International Capital Flows to Emerging Markets- National and Global Determinants

7/25/2019 International Capital Flows to Emerging Markets- National and Global Determinants

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Accepted Manuscript

Title: International capital flows to emerging markets: national and global

determinants

Author: Joseph P. Byrne, Norbert Fiess

PII: S0261-5606(15)00194-1

DOI: http://dx.doi.org/doi:10.1016/j.jimonfin.2015.11.005

Reference: JIMF 1617

To appear in: Journal of International Money and Finance

Please cite this article as: Joseph P. Byrne, Norbert Fiess, International capital flows to

emerging markets: national and global determinants,Journal of International Money and

Finance(2015), http://dx.doi.org/doi:10.1016/j.jimonfin.2015.11.005.

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International Capital Flows to Emerging Markets:

National and Global Determinants*

Joseph P. Byrneand Norbert Fiess

Department of Accountancy, Economics and Finance,

Heriot-Watt University, Edinburgh, UKWorld Bank, Washington DC, USA

21st August 2015

Highlights

1.We examine the nature and determinants of aggregate and disaggregate portfolio flows to

emerging markets.

2. We identify substantial comovement in gross capital inflows, evidenced by a common

factor originating in the global environment.

3.Capital inflows are driven by commodity prices, US rates of return, uncertainty and growth

in advanced economies.

4. Financial openness and the quality of institutions are important country specificcharacteristics driving capital inflows.

5.There is a common factor in the volatility of capital inflows, related to commodity pricesand US interest rates.

*For their helpful comments the authors would like to thank Cline Azmar, Julia Darby, Rodolphe Desbordes,

Giorgio Fazio and Gregg Huff. We would also like to thank Serena Ng for the use of Matlab code. Finally, we

would like to thank the Editor and Reviewer for helpful and very detailed comments. Correspondence Address:

Department of Accountancy, Economics and Finance, Heriot-Watt University, Edinburgh, UK. Email:

.

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Abstract

Using a novel dataset for emerging markets, we empirically investigate the nature and

determinants of aggregate and disaggregate capital inflows. We present formal statistical

evidence of commonalities in capital inflows, with the strongest evidence for the level of

equity and bank flows. Advanced economy long-run bond yields and commodity prices areidentified as determinants of global capital flows. We also consider the national determinants

of capital flows, finding that financial openness and institutions matter for country flows.

Finally, we identify important commonalities in the volatility of bank inflows.

Keywords: Capital Flows; Emerging Markets; Global Factors; Idiosyncratic Flows.

JEL Classification Numbers: F32; F34.

Abstract

Using a novel dataset for emerging markets, we empirically investigate the nature and

determinants of aggregate and disaggregate capital inflows. We present formal statistical

evidence of commonalities in capital inflows, with the strongest evidence for the level of

equity and bank flows. Advanced economy long-run bond yields and commodity prices are

identified as determinants of global capital flows. We also consider the national determinants

of capital flows, finding that financial openness and institutions matter for country flows.

Finally, we identify important commonalities in the volatility of bank inflows.

Keywords: Capital Flows; Emerging Markets; Global Factors; Idiosyncratic Flows.

JEL Classification Numbers: F32; F34.

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1. Introduction

Historically, capital flows to emerging markets have mainly comprised foreign direct

investment. Recently, however, portfolio equity and bank-related flows to emerging markets

have increased substantially. Policy makers and academics are increasingly interested in the

nature and causes of these flows. For example, are international or domestic factors important

for capital flows? An existing strand of the literature highlights global characteristics, see

Calvo, Leiderman and Reinhart (1993) and Reinhart and Reinhart (2009). Although different

types of portfolio flows, whether this be equity, bond and bank portfolio inflows, behave

differently, see Contessi, DePace and Francis (2009). As well as focusing on global and

disaggregate behaviour this paper also considers the nature and relevance of country-specific

factors. Domestic structural characteristics may also be important for emerging market capital

inflows, such as financial openness, human capital or institutions, see Lucas (1988), North

(1994) and Alfaro, Kalemli-Ozcan and Volosovych (2008). This study makes use of a novel

panel time series dataset and innovations in panel methodology to examine both global and

national determinants of gross capital inflows.

According to Rothenberg and Warnock (2011) net capital flow dynamics may be

driven by capital inflows or outflows, which in turn may be related to different factors. Hence

capital in- and outflows require to be studied separately. Forbes and Warnock (2012) suggest

few papers have studied gross capital inflow data, previously focusing upon the more readily

available net flow data. Given Reinhart and Reinharts (2009) ocular evidence on common

capital inflow bonanzas, we statistically test for commonalities in our Bondware capital inflow

data. The extent of commonalities in global capital flows and their nature is assessed by Bai

and Ng (2004)s Panel Analysis of Nonstationarity in Idiosyncratic and Common components

(PANIC) methodology. The PANIC approach deals with potential nonstationarity by first

differencing the data, identifying a principal component and then re-cumulating the principal

component as a common factor. This avoids the identification of spurious common factors

based upon nonstationary data. When used in conjunction with Ngs (2006) test for cross

sectional correlation and Bai and Ngs information criteria, PANIC is useful since it allows us

to examine the existence and nature of common factors in global capital flows. This is

important in the current context for two reasons: if shocks to capital inflows are temporary

they are quickly reversed and thus less worrisome from a policy makers perspective. If

shocks are permanent this is more problematic. For example, if there is a permanent increase

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in volatility of capital inflows following openness this would be highly disadvantageous,

making economic growth volatile. Moreover, from a statistical perspective whether shocks are

permanent or temporary is important since it shall decide whether our methodology should be

robust to nonstationarity when assessing the determinants of commonalities in capital flows.

We examine the drivers of the common component in capital flows, and whethereconomic developments in the global environment are important for common trends in capital

flows. This is related to the work by Levchenko and Mauro (2007), Reinhart and Reinhart

(2009) and Forbes and Warnock (2012). Makowiak (2008), Uribe and Yue (2006) and

Neumeyer and Perri (2005) emphasize international factors in driving interest rates and output

in emerging markets. Forbes and Warnock (2012) also identify important global variables that

drive extreme movements in capital inflows and outflows. However, we go beyond the

existing work on capital flows since we focus on identifying the global component which, by

construction, is orthogonal to idiosyncratic characteristics, such as country-specifics or the

domestic policy in a particular recipient country. These idiosyncratic movements in flows are

not truly global capital flows and indeed may mask important global determinants. We think

the common component in global capital flows may be influenced by international economic

activity and we test this hypothesis in our paper.

In this paper we extend existing work on capital flows in several regards: we first

assess the degree of commonality in capital flows, which provides a gauge for the importance

of common factors in determining the global supply of capital. We then extract this common

factor and relate it to economic fundamentals. As the level of aggregation of capital flow data

may impact both on the time series and economic determinants, we provide evidence for both

aggregate capital flows as well as disaggregated data based on portfolio equity, bank and bond

flows. We next explain the national determinants of aggregate capital flows. This is important

as it allows us to consider different conjectures as to how individual countries are impacted by

financial openness (Chinn and Ito, 2008), human capital (Lucas, 1990) and institutional

characteristics (North, 1994). We also consider standard recipient country explicators like

economic growth and interest rates.

To preview our main results, we identify important commonalities in capital inflows,

but these commonalities depend upon whether we consider aggregate or disaggregate capital

flows. Shocks have long lasting consequences for the common element in capital inflows. For

bank flows we find US long-run real interest rates are an important determinant of this

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common element, in parallel with Bernankes et al. (2011) suggestion that financial

globalisation operated through assets of a longer maturity. Also, there is a role for commodity

prices and uncertainty in driving equity flows, consistent with the evidence from Reinhart and

Reinhart (2009) and Forbes and Warnock (2012). Using panel econometrics we also present

formal statistical evidence that de jure financial openness and institutions explains why somecountries receive capital inflows. Finally, we identify commonalities in capital flow volatility;

these common shocks are not long lasting and relate to global determinants. Overall this

implies that the volatility of a countrys capital markets is influenced by external factors that

may be transient in nature.

This paper is structured as follows. Section 2 sets out the formal statistical methods

used in this study, including Uniform Spacings, PANIC and our approach to identifying

national and global determinants of flows. Section 3 introduces the dataset and presents our

main results. We examine evidence of commonalities in capital flows for aggregate and

disaggregate data, their time series properties and consider what drives these global capital

flows. Finally, we discuss what time-varying country characteristics influence whether a

country can attract idiosyncratic capital inflows. Section 4 concludes and makes policy

recommendations.

2. Empirical Methods

This study considers both the nature and determinants of the common and

idiosyncratic element of emerging markets capital inflows. We posit that the common

element is global flows. The idiosyncratic component is country, or nation, specific. Ngs

(2006) Uniforms Spacings approach is our first evidence on capital flow commonalities. Ng

(2006) constructs a test statistic, from a standardized spacings variance ratio (svr) test, which

examines the null hypothesis of no correlation in a panel time series. The svrtest examines the

probability integral transformation of the ordered correlations, rather than the sample

correlations themselves. Once these correlations are ordered it is more straightforward to

partition them into a sample of smalland largecorrelations. Ng (2006) framework allows

us to ascertain the proportion of small ( ) and large (1 ) bivariate correlations in a panel

dataset, where 1,0 . The test utilizes small and large correlation subsets of size n,0 ,

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where n = N(N1)/2 is the maximum number of correlations for N time series. The

standardized test statistic is:

2)(q

SVRsvr , where 2,0Nq

SVR (1)

SVRis based upon the second moment of the actual correlations. Therefore, we have two svr

statistics for each of the small and large groups of correlations to initially test for co-

movement.

We can shed further light on the nature and determinants of capital co-movement in

our aggregate and disaggregate capital inflow data by using the Bai and Ng (2004) Panel

Analysis of Nonstationarity in Idiosyncratic and Common components (PANIC)

methodology. This has a number of advantages. We can identify pervasive or country specific

nonstationarity in the data, since we do not merely assume the latter and hence potential

idiosyncratic nonstationarity. Nonstationarity is relevant to the recent period of financial

globalisation, which involved increasing capital inflows to emerging markets. Also, this factor

model has the advantage that we are not required to know a priori if there is nonstationarity in

the data, since we first differences the data to identify the common component and then re-

cumulating. This avoids spurious factors based on nonstationary data. Moreover, by extracting

a common factor and identifying co-movement, PANIC allows us to model global capital

flows. Kose et al. (2003) and Ciccarelli and Mojon (2010) also use factor models to examine

co-movement of real and nominal international data.We focus on two key issues: the global and national determinants of capital inflows.

We consider these issues in several estimation steps. Our first estimation step is to examine

the global determinants of capital flows in a bivariate time series approach as follows:

Ft =f(Xt ) t=1,...,T (2)

We proxy the level of the common factor in capital flows (Ft) at time t using the principal

component extracted by the PANIC methodology. Capital flows are a linear function f(.) of a

vector of potential explanatory variablesXt: these includes the level of real non-oil commodityprices (RCPt), the real short term (RSRUSt) and long term (RLRUSt) US interest rates, VIX

uncertainty index (VIXt) and real GDP growth in the G7 (YtG7

). We are specifically interested

in examining the correlation and evidence of Johansen (1988) cointegration between these

potential explanatory variables and the global component in capital inflows. Given that the

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common factor is central to our approach, we now go on to explain the PANIC methodology

in some detail.

The PANIC approach separates a panel time series of capital inflows (CAPit) into

country specific fixed effects (ci) for each country i,acommon factor (Ft) which varies over

time tand is associated with corresponding factor loadings (i) and idiosyncratic components(uit). It is unlikely to be the case that all countries capital flows are equally correlated and the

common factor may matter more for some countries rather than others. Hence factor loadings

ishall vary across country i. The PANIC specification is as follows:

CAPit= ci + iFt+ uit i=1,...,N;t=1,...,T (3)

This approach identifies a common factor Ft taking account of nonstationarity by first

differencing the data, identifying a principal component and then re-cumulating this

component and testing its statistical properties. The PANIC method of differencing and re-

cumulating is advantageous since it can be used to consistently identify commonalities and

nonstationarity in the panel dataset. The factor loadings i in equation (3) are obtained from

the loadings of the principal components analysis. That is, they are the eigenvalue associated

with the corresponding eigenvector using a principal components approach. The constant term

ci is latent and is removed by first-differencing the data. The country-specific (or

idiosyncratic) component in the factor model is the error term (uit) in equation (3).

We test whether the panel time series CAPit is nonstationary by examining the

statistical properties of the common factor and the errors term in equation (3). Using panel

unit root tests we can examine the null hypotheses of a nonstationary common factorFtand/or

idiosyncratic component uit. We examine nonstationarity in the factor component using a

univariate Augmented Dickey Fuller (ADF) test, as follows:

Ft= Ft-1+ t (4)

Based upon equation (4), the common factor ADF test has a null hypothesis H 0:= 1 against

an alternative of HA:< 1. For this test we would reject the null hypothesis of factor unit root

for test statistics with large negative values, i.e. less than -2.89 at the 5% significance level,

but fail to reject it otherwise.

The idiosyncratic test statistic ( cu

P

) is a Fisher-type pooled ADF test on the individual

errors uit in equation (3). This is distributed as standard normal as follow:

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NNipP Ni

c

u 4/2)(log2

1

. Here p(i) is a probability value from an ADF test on the

following equation for each cross section iforNcross sections:

uit= i uit-1+ eit (5)

The idiosyncratic statistic examines H0: i= 1 for all iin equation (5) against HA: i< 1, for

some i. The test statistic on the idiosyncratic component is based upon the adjusted sum of

probability values from i idiosyncratic ADF tests, and if this test statistics is greater than 1.65

we would reject the null of idiosyncratic nonstationarity at the 5% significance level, and fail

to reject the null otherwise. Bai and Ng (2002) also set out three information criteria to

identify whether there are common factors in the data.

What motivates our interest in particular global determinants in equation (2)? Reinhart

and Reinhart (2009) examine the relationship between capital flows and two measures of

global economic activity. The first measure is real per capital GDP growth in advancedeconomies. A slowdown in growth in advanced economies leads to an expansion of capital

flows to emerging market economies, to take advantage of relatively stronger economic

activity and higher returns. The second global determinant is an index of real non-oil

commodity prices, since emerging markets are often exporters of primary commodities and an

increase in their price shall elicit higher investment. Moreover, Frankel (2008) illustrates a

potential link between commodity prices and real interest rates, with lower rates encouraging

speculation in commodities. A fall in interest rates will lead to a lower discounting of future

commodities, leading to an increase in the price of commodities today. Also, a decline in rates

is associated with an increase in investment and commodity prices. Reinhart and Reinhart

(2009) present evidence of a statistically significant and positive (negative) relationship

between commodity prices (economic growth) and capital inflows between 1967 and 2006.

See also Ahmed and Zlate (2014). Finally, Reinhart and Reinhart (2009) consider the direct

impact of short term real interest rates on capital flows. A fall in real rates of return in

advanced countries leads to an increase in capital flows to emerging market economies, as

investors search for yield. They measure capital inflows using current account data and we go

beyond this in our analysis by using actual capital inflow data.

In addition to advanced economiesgrowth, commodity prices and short term interest

rates, there are other potential determinants of capital inflows to emerging markets and we

examine two not widely considered by the literature: long-term interest rates and global

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uncertainty.Long-run interest rates may be at least as important for capital flows as short-run

rates, if investors prefer to diversify assets with short maturities and assets of different

maturity are imperfect substitutes. This is related to Bernanke et al. (2011) discussion of

global saving being closely associated with the assets of longer maturities. Moreover,

investment opportunities may be driven by uncertainty in advanced economies. This isconnected to the literature on investment and uncertainty, exemplified by Dixit and Pindyck

(1994). A recent and popular measure of financial uncertainty is the VIXindex of the Chicago

Board which measures the implied volatility of options on the S&P 500 equity index. It

reflects risk aversion in global capital markets to the extent that a rise in implied volatility

reflects a decline in investors risk appetite. Forbes and Warnock (2012) identify an important

role for global risk in influencing extreme aggregate capital flows. See also Bekaert et al.

(2012) for volatility commonalities in financial markets.

Beyond a consideration of purely global determinants of capital flows, we also seek to

investigate the country-specific determinants using a combined panel fixed effects

methodology. In particular, we consider whether financial openness, quality of institutions and

human capital in the recipient country are important in attracting aggregate capital flows.

Consequently, our empirical model of the global and country-specific determinants of

aggregate global capital flows to emerging markets is:

CAPit= 0i+ 0 + 1FOit+ 2Iit+ 3HCit+ 4Yit+ 5Rit

+6RCP.t+7RSRUS.t+8RLRUS.t+9VIX.t+10Y.tG7

+it i=1,...,N; t=1,...,T (6)

Equation (6) implies capital flows (CAPit) for country i at time t are a function of country-

specific characteristics financial openness (FOit), institutions (Iit) and human capital (HCit).

Financial openness is a necessary condition for capital inflows and is measured by the Chinn

and Ito (2008) index. Human capital as suggested by Lucas (1990) and institutions as

emphasized by North (1994). The remaining country-specific, or pull, determinants, or

push factors,of capital flows come in the form of domestic economic growth (Yit) and local

interest rates (Rit). Secondly, capital inflows are also impacted by global determinants

including non-oil commodity prices (RCP.t), the real US short term (RSRUS.t) and US long

term (RLRUS.t), VIX uncertainty index (VIX.t) and real GDP growth in the G7 (Y.tG7

). The

subscript (.t) denotes that these explicators are global and do not vary across cross section,

hence the country i subscript is suppressed. In equation (6) parameters 0 to10 are coefficients

estimated by panel fixed effects and it is the random error term.

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What is more, our methodology allows us to focus upon the country-specific

determinants of capital inflows, by considering the drivers of idiosyncratic capital flows (uit)

in the following estimated panel equation:

uit= 0i + 0+ 1FOit+2Iit+3HCit+4Yit+5Rit+ it i=1,...,N;t=1,...,T (7)

In equation (7), idiosyncratic capital flows uit are extracted from aggregate flows usingequation (3) and the Bai and Ngs PANIC approach. Estimated coefficients in equation (7) are

denoted by 0 to5 and it is a random error term. Since we seek to explain idiosyncratic capital

flows which are country-specific, we primarily focus upon country-specific determinants in

equation (7). Hence we examine the importance of financial openness, institutions, human

capital, national economic growth and national interest rates.1Having set out our empirical

methodology and research hypotheses we now proceed to discuss our data and present our

results.

3. Dataset and Empirical Results

3.1 Data

In this study we use quarterly data on capital inflows for up to 64 emerging markets. A

list of countries is provided in the Data Appendix Table A1. The quarterly inflow data is from

Euromoney Bondware and Loanware, with the sample period 1993Q1 to 2009Q1. We scale

our capital flows using a period-by-period measure of economic activity in each country. We

have three types of disaggregate capital inflow data: Equity Issuance, Bond Issuance and

Syndicated Bank Lending. We avoid a difficulty flagged by Rothenberg and Warnock (2011),

since we use capital inflows and we do not conflate foreign and domestic investors which

occurs when net capital flows are examined, for example when using current account data. We

combined our three flow measures to represent our aggregate capital inflow data. Our gross

capital inflow dataset is preferable since it goes beyond the net data often used, has greater

frequency than other Balance of Payments data and greater granularity in allowing us to

consider disaggregate equity, bond and bank lending. The data may be considered to have

drawbacks however, as it uses only inflows, not offsetting outflows, and focuses upon primary

1As robustness we also consider whether global determinants are important for idiosyncratic flows in equation

(7). However since we have extracted the orthogonal global component from capital flows to produce uit, we

have premia facia reasons to believe these global factors are unlikely to be important in equation (7).

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issuance, excluding the secondary market. However there is a good concordance between

Lane and Milesi-Ferreti (2007)External Wealth of Nations dataset and ours.2,3

During our sample period there have been waves of aggregate capital inflows across

emerging market economies. In the 1990s capital inflows increased substantially prior to theAsian Crisis in 1997. Then, more recently a relatively more substantial wave preceded the

Global Financial Crisis. This is illustrated by Figure 1 which contains the first principal

components of aggregate and disaggregate inflows. Kose et al. (2007) argue that the ability of

emerging economies to share consumption risk is hindered by limited access to external debt.

However, the most recent financial wave has been associated with a deepening of financial

markets in emerging economies, see Lane and Milesi-Ferretti (2008). Early in the sample

period bond flows increased relative to bank and equity flows. However, bank and equity

flows have recently become important. Figure 1 also indicates bank flows have been volatile

during the crisis, consistent with Bankings significantrole in the crisis.

3.2 Uniform Spacings

We now formally test the extent of co-movement of capital inflows to emerging

markets by applying Ngs (2006) Uniform Spacings. The results are presented in Table 1.

They provide evidence of commonalities across disaggregate capital flows, although there are

some quantitative differences for each category of flow. The test statistic (svr) is based upon a

partition of the ordered correlations into small and large groups. Hence we have two svrtest

statistics: one for large correlations and another for small. For aggregate flows we marginally

fail to reject the null hypothesis of no correlation for a subgroup of over 20% of large bivariate

correlations (large svr = 1.464). This is indicative of some aggregate co-movement. For

disaggregate flows we find greater evidence of a correlation between bank, bond and equity

flows since we reject the null hypothesis of no correlation for all three large svr statistics. This

is illustrative of a relatively greater degree of co-movement of disaggregate than aggregate

2Altman et al. (2010) discuss the secondary market for bank and bond debt. Cerutti et al. (2014) differentiate

between syndicated and non-syndicated loans when examining cross border bank lending: syndicated loans are

typically held to maturity, but can be traded in secondary markets. Eichengreen and Mody (2000) provide a

discussion of the difference between primary and secondary data for interest rate spreads. For a firm level study

of Asian corporate bond issuance see Mizen and Tsoukas (2014).3Our gross capital inflow dataset does not allow direct comparability with net Balance of Payments data. We do

find that our dataset compares well on an annual basis with Lane and Milesi-Ferretti (2007) external wealth

dataset, with a correlation of our aggregate data of 0.95 in means and 0.75 in standard deviations.

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capital flows. Hence the disaggregate data provides a sharper indication of actual country

correlations of particular financial flows. This is interesting especially for equities given the

recent development of equity markets in emerging markets, see Lane and Milesi-Ferretti

(2008).4Having statistically identified the global nature of flows we now turn to an analysis of

common factors in the aggregate and disaggregate data.

3.3 Common Factors in Capital Flows

Our main approach to identify commonalities in based upon principal components.

Principal components are a means to reduce the dimensions of a large dataset such as ours.

The core PANIC results using this approach are set out in Table 2 Panel A. These identify

whether there is a principal component (Ft) in the inflow data and the nature of this

component. Information Criteria (IC) from Bai and Ng (2002) inform us whether there exists a

common component. Time series and panel unit root tests examine whether the common and

idiosyncratic components are nonstationary, respectively. For aggregate, equity and bank

flows there is evidence of a common or global component based on all three information

criteria (i.e. IC>0). This supports Reinhart and Reinharts (2009) informal evidence of capital

inflow bonanzas across countries and our uniform spacings evidence. For international bond

flows to emerging markets there is slightly less evidence of co-movement. That is, according

to Table 2, bond flows display evidence of a common factor for some but not all information

criteria (i.e. IC3=0). So whilst there are substantial capital inflow commonalities we can

differentiate financial flows across country and across asset. We proceed by imposing one

common factor on the data for the aggregate and disaggregate data.

We next test whether the capital inflow common factor is nonstationary in Table 2. We

do so using autoregressive factor equation (4) and examining the null hypothesis H0:= 1. In

terms of time series properties, the aggregate and disaggregate global flow factors are always

nonstationary. In other words, since the univariate factor ADF test statistics are greater than

the 5% critical value we are unable to reject the null hypothesis of a unit root in the common

component of aggregate and disaggregate bank, bond and equity flows. There appears to be

pervasive permanence in capital flows in response to global economic shocks. As can been

seen from Figure 1 which plots the first principal component of our four panel time series, the

4We should note that there is not a substantial proportion of statistically significant correlations for bank and

equity flows in the uniform spacings test. In the next section therefore, we use Bai and Ngs (2002) information

criteria on the existence of a common component to buttress this evidence.

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aggregate flow factor is characterized by a sharp rise and fall towards the end of the sample

period, associated with the crisis. Equity and bank flows also have experienced a more

pronounced wave towards the end of the sample period, consistent with Lane and Milesi-

Ferretti (2008) and IMF (2012). Also this upward trend took a significant reversal with the

crisis. Figure 1 also suggests that bonds flows have experienced earlier increases compared tothe rapid rise of equity and bank flows in the recent wave of financial globalisation. Hence,

the reduced evidence of a common component in bonds flows may be due to the lack of an

unambiguous upward global stochastic trend during our sample period.

Our PANIC results in Table 2 also allow us to characterise the nature of the

idiosyncratic (nation specific) capital inflows. In contrast to the common factor, the

idiosyncratic components all appear to be stationary. These test equation (5) using the null

hypothesis H0: i= 1 for all iin equation (5). That is we are able to reject the null hypothesis

of pooled panel unit root test since the pooled probabilities test statistic is greater than the 5%

critical value of 1.65, denoted by an asterisk. Domestic capital inflows, abstracting from

global components, have not experienced a permanent shock during our sample period. This

reinforces our interest in the global component, suggesting that individual country shocks have

not had permanent effects on their capital flows and we should look at the common

components for information on permanent changes during our sample period.

3.4 Correlations of Global Determinants and Common Factors

Having identified commonalities and delineated their time series behavior, in this

section we investigate the relationship between the common elements of capital flows across

countries and their relationship to other macro variables. It is important to look at the

determinants of the common component in capital flows, since this global element may be less

related to individual country characteristics. In this sense we go beyond the existing literature

on capital flows and identify the global determinants of capital flows to particular countries. A

similar approach is set out in the methodological contributions by Bai (2004) and Gengenbach

et al. (2006). As explained above, after extracting the common factors from our PANIC

approach, we consider the relationship between the common factors in aggregate, bank, bond

and equity capital inflows to emerging markets and the following explanatory variables: the

real non-oil commodity prices (RCPt), the real short term (RSRUSt) and real long term

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(RLRUSt) US interest rate, VIX uncertainty index (VIXt) and real GDP growth in the G7

(YtG7

).

Table 3 presents evidence on the global determinants of capital inflows for both

aggregate and disaggregate data. Some heterogeneity is again manifest since the importance ofthese explicators varies for aggregate and disaggregates flows: this supports our

methodological approach of considering aggregate and disaggregate data. There is a sizable

correlation between aggregate capital flows and real commodity prices, i.e. the correlation

coefficient is 0.39 in Table 3. Emerging markets are often commodity exporters and hence

capital inflows may be associated with increases in commodity prices. Moreover, there is

evidence of a negative correlation between aggregate flows and real long-run interest rates in

the US (i.e. -0.12). Low returns in advanced economies are pushing investment to emerging

markets. Uncertainty is also important in reducing flows, with a correlation coefficient of

-0.22. But, for aggregate flows, we find smaller correlations and counter-intuitive signs on

short rates and real economic growth rates, since they have positive and/or lower correlations

with the common factor. These warrant further disaggregate analysis.

Disaggregate data provides for a more granular analysis and Table 3 shows that the

determinants of capital flows vary for bank, bond and equity flows: for bank flows the most

important determinant is the long-run real US interest rate. Banks will actively lend to

emerging markets if there is a lower rate of return to long term US bonds. Relative to the size

of the correlation for long-run yields and given the lack of cointegration evidence below, there

is a less important role for short-term rates. This implies that longer maturity assets are more

important for global capital flows and the flows themselves are less directly attributable to US

monetary policy, contrasting with Reinhart and Reinhart (2009). They propose that capital

flows are related to economic growth, short-run interest rates and commodity prices. Forbes

and Warnock (2012) also highlight the importance of global factors, especially global risk

factors in driving gross capital flows. The VIX index is indicative of global uncertainty:

heightened global uncertainty can suppress investments due to potential irreversibility. This

uncertainty measure has a small correlation with bank or bond flows, the sign is counter-

intuitive or there is no evidence of cointegration, see below. Long rates appear more

connected to global financial developments than short rates. Bond flows are also influenced by

long-run interest rates. Table 4 indicates that economic growth matters for bank and bond

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flows (i.e. correlations with the common factor are -0.24 and -0.32 respectively). Common

equity inflows are substantially associated with real commodity prices, providing one of the

largest correlation of all our results (i.e. 0.48), and with long term interest rates (i.e. -0.32). As

mentioned earlier, Frankel (2008) highlights a negative link between real commodity prices

and interests rates. Since the signs of our bivariate relationships are consistent with thishypothesis, we cannot rule out that this channel explains the link between capital flows,

interest rates and commodity prices. Although the correlation between VIX and equity flows

is relatively small, the sign is intuitive and below we confirm there is evidence of a long-run

relation. This replicates Forbes and Warnock (2012) evidence of uncertaintys importance for

aggregate gross inflows, but extended to disaggregate equity flows. Finally, smaller

correlation statistics suggest there is a less important role for short term interest rates and

economic growth in driving equity flows.

It was noted above that there is evidence of nonstationarity in the common factors, and

there is nonstationarity in the explanatory variables, available upon request. Consequently, we

should exercise caution when interpreting evidence of correlations in the data unless there is

complementary evidence of cointegration. Table 3 presents evidence of a cointegrating vector

between the common factors in capital flows and also our explicators; this is denoted by aand

b at the 5% and 10% significance level respectively. The results strongly support our

correlation analysis. We find evidence that the aggregate behaviour reflects components of the

disaggregate results. For bank flows we find evidence that long-run real interest rates RLRUSt

are an important explanatory variable, since we have evidence of cointegration using

Johansens (1988) Trace Test statistic. And in addition to -0.57 being the largest correlation in

Table 3, a simple regression of f_bankt(the common factor in bank flows) on a constant and

RLRUStproduced a negatively signed estimate on the long-run interest rate with a t-statistic of

5.36, see the scatter plot in Figure 2. Economic growth also cointegrated with bank flows but

the correlation coefficient was much smaller. Like bank flows, bonds flows are also influenced

by global interest rates and growth. However, for bond flows we caveat these results since

they may not have a common factor according to one of Bai and Ngs (2002) information

criteria. The contrasting results between different types of debt highlights the usefulness of our

dataset since it allows us the granularity to distinguish between bank and bond flows.

Equity flow results appear to drive the path of aggregate capital flows with respect to

commodity prices and uncertainty. Equities are especially related to real commodity prices:

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the correlation coefficient is 0.48; there is evidence of bivariate cointegration; and the

bivariate cross plot has a positive coefficient and t-statistics= 4.16, see Figure 3. Indeed Table

2 Panel B identifies a high correlations between aggregate, equity and bank factors, which

again reinforces Lane and Milesi-Ferrettis (2008) point on the development of equity markets

in emerging markets. Uncertainty is also important since there is evidence of correlation andcointegration. This disaggregate equity evidence chimes with Forbes and Warnocks (2012)

result that risk is important in driving aggregate and extreme capital flows. Real interest rates

and growth are less important for equity flows since there is either no cointegration or a very

small correlation coefficient.

3.5 Panel Estimation: Global and Individual Country Determinants

Having identified commonalities in global capital flows, we now turn to address why

some individual countries receive more than others. We consider different hypotheses on why

some countries are affected more than others from the trends in financial globalisation. In

other words, why did some countries received substantial capital inflows as a consequence of

the deeper financial integration? At a fundamental level, financial openness would appear to

be an obvious reason why some countries receive a greater share of capital inflows. A country

decides to open markets and foreign capital should immediately flow in. This may be to ignore

the other potential obstacles to capital inflows. In this study we are interested in de jure

measures of financial openness since these give an indication of capital control liberalisation.

Other potential national determinants of capital inflows are the level of human capital

in a country and the general quality of institutions, based on a suggestion from Lucas (1990)

and North (1994) respectively. The main question in the literature on the Lucas (1990)

Paradox is why capital does not flow from rich to poor countries, despite a high relative

marginal product of capital for poor countries. Lucas (1990) suggests that accounting for

human capital can reduce or indeed completely eliminate the differential in marginal rates of

return to capital across countries, assuming that human capital spillovers are internalized

within a country. Hence, low human capital may be a bar to capital inflows. In contrast North

(1994) emphasizes institutions may be important for capital flows since economic returns

from investing in emerging markets may be dependent upon the quality of institutional

arrangements. The importance of institutions for capital flows is considered in a systematic

empirical framework by Alfaro et al. (2008) between 1970 and 2000. They suggest low

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institutional quality as the leading explanation for the Lucas Paradox. In summary, we seek to

discriminate between financial openness, the quality of institutions and also human capital in

our subsequent analysis in explaining why some countries receive substantial capital inflows

resulting from the recent period of financial globalisation.

We have a range of means of measuring potential country determinants of capitalinflows. Firstly, we have Chinn and Itos (2008) measure of financial openness (FOit) for each

country i. This is based on capital account transactions and the extent of capital controls and

their data is based upon the IMFs Annual Report on Exchange Arrangements and Exchange

Restrictions.5 We prefer Chinn and Itos de jure measure of capital controls rather than de

facto measures, as the latter are based on actual capital flows data which would make the

analysis somewhat circular. Secondly, for human capital (HCit) we use the Institute for Health

Metrics and Evaluation (IHME) data on the educational attainment of total population of 25

year olds and over.6This is a proxy for human capital and should raises capital inflows based

upon the argument in Lucas (1990). Furthermore, we have a measure of the quality of

institutions (Iit), from the International Country Risk Guide; an increase in the index means an

improvement of institutions in that particular country. An improvement in a country's

institution should increase capital inflows. Finally, we consider standard macroeconomic

determinants of capital inflows, including recipient country i economic growth (Yit) and

interest rates (Rit).

In Table 4 we examine the determinants of capital inflows across time and country

using panel fixed effects estimation of equation (6). In column [1] and [2] of Table 4 we

consider whether country specific explicators and global determinants impact upon aggregate

capital inflows. Column [1] includes all potential determinants and column [2] deletes

insignificant explanatory variables in a general-to-specific approach that we prefer. The

country specific explicators are financial openness, institutions, human capital, economic

growth and real interest rates. The estimated coefficients in column [2] on institutions and

financial openness are both positive and important, in that they are both statistically significant

at the 5% level. This is consistent with the suggestion of North (1994) and evidence in Alfaro

et al. (2008) that institutions matter. In contrast our measure of human capital is not a

5This section uses annual observations, since our key determinants are provided on an annual basis.6As robustness we also considered the Barro and Lee (2000) dataset and results were not quantitatively different.

IHME data was preferred since it is available at an annual frequency.

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statistically significant determinant of capital inflows: hence, we have no evidence in this table

to support the Lucas (1990) argument that human capital explains the level of capital flows to

emerging markets. In addition, country specific macroeconomic determinants output and

interest rates are insignificant pull factors. Table 4 column [2] also considers global

determinants and finds an important role for real commodity prices and advanced economieseconomic growth, consistent with Table 3, also in addition to short and long US interest rates.

Whilst column [2] has a significant F-statistic, rejecting the joint null hypothesis that our

coefficients are equal to zero, the R2 statistic indicates that we explain around a fifth of the

total variation in the capital inflow data.7

In Table 4 we can also examine the determinants of idiosyncratic capital inflows using

equation (7). This is the aggregate data filtering out common components. Using panel fixed

effects we find that institutions and openness are again highly important for this idiosyncratic

country data in column [4]. Human capital is again unimportant for idiosyncratic capital

inflow data, since it is not significant at the 10% level in column [3] and we delete this

determinant. In column [3] of Table 4, we see that domestic factors like output and interest

rates are again unimportant and they are deleted from estimation in column [4]. Given the

idiosyncratic has extracted global components we anticipate that the global determinants shall

be unimportant for idiosyncratic capital flows. Column [3] indicates that our method for

accounting for global factors is coherent since none of the global determinants are in fact

significant. Indeed, de-factoring the data means only country specific factors now matter and

the global determinants have been filtered out. In this case the R2statistic indicates that we

explain around 10% of the total variation in the capital inflow data. This implies that the

proportion of variation in the data that we explain is approximately equivalent between the

common factor and the idiosyncratic element.8

We also examine the extent to which the country specific determinants are important

for disaggregate capital flows using panel fixed effects estimation. We use a general to

specific methodology and present only the final regression results in Table 5. Overall these

7As recommend by a referee we experimented with including a time trend in Table 4 and also tested for panel

cointegration. These estimations, available upon request, indicate that key results are not sensitive to including a

deterministic trend nor indicative of a spurious regression.8 We also examined whether emerging markets are exporters of commodities when assessing the impact of

commodity prices on capital flows. We used data from UNCTAD and WTO to construct an interaction dummy

for export commodity dependence. However, this interaction was not statistically significant.

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highlight the importance of financial openness and institutions for bank and bond flows to

emerging markets. We find a role for human capital, although this seems paradoxical. Human

capital is important for total disaggregate flows and for idiosyncratic flows, but with the

opposite sign. This negative sign was not robust to the inclusion of a time trend. Real

commodity prices are positive for total bank and total equity flows: this mirrors the results inTable 4. There are some counter-intuitive signs for short-run yields on bank and bond flows,

but not for equity. The uncertainty measure is important for bond and equity flows, although

this is more believable for equity since the sign is consistent with results in Table 3 and this

global determinant operates through total equity flows.

3.6 Volatility of Capital Flows

For an emerging market it is not only the level of capital flows that matters, it is also

important to consider the nature and determinants of the volatility of inflows. This is

suggested in the related literature, in which it is assumed that rapid reversals of capital flows

(i.e. high volatility) have negative economic consequences. In this section we investigate the

degree of co-movement across countries in volatility of capital inflows focusing upon global

volatility flows. This provides formal statistical information on the extent to which individual

countries themselves are dependent upon global capital flows and hence are not entirely

responsible for the behaviour of capital markets that confronts them. We use a rolling window

of the standard deviation of 12 monthly observations to measure the volatility of capital

inflows. Table 6 presents our PANIC results for the co-movement of inflow volatility.

Table 6 provides evidence of co-movement of volatility for the aggregate flows.

Following Bai and Ng (2002), the information criteria indicate at least one principal

component in the aggregate data. This suggests that capital flow volatility, in addition to

potential country-specific determinants, has many commonalities across countries. This result

also stands for a panel time series of disaggregate inflows, since there is evidence of co-

movement in the volatility of bank, bond and equity flow indicated by the information criteria.

The data is also stationary as suggested by the rejection of the null of nonstationarity for the

factor and idiosyncratic data.

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What determines this global volatility in capital inflows to emerging markets? From

Table 7 the main result is that the volatility of aggregate flows is determined by real

commodity prices and long-run US interest rates. RCPt and RLRUSt appear to be highly

correlated with aggregate volatility, i.e. correlation coefficient of 0.50 and -0.53 respectively.

Using bivariate regressions we find thatRCPtandRLRUStare statistically significantly relatedto aggregate flow volatility, denoted by the superscripts in Table 7. Short rates are also

important for some of the short term volatility of capital flows but with a less negative

correlation. It again may be the case that as interest rates fall and become more stable

investors look for alternative, higher and more risky rates of return elsewhere. This aggregate

volatility evidence is replicated most strongly for the disaggregate bank and equity flows,

consistent with the evidence for the level of capital inflows.

4. Conclusion

This paper considers the nature and determinants of capital inflows to emerging

markets. We examine both aggregate and disaggregate capital inflows since they do not

display the same time series behaviour, depend upon the same shocks and have the same

economic implications. We find important similarities and differences in the cross country

behaviour of financial flows of different asset types. For example, we find evidence of

considerable cross country correlation in bank and equity flows according to our PANIC

approach. These consequently influence aggregate flows, which may primarily be a reflection

of bank and equity capital inflows. In contrast there was slightly less PANIC evidence that the

level of bond flows is correlated across countries.

We went on to consider the potential determinants of the waves in financial

globalization. We set out an important channel for financial globalization operating through

long rates and impacting emerging markets. We find that real US long-run interest rates are an

important determinant of disaggregate bank and equity capital inflows. Bernanke et al. (2011)

and Byrne et al. (2012) set out how the rapid increase in global savings that preceded, and may

have caused the recent Global Financial Crisis (the Global Savings Glut), operated on long-

run rather than short-run interest rates and would in turn have important consequence for

emerging markets. A fall in long-run returns in bonds causes investors to direct funds to

emerging markets. There is evidence of a less important role for short term interest rates in

driving capital inflows in our results. Hence, US monetary policy which operates through

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short term interest rates may be having a relatively less powerful effect in emerging markets.

Real commodity prices also appear to be important for equity and aggregate capital flow data,

but less so for bank flows, where US long rates clearly dominate. Frankels (2008) suggestion

of a symbiotic relationship between commodity prices and interest rates is therefore unlikely

to be the whole story explaining bank flows. Risk is important for capital flows, in particularfor disaggregate equity flows. We also were able to identify common elements in the volatility

of capital inflows. This suggests that some of the negative implications of capital flows (i.e. an

abrupt discontinuation of capital inflows or Sudden Stops) may be less the result of specific

policies associated with particular emerging market economies but may be largely generic to

this investment group.

Finally, our methodology allows us to consider the potential determinants of an

individual countrys experience with capital inflows and to discriminate between competing

hypothesis from North (1994) and Lucas (1990). We found evidence of an important role for

de jurefinancial openness, see Chinn and Ito (2008), and institutions, following North (1994)

and Alfaro et al. (2008). In contrast the idea that human capital is less important for the level

of aggregate or idiosyncratic capital inflow stands, which undermines the idea that human

capital explains the Lucas Paradox that financial capital does not flow to emerging markets

despite a high marginal product of capital. This result was robust to alternative measures of

human capital. These results matter for emerging market economies because they imply it

may not be sufficient to remove capital controls to benefit from global capital flows. To gain

from future waves of financial globalization, emerging markets economies should therefore

have increased financial openness and aim to strengthen their institutions.

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Data Sources Appendix

We use quarterly Capital Inflowin US Dollars from Euromoney Bondware and Loanware.

This disaggregate data is for Equity, Bond and Bank flows. We sum the data to produce an

aggregate flow of portfolio capital. Capital Inflow has been divided by period-by-periodnominal GDP from IMF World Economic Outlook, to account for relative size of flows across

countries. Since international capital flows are typically measured in US Dollars and following

Reinhart and Reinhart (2009) all data is in US Dollars. Table A1 presents the countries that wehave data from Euromoney. We apply a four quarter moving average. We restrict attention to

countries that participate in International Capital Inflows. Hence we exclude those countries

for which we have less than four quarters of observations between 1993Q1 to 2009Q1. Outliercountries were removed. In the section of the volatility of capital flows we measure volatility

as a rolling standard deviation with a window of 12 monthly observations.

Financial Openness (FOit) Chinn and Ito (2008) produce a de jure measure of financialopenness based on capital account transactions and the extent of capital controls in 2000.

From the IMFs Annual Report on Exchange Arrangements and Exchange Restrictions. The

index has a mean zero and an increase is the index indicates increasing openness.

G7 Real GDP Growth(YtG7

) from OECDMain Economic Indicatorsand varies over time t.

Human Capital (HCit) We use Institute for Health Metrics and Evaluation data fromGapMinder on the average number of years of schooling for each country iat time t. We also

used for robustness Barro and Lee (2000) measure of the average number of years of

schooling in 2000 for each country i.

Institutional Quality (Iit)A composite index from International Country Risk Guide (ICRG).

The measure is from 40 to 87 and a rise in the index is associated with an improvement in

institutions. The twelve different institutional measures include: Government Stability,

Socioeconomic Conditions, Investment Profiles, Internal Conflict, External Conflict,Corruption, Military Involvement in politics, Religious involvement in politics, Law and

Order, Ethnic Tensions, Democratic Accountability and Bureaucratic Accountability.

Real Commodity Prices(RCPt) are from IMF International Financial Statistic. Based upon

Non-oil commodity prices deflated by US wholesale price index following Reinhart andReinhart (2009).

Real GDP Growth(Yit) is from the IMFInternational Financial Statisticsand World BankWorld Development Indicatorsand varies over country i and time t.

Real Interest Rates are from IMF International Financial Statistic. They are 3 Month USTreasury Bill Rate (RSRUSt) and 10 year US government bond yield (RLRUSt) deflated expost by the annual US Consumer Price inflation. National real interest rates (Rit) are from

World Bank World Development Indicatorsand varies over country i and time t.

VIX Index (VIXt) is a measure of US stock market uncertainty from the Chicago Board

Option Exchange.

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Table A1. Sample of CountriesAggregate (N = 29) Bank (N = 46) Bond (N = 35) Equity (N = 34)

Algeria Algeria

Argentina Argentina Argentina Argentina

Bangladesh

Belarus Belarus

Bolivia Bolivia

Bulgaria Bulgaria Bulgaria

Burkina Faso

CameroonChile Chile

Colombia Colombia Colombia Colombia

Costa Rica Costa Rica

Cote D'Ivoire

Croatia Croatia Croatia

Dominican Republic Dominican Republic

Ecuador

Egypt Egypt

El Salvador El Salvador

Estonia Estonia

Georgia

Ghana Ghana

Guatemala Guatemala

Guinea Guinea

HondurasIndonesia Indonesia Indonesia Indonesia

Iran Iran

Jamaica

Jordan Jordan Jordan Jordan

Kazakhstan Kazakhstan

Kenya Kenya

Latvia Latvia Latvia

Lebanon Lebanon

Lithuania Lithuania Lithuania Lithuania

Macedonia

Malawi

Malaysia

Mauritius

Mexico Mexico Mexico

Morocco Morocco Morocco Morocco

Mozambique

Namibia

Nigeria

Oman

Pakistan Pakistan Pakistan

Panama

Papua New Guinea

Peru Peru Peru Peru

Philippines Philippines Philippines Philippines

Poland Poland Poland Poland

Qatar

Romania Romania Romania Romania

Senegal

Slovak Republic Slovak Republic

South Africa South Africa South Africa South Africa

Sri Lanka Sri Lanka Sri Lanka Sri LankaTanzania

Thailand Thailand Thailand Thailand

Trinidad and Tobago

Tunisia Tunisia Tunisia Tunisia

Turkey Turkey Turkey Turkey

Ukraine Ukraine Ukraine Ukraine

Uruguay Uruguay

Venezuela Venezuela Venezuela

Vietnam Vietnam Vietnam

Zimbabwe Zimbabwe

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Figure 1. Principal Component of Capital Inflows to Emerging Markets

Notes:this figure contains the first principal component extracted from the panel dataset of Bank

(f_bank), Bond (f_bond) and Equity (f_equity) and Aggregate Capital Inflows (f_agg).

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Figure 2. Cross Plot of Real Long US Interest Rate and Bank Factor

Notes: OLS estimation indicates there is a strong and statistically significant negative relationship

between Real Long US Interest Rates (RLRUSt) and the common factors of bank capital inflows

(f_bankt). Time period is 1993Q3 to 2008Q3 and T= 61.

Figure 3. Cross Plot of Real Commodity Prices and Equity Factor

Notes: There is a positive relationship between the common factor in capital flows for equity

(f_equityt) and real commodity prices (RCPt). This relationship is statistically significant,

although there is a slightly smaller R2and t-statistic than for regression between the factor and

real long-run interest rates. Time period is1993Q3 to 2008Q3 and T= 61.

f_bankt = 0.0080.001RLRUSt(t=10.10) (t=5.36)

R2= 0.33

f_equityt =0.007 + 0.0001RCPt(t=3.53) (t=4.16)

R2

= 0.23

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Table 1. Uniform Spacings Analysis of Capital Inflows

Number of small

correlation pairings

Small svr Large svr

Aggregate 0.793 322 out of 406 -0.658 1.464

Disaggregate

Bank 0.894 925 out of 1035 -0.467 2.021*

Bond 0.882 525 out of 595 1.659* 1.857*

Equity 0.856 480 out of 561 0.542 2.484*

Notes: This table presents evidence on the degree of cross sectional correlation for our aggregate anddisaggregate capital inflow data. is the proportion of all possible correlations (n) that are small ( ). Ng

(2006) Spacings Variance Ratio test statistic (svr) provides evidence of whether correlation is significantly

different from zero, distributed as standard normal, therefore the 5% critical value is 1.65, and significance

at the 5% level is denoted by an asterisk (*). First order serial correlation is removed following Ng (2006),

assuming an AR(1) model. There are n= N(N-1)/2 correlations, forN = 29, 46, 35 and 34 respectively for

Aggregate, Bank, Bond and Equity flows. The time dimension is 1993Q1 to 2009Q1.

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Table 2. PANIC Analysis of Capital Inflows

FACTOR (Ft) IDIOSYNCRATIC(uit) IC1 IC2 IC3

Panel A

Aggregate -1.208 3.158* 3 3 1

Disaggregate

Bank -1.334 5.389* 5 3 1

Bond -2.094 5.197* 5 5 0Equity 0.056 8.436* 5 5 1

Panel B

Factor Correlations

Bank Factor 0.46

Bond Factor 0.36 0.40

Equity Factor 0.77 0.71 0.31

Aggregate Factor Bank Factor Bond Factor

Notes: This Table examines the statistical properties of our capital inflow data. Panel A presents evidence on whether the

common factor and idiosyncratic components are nonstationary, using Bai and Ngs (2004) PANIC approach , and the

number of common factors. We use equation (3) to decompose the dataset into common factor (Ft ) and idiosyncratic

component (uit). For the factor Ft using equation (4) we reject the null hypothesis of a unit root in the common

component for large negative values for the test statistic (less than -2.89). For the idiosyncratic component usingequation (5), we reject the null hypothesis of a unit root for large positive values of the test statistic (greater than 1.65).

Rejected null hypotheses of nonstationarity are denoted by an asterisk (*) and in bold. We identify the factor structure

using information criteria from Bai and Ng (2002), i.e. IC1 to IC3. Panel B contains correlations of the first principal

component of the Aggregate and Disaggregate data. We use Aggregate data and Disaggregate data for Bank, Bond and

Equity flows. The number of cross sections areN = 29, 46, 35 and 34 respectively for Aggregate, Bank, Bond and Equity

flows. Thetime span of the capital inflow dataset is 1993Q1 to 2009Q1 (T=65).

Table 3. Correlation and Cointegration of Capital Flow Factors and Explicators

RCPt RSRUSt RLRUSt VIXt YtG7

Aggregate 0.39b 0.27 -0.12 -0.22b 0.11a

Bank 0.08 -0.43 -0.57a 0.20 -0.24

a

Bond -0.13 -0.16b -0.46

a -0.13 -0.32

a

Equity 0.48b 0.00 -0.32 -0.09b -0.02a

Notes:This table includes bivariate numerical correlation of common factors (Ft) in capital inflows with

potential explicators. Also, this table presents evidence of the existence of one cointegrating vectors

between the type of capital flow and explanatory variable, in bold and denoted by aat 5% and bat 10%

level of statistical significance. This is based on the Johansen (1988) Trace Test Statistic, where the nullhypothesis is no cointegration. The time period is 1993Q2 to 2008Q3. Lag length determined by AIK.

RCPtis real commodity prices excluding oil, RSRUStis the real short-run US interest rate, RLRUStis the

real long-run US interest rate, VIXtis a measure of market uncertainty and YtG7is real GDP growth in

the G7.

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Table 4. Determinants of Aggregate and Idiosyncratic Country Portfolio Flows

Explicators

Aggregate Idiosyncratic

[1] [2] [3] [4]

FOit 1.382*** 1.450*** 1.435*** 1.256***

Iit 0.103** 0.102** 0.122*** 0.125***

HCit 1.372 -1.053

Yit 0.086 0.076

Rit 0.005 0.005

RCP.t 0.040** 0.034** 0.003

RSRUS.t 0.605*** 0.679*** -0.129

RLRUS.t -0.112 -0.829*** -0.046VIX.t 0.028 0.083

Y.tG7 0.476 0.511** 0.335

Constant -18.596*** -5.283* 0.329 -5.054*

NxT 365 385 365 385

N 25 25 25 25

R2 0.21 0.19 0.12 0.09

F-statistic 8.983*** 13.861*** 4.486*** 17.157***

Notes: This table presents evidence on the determinants of aggregate and idiosyncratic capital flows to

emerging markets. Table 4 estimates equation (6) and (7) in the main text by panel fixed effects.

Column [1] seeks to examine whether the following determinants are important for aggregate capital

inflows: Financial Openness (FOit), Institutions (Iit), Human Capital (HCit), country specific economic

growth (Yit) and interest rates (Rit) from recipient countries. Also column [1] contains global

determinants of capital inflows: RCP.tis real commodity prices excluding oil,RSRUS.tis the real short-

run US interest rate, RLRUS.t is the real long-run US interest rate, VIX.t is a measure of market

uncertainty and Y.tG7 is real GDP growth in the G7. Column [2] is a general to specific approach.

Column [3] repeats the analysis on the idiosyncratic or nation specific capital inflows. Idiosyncratic data

is obtained by removing the global factor using Bai and Ng (2004) from the aggregate data, see

equation (3). The time dimension is 1993 to 2009, and the data is annual here due to data availability.

Explicators that are statistically significant are denoted by asterisk: ***at 1%, **at 5% and *at 10%

level of statistical significance. The F-statistic tests the joint null hypothesis that all estimated

coefficients are equal to zero.

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Table 5. Determinants of Disaggregate Portfolio Flows

Explicators

Bank Bank

Idiosync.

Bond Bond

Idiosync.

Equity Equity

Idiosync.

[1] [2] [3] [4] [5] [6]

FOit 0.477*** 0.564*** 0.480** 0.467**

Iit 0.078*** 0.070*** 0.116*** 0.099***HCit 0.939*** -0.745*** 1.106*** -0.665** 0.448*** -0.375***

Yit

Rit

RCP.t 0.019*** -0.037*** 0.019***

RSRUS.t 0.269*** 0.283*** 0.411*** -0.134***

RLRUS.t

VIX.t 0.061** -0.027***

Y.t

0.447***

Constant -11.177*** 0.440 -9.804*** -2.254 -4.365*** 3.200***

NxT 694 694 522 522 544 544

N 45 45 35 35 34 34R 0.120 0.135 0.096 0.069 0.112 0.032

F-statistic 17.629*** 25.072*** 10.286*** 7.101*** 21.357*** 8.274***

Notes: This table presents evidence on the determinants of disaggregate total and idiosyncratic capital flows to

emerging markets. Table 5 estimates equation (6) and (7) in the main text for disaggregate data. Column [1] seeks to

examine whether the following determinants are important for disaggregate Bank inflows: Financial Openness (FOit),

Institutions (Iit), Human Capital (HCit), country specific economic growth (Yit) and interest rates (Rit) from recipient

countries. Also column [1] contains global determinants of capital inflows: RCP.tis real commodity prices excluding

oil, RSRUS.tis the real short-run US interest rate, RLRUS.tis the real long-run US interest rate, VIX.tis a measure of

market uncertainty and Y.tG7is real GDP growth in the G7. Column [2] is the estimation results for idiosyncratic bank

flows. Column [3] and [4] are for bond flows. [5] and [6] repeats the analysis equity inflows. Idiosyncratic data is

obtained by removing the global factor using Bai and Ng (2004) from the aggregate data, see equation (3). The time

dimension is 1993 to 2009, and the data is annual here due to data availability. Explicators that are statistically

significant are denoted by asterisk: ***at 1%, **at 5% and *at 10% level of statistical significance. The F-statistic

tests the joint null hypothesis that all estimated coefficients are equal to zero.

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Table 6. PANIC Analysis of Capital Flow Volatility

FACTOR IDIOSYNCRATIC IC1 IC2 IC3

Panel A

Aggregate -3.767* 9.362* 4 3 1Disaggregate

Bank -4.110* 18.335* 5 5 0

Bond -3.768* 15.508* 5 5 1

Equity -3.742* 21.758* 5 5 5

Panel B

Factor Correlations

Bank Factor 0.77

Bond Factor -0.05 -0.03

Equity Factor 0.89 0.88 -0.11

Aggregate Factor Bank Factor Bond Factor

Notes: This Table examines the statistical properties of the volatility of our capital inflow data. Panel A presentsevidence on the number of common factors and whether the common factor and idiosyncratic components of volatility

are nonstationary, using Bai and Ngs (2004) PANIC approach. Time period is 1994Q1 to 2008Q4. Number of cross

sections isN = 29, 46, 35 and 34 respectively for Aggregate, Bank, Bond and Equity flows. Volatility is measured as a

rolling standard deviation with a window of 12 monthly observations. Rejected null hypotheses of nonstationarity are

denoted by an asterisk (*) and in bold. Panel B contains correlations of the first principal component of the Aggregate

and Disaggregate volatility data. See Notes to Table 2 for more details.

Table 7. Explicators of Common Factors in Flow Volatility

RCPt RSRUSt RLRUSt VIXt YtG7

Aggregate 0.50a -0.29a -0.53a -0.04 -0.22

Bank 0.36a -0.57a -0.55a 0.23b -0.48a

Bond 0.00 0.25b -0.05 -0.09 0.18

Equity 0.44a -0.43a -0.56a 0.17 -0.35a

Notes:The values in this Table represent bivariate correlations of volatility of the various capital flows

factors with explanatory variables. The time period is 1994Q1 to 2008Q4. Lag length determined by

AIK.RCPtis real commodity prices excluding oil,RSRUStis the real short-run US interest rate, RLRUSt

is the real long-run US interest rate, VIXt is a measure of market uncertainty and YtG7 is real GDP

growth in the G7. Statistical significance of the correlation in bold denoted by superscript aat 5% and b

at 10% level of statistical significance.

Documents

International Capital Flows to Emerging Markets- National and Global Determinants