto the measuresof</sup> investment risk. Our measure of co- movement<sup>also shows an association with the NFA/wealth</sup> ratio, but not as robust as with the other explanatory<sup>variables. Finally, the long-run parameter</sup> homogeneity across countries imposed<sup>**by**the PMGestimatoris supportedin most cases</sup><sup>**by**</sup> Hausman specification tests. The results for countries characterized **by** high capital controls and, especially, lower income levels, are less<sup>supportiveof theportfolioequilibriummodel.For the</sup> former countries, this might be viewed<sup>as evidencethat capitalcontrols achievesome degreeof</sup> success **-** they dampen the effects of risk<sup>and returnfactorson portfolio decisions.For the lower</sup> income countries, the likely reason is the limited<sup>rolethat optimal diversification decisions play</sup> in the observed evolution of their net foreign<sup>assets.To a largeextent, theseconsist of official</sup> concessional debt, whose pattern across countries<sup>and over time may be dominatedinstead</sup><sup>**by**</sup> non- market considerations related to geopolitical<sup>interests,humanitarianaid,anddevelopment</sup> purposes.

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