Description
This paper aims to uncover potential contemporaneous relationship between foreign
portfolio investment (FPI) and another popular type of cross-border investment outflow, namely,
foreign direct investment (FDI).
Journal of Financial Economic Policy
Portfolio investment outflow and the complementary role of direct investment
Abdullah Noman Mohammad Nakibur Rahman Atsuyuki Naka
Article information:
To cite this document:
Abdullah Noman Mohammad Nakibur Rahman Atsuyuki Naka , (2015),"Portfolio investment outflow
and the complementary role of direct investment", J ournal of Financial Economic Policy, Vol. 7 Iss 3
pp. 190 - 206
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Portfolio investment outfow and
the complementary role of direct
investment
Abdullah Noman
Department of Accounting and Finance, Nicholls State University,
Thibodaux, Louisiana, USA
Mohammad Nakibur Rahman
Department of Accounting and Finance, University of North Carolina,
Pembroke, North Carolina, USA, and
Atsuyuki Naka
Department of Economics and Finance, University of New Orleans,
Louisiana, USA
Abstract
Purpose – This paper aims to uncover potential contemporaneous relationship between foreign
portfolio investment (FPI) and another popular type of cross-border investment outfow, namely,
foreign direct investment (FDI).
Design/methodology/approach – The relationship between FPI and FDI are modeled using
simultaneous equations approach to take potential endogeneity in to account. In a panel of 45 countries
over the period of 2001-2009, FPI and FDI are found to be strategically complimentary to each other.
Findings – The two-stage least square estimates suggest existence of both statistically and
economically signifcant relationship between these two types of outfows. In particular, the FDI
outfow has empirically signifcant predictive power in explaining the FPI outfow. Similarly, the FPI
outfow also has signifcant explanatory power for the observed level of FDI outfow. Second, the FPI
has greater explanatory power for FDI outfow than the FDI for the FPI outfow.
Originality/value – The authors believe that the paper would contribute to the relevant literature in
terms of its originality and scope. The empirical fndings of the paper have valuable policy implications.
Keywords Investment, Endogeneity, International fnance, Foreign direct investment, Home bias,
Foreign portfolio investment, Simultaneous equations
Paper type Research paper
1. Introduction
In the wake of fnancial globalization, investors have larger access to international
markets for the purpose of portfolio diversifcation than ever before. Increased
integration among fnancial markets has also pushed the horizon of investment
opportunities[1]. Investment theories have demonstrated the benefts of international
diversifcation in terms of enhanced expected returns and reduced risk. Standard asset
JEL classifcation – F21, F30, F32
The authors would like to thank the editor and the anonymous reviewers for the comments and
suggestions that have resulted in substantial improvement of this paper.
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
7,3
190
Received31 March2014
Revised20 October 2014
Accepted9 December 2014
Journal of Financial Economic
Policy
Vol. 7 No. 3, 2015
pp. 190-206
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-03-2014-0024
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pricing theories, like the modern portfolio theory of Markowitz (1952) and the capital
asset pricing model of Sharpe (1964), suggest that investors are better off holding the
most diversifed portfolio possible.
One attraction of international portfolio diversifcation is the potential beneft from
the growth differential of various fnancial markets. The growth rates of the mature and
developed economies are generally slower than those of emerging ones. Investment
portfolios can take advantage of this differential. An important condition for successful
diversifcation is, therefore, that fnancial markets across borders are not highly
correlated. If returns across borders are highly correlated, international diversifcation
might not offer any benefts. Nevertheless, given the fact that national economies rise
and fall following different cycles, a carefully chosen set of national fnancial markets
should offer prospects of lower risks to investors.
Despite the potential benefts of international portfolio diversifcation, the observed
pattern of portfolio weights allocation across international fnancial markets actually
shows a substantial lack of diversifcation. Chan et al. (2005, pp. 1505-1508) report
evidence of home bias between 1999 and 2000. Fidora et al. (2007) report comparative
fgures on the extent of home bias across mature and emerging fnancial markets. There
is an extensive body of literature available that investigates the nature and
determinants of cross-border investment fows in international fnance. Early studies
including French and Poterba (1991) and Tesar and Werner (1995) identify existence of
the home bias. A related stream of literature studies the correlation between domestic
savings and investment at macro level. Feldstein and Horioka (1980) discover the
apparent puzzle that there is high correlation between domestic savings and domestic
investment even among the fnancially developed countries. This observed high
correlation is indicative of low level of integration among fnancial markets across
national borders[2]. Existence of home bias and the low level of observed capital
mobility have led researchers to inquire in to which determinants contribute to the
capital fow beyond national borders.
There are relatively few researches that directly investigate the role of past
foreign direct investment (FDI) in determining foreign portfolio investment (FPI)
fow in international investments. Andrade and Chhaochharia (2010) present that
past history of FDI positions predict the US portfolio investment worldwide. A
possible explanation is that history of FDI position generates information about
risk-sharing characteristics of the host country and absorption capacity of the local
market. Their fndings present relative importance of distant past history (a decade
earlier) with backward looking expectation. Theories developed by Goldstein and
Razin (2006) and Razin and Sadka (2007), however, predict contemporaneous
relationship between FDI and FPI. With regards to the relationship between foreign
portfolio and direct investment outfow, there is strong possibility that these fows
would highly correlate with each other.
This paper seeks to uncover potential contemporaneous relationship between
FPI and another popular type of cross-border investment outfow, namely, FDI. The
relationship between FPI and FDI are modeled using simultaneous equations
approach to take potential endogeneity in to account. In a panel of 45 countries over
the period of 2001-2009, FPI and FDI are found to be strategically complimentary to
each other. The two-stage least square (TSLS) estimates suggest existence of both
statistically and economically signifcant relationship between these two types of
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outfows. In particular, the FDI outfow has empirically signifcant predictive power
in explaining the FPI outfow. Similarly, the FPI outfow also has signifcant
explanatory power for the observed level of FDI outfow. Second, the FPI has greater
explanatory power for the FDI outfow than the FDI for the FPI outfow. The
fndings are robust to alternative specifcations. A number of diagnostic test results
also validate the fndings.
2. Literature review
2.1 Benefts of foreign investment
The important role of international capital fow in promoting fnancial and economic
well-being can hardly be overemphasized. Cross-border equity investments have been
considered as a virtue by many economists. For example, Rogoff (1999) suggests that
equity fnance is more favorable than debt fnance for economic growth. Foreign
investment may provide with necessary capital to countries that fall short of required
level of domestic saving. Furceri and Borelli (2008) outline such importance of foreign
capital for emerging Europe and The Commonwealth of Independent States (CIS)
countries. Foreign investment transfers not only fnancial capital to recipient nations, it
also provides advanced technology and managerial know-how. Li et al. (2012) survey the
relevant literature highlighting the interdependence between macroeconomic
conditions and international trade and capital fow across different countries and
regions. More recently, Reinhardt et al. (2013) have presented empirical fndings that
underline the importance of fnancial openness in cross-border capital fows and
economic development.
Sadik and Bolbol (2001) document evidence of technology spillover, resulting
from foreign investment in a set of Arab countries. Woo (2009) investigates impact
of foreign investment on factor productivity and technological transfer in a large set
of developing countries and fnds robust results of positive impact of foreign
investment on productivity growth. On the other hand, Durham (2004) does not fnd
any lasting impact of FDI and FPI on economic growth in a set of 80 countries but
uncovers some evidence of positive effects of foreign capital fows being contingent
on the “absorptive capacity” of the host economies. The study employs data over the
period of 1979 to 1998 and includes both developed markets like USA and the UK
and developing ones like Kenya and Indonesia. The fndings of this paper need to be
re-examined using latest data set, as the absorptive capacity has signifcantly
increased over the past decade. Similarly, it is also important to break down the
large panel into small ones according to their stage of fnancial development, so that
much deeper insights could be obtained. In a recent paper, Boubakri et al. (2013)
show that FDI and FPI fow can foster privatization process in an economy by
creating favorable investment environment. Using data from 55 developing nations
for the period of 1984-2006, the authors show that both FDI and FPI can contribute
to enhancing fnancial and economic development.
In addition to macroeconomic benefts of foreign investment, the benefts to
individual investors have also been extensively studied. Empirical evidence supports
the theoretical outcome of portfolio diversifcation across-borders. For example,
Driessen and Laeven (2007) study the potential benefts of global diversifcation for a
large cross-section of countries and fnd that these benefts are largest for investors in
developing countries even after adjusting for currency risks. Further, Chiou (2009)
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reports evidence of similar benefts for US investors too, even in the presence of some
investment constraints.
Persistent level of home bias and the resulting low cross-border international capital
fows are costly in terms of low degree of risk sharing across nations. Lau et al. (2010)
study the impact of home bias on global risk sharing in terms of increasing cost of
capital. Using a large data set, they fnd that cross-sectional variation in cost of equity
can be attributed to differences in the degree of home bias across countries. They
conclude that absence of global fnancial markets integration creates low degree of risk
sharing among nations pushing up the costs of capital.
2.2 Determinants of FPI
Portes and Rey (2005) investigate bilateral cross-border equity fows over the period of
1989-1996. They report usefulness of the gravity model in explaining the volume of
foreign capital fows. Two infuencing factors in this regard that they identify are local
market size and trading costs. Their panel includes mostly advanced markets and a few
emerging markets. Liljeblom and Lofund (2005) study the determinants of FPI in
Finland where the capital market restrictions have recently been removed. They fnd
that investment barriers and risk–return related variables are highly relevant for FPI
infow in Finland.
De Santis and Luhrmann (2009) investigate the determinants of net capital fows across
a large number of countries. Theyuse a diverse set of institutional andfnancial explanatory
variables ranging from aging to uncovered interest parity, and to predict capital fow. A
problem with this study is that it requires acquisition of huge amount of socio-economic
information by the mutual funds managers, which is not without its costs.
There are mainly two alternative theories that attempt to explain the observed lack of
international portfolio diversifcation. The frst theory holds different forms of local
restrictions responsible for low international capital fows. Empirical investigations
(Errunza and Losq, 1985 and Bonser-Neal et al., 1990) support this line of argument.
However, with increasing level of fnancial liberalization and removal of restriction, it is
argued that persistence in the home bias needs some other explanation. Information
immobility across international markets is thought as an alternative theory, explaining
suboptimal international capital fows. The core of the argument is that domestic investors
have comparative informational advantage about the local market than the foreign
investors.
Chan et al. (2005) document importance of information associated with familiarity
variables in determining cross-sectional differences in FPI fow in a set of 26 developed
and developing nations. Similarly, Mishra (2007) fnds that information fow is crucial
for bilateral equity investment. In an interesting study, Daude and Fratzscher (2008)
uncover the existence of pecking order in international capital fow. Their fndings
suggest that information frictions signifcantly affect foreign investment in both direct
and portfolio.
Recent literature recognizes the importance of information in determining both type
and volume of the international capital fows. Kirabaeva and Razin (2009) survey the
existing literature to uncover the current theoretical understanding on the composition
of international capital fows. They identify informational asymmetry and pattern of
risk sharing among investors to be the most infuential factors determining the nature
and volume of different capital fows across borders.
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3. Theoretical background and the hypotheses
3.1 Theoretical background
A theoretical reason for such complementarity between FDI and FPI could arise from
fnancial liberalization in the investing country itself and in the rest of the world.
Another reason may be the similarity of economic or other shocks in the investing and
host country (Acharya et al., 2007). For example, the recent global fnancial crisis has
affected many countries at the same time in a very similar fashion. A third reason for
complementarity may arise fromthe strategic behavior of corporations facing options to
invest directly or in portfolio (Pfeffer, 2008). There are some other papers that have
documented a rising level of FDI infowin many countries along with falling level of FPI.
(Aguiar and Gopinath, 2005; Acharya et al. 2007). While this might be reasonable to
think of an inverse relationship between FPI and FDI infow, but the focus of this paper
is capital outfow.
Goldstein and Razin (2006) develop a theoretical model incorporating both FPI and
FPI, and being able to predict several stylized facts of the cross-border equity fows.
Their model predicts that FDI infow is more volatile than the FPI infow. Also, FPI can
quickly response to changes in market environment, whereas FDI cannot do so because
of the complications involved in liquidation process. As result, the model predicts that if
investors anticipate possibility of liquidity crisis, they would prefer FPI to FDI. Another
prediction of their model is that as the legal environment and investors’ protection
improves in a nation, the FDI infowwill rise compared to FPI. The focus of this paper is
different fromtheir paper’s central idea. In particular, the current paper revolves around
the idea of FPI and FDI outfow, rather than infow. Capital infow and outfow can be
affected considerably differently, as documented by the contemporary literature.
3.2 The hypotheses
The following two hypotheses summarize the arguments which we empirically
investigate in the subsequent sections:
H1. The FPI outfowis a signifcant determinant of the FDI outfowfroma country.
Similarly, the FDI outfow is an important determinant of the FPI outfow from
the same country.
International portfolio and direct investors receive relevant information on certain
cross-border market from each other in that particular market. Therefore, FPI outfow
will be positively infuenced by FDI outfow from the same country. Similarly, FDI
outfow would also be directly affected by the level of FPI from the same country. In
brief, the relationship between these two types of foreign investments is that of strategic
complementarity:
H2. The predictive power of FPI outfow in explaining FDI outfow is greater than
that of FDI outfow explaining the FPI outfow.
Given the nature of liquidity of portfolio investment and less liquidity of direct
investment, it is expected that FPI investors can respond to the changes in the market
environment quicker than the FDI investors. This enables the portfolio investments to
mobilize greater amount of information than the direct investment for the prospective
investors. This observation leads to the second hypothesis of this paper. Together, these
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two hypotheses provide a basis for understanding the empirical relationship between
FPI and FDI outfows.
4. The empirical specifcation
4.1 The baseline model
We begin with a baseline model that outlines the relationship between FPI and FDI.
Consider the following set of simultaneous equations:
FPI
it
? ?
0P
? ?
1P
FDI
it
? ?
2P
FO
it?1
? u
it
(1)
FDI
it
? ?
0D
? ?
1D
FDI
it
? ?
2D
TO
it?1
? v
it
, (2)
where FPI
it
and FDI
it
are expressed as percentage of the gross domestic product (GDP),
FO
it?1
is fnancial openness for which the Chinn–Ito index (KAOPEN)[3] is used (Chinn
and Ito, 2006, 2008), TO
it?1
is trade openness, as measured by the total import and export
as percentage of GDP, u
it
and v
it
are the classical error terms. Subscripts i and t are
indexes for country and time and subscripts P and D with the coeffcients indicate
portfolio investment and direct investment, respectively.
Equations (1) and (2) indicate that both FPI and FDI are determined jointly, and there
may exist of reverse causality between these variables which are investments across
borders. In this baseline model, the predetermined variables are two openness
measures[4], one for FPI (namely, FO
it?1
) and the other is for FDI (namely, TO
it?1
).
According to the theoretical expectation, both of these openness measures are supposed
to have a positive impact on the dependent variables on both equations. As for the
relationship between FPI and FDI, the equations postulate a contemporaneous fow of
information between them. As both of these are investment outfows, one would expect
a positive relationship between them.
A particular weakness of the above model is that it does not take into account the
overall fnancial environment of the economy. Inclusion of economic determinants to the
baseline model would allowus to ensure robust results. The extended model we consider
in this paper is:
FPI
it
? ?
0P
? ?
1P
FDI
it
? ?
2P
FO
it?1
? ?
3P
Return
it
? ?
4P
RER
it
? ?
5P
Inf
it
? u
it
(3)
FDI
it
? ?
0D
? ?
1D
FPI
it
? ?
2P
TO
it?1
? ?
3D
GDPGr
it
? ?
4P
RER
it
? ?
5P
Inf
it
? v
it
(4)
where, FPI, FDI, FO, TO, Return and GDPGr are as defned before. Two other variables
that are RER, the real exchange rate with reference to the US dollar, and Inf, the infation
rate. These variables are common to both equations, as they are assumed to be affecting
both types of investment outfows.
As for the H1 discussed in the previous section, one would expect ?
1P
?0 and ?
1D
?0
in all specifcations. The acceptance of the null hypothesis would indicate the existence
of strategic complementarity between FPI and FDI outfows. In terms of the regression
coeffcients, the H2 implies that the magnitude of FPI in the FDI equation will be larger
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than the size of the coeffcient on FDI in the FPI equation. In other words, we would
expect that ?
1D
??
1P
.
5. Data and estimation methodology
5.1 Data description
FPI data are collected from the Coordinated Portfolio Investment Survey (CPIS)[5] of
International Monetary Fund (IMF). The survey collects information on cross-border
portfolio investment directly from the reporting country. We begin with all countries in
the survey and then exclude those not having data. This leaves us with 45 countries with
annual data from 2001 to 2009, for which we do not have any missing data. Although
researchers employed the CPIS data with many different objectives, few papers have
used the full set of data for the period between 2001 and 2009. For example, Lane and
Milesi-Ferretti (2008) and Aviat and Coeurdacier (2007) used for 2001 only; Mishra
(2007) for 1997, 2001 and 2002; and Lee et al. (2012) from 2001 to 2007.
Table I presents the list of the countries. The countries of our sample are divided into
three market categories following the Morgan Stanley Capital International (MSCI)
market classifcation. They consist of 24 developed markets, 15 emerging markets and
six frontier markets. FDI data are fromthe Global Development Finance database of the
World Bank. Both FPI and FDI are expressed as percentage of current value of GDP
which is from the World Development Indicators (WDI) of the World Bank.
For fnancial openness (FO) the Chinn–Ito index (KAOPEN) (Chinn and Ito, 2006,
2008) is collected from the Web site of its authors[6]. The trade openness (TO) is
calculated as the total value of imports and exports as percentage of GDP and both
current dollar values of imports and exports are collected from the WDI of the World
Bank. The variable Return is the yearly return (log difference) on the MSCI equity index
for each country of our sample and the variable GDPGr is the growth rate in the GDP.
The real exchange rate (RER) is obtained from the International Financial Statistic and
the infation rate (Inf) is from World Economic Outlook, both maintained by the IMF.
Table II provides description all variables as well as their sources.
Table III presents the descriptive statistics of the variables that appear inall three models
considered. The correlations among these variables are presented in Table IV. As can be
Table I.
List of countries in
the sample
Developed markets (24) Emerging markets (15) Frontier markets (6)
Australia Italy Brazil Malaysia Argentina
Austria Japan Chile Philippines Estonia
Belgium Netherlands Colombia Poland Kazakhstan
Canada New Zealand Czech Republic Russia Lebanon
Denmark Norway Egypt South Africa Romania
Finland Portugal Hungary Thailand Ukraine
France Singapore Indonesia Turkey
Germany Spain South Korea
Greece Sweden
Hong Kong Switzerland
Ireland United Kingdom
Israel United States
Note: The countries of the sample are categorized following the MSCI market classifcation
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Table II.
Variable description
Variable Description Source
FPI FPI outfow (as % of GDP) The CPIS, IMF
FDI FDI outfow (as % of GDP) Global Development Finance, The World
Bank
FO Financial openness The Chinn–Ito index (KAOPEN) (Chinn and
Ito, 2006; 2008)
TO Trade openness: imports and exports
(as % of GDP)
WDI, The World Bank
Return Local equity market return Morgan Stanley Capital International (MSCI)
GDPGr GDP growth WDI, The World Bank
RER Real exchange rate International Financial Statistic (IFS)
Inf Infation rate World Economic Outlook, IMF
Market Dummy variable 0 ?Developed; 1 ?Emerging; 2 ?Frontier
Table III.
Descriptive statistics
Variable Mean Median SD Minimum Maximum
FPI 0.245 0.104 0.393 0.000 2.577
FO 1.482 2.478 1.346 ?1.844 2.478
Return 0.073 0.182 0.408 ?1.825 0.934
FDI 3.528 1.837 5.825 ?4.876 49.079
TO 0.945 0.729 0.740 0.205 4.459
GDPGr 2.997 3.070 3.664 ?14.800 13.500
RER 1.976 1.280 2.489 ?0.707 9.474
Inf 1.363 ?0.206 5.380 ?5.926 51.409
Notes: Descriptive statistics of the sample are presented in this table. For description on the variables,
see table II above; FPI and FDI are FPI outfow and FDI outfow measured in US dollar and expressed
as percentage of current GDP; TO is total current value of imports and exports as percentage of GDP;
the only variable that is converted in to natural logarithm is real exchange rates (RER)
Table IV.
Pearson’s correlation
coeffcients among
variables
Variable FPI FO Return FDI TO GDPGr RER Inf
FPI 1.000
FO 0.386* 1.000
Return ?0.018 ?0.095 1.000
FDI 0.510* 0.341* ?0.122* 1.000
TO 0.491* 0.176* ?0.046 0.502* 1.000
GDPGr ?0.108* ?0.274* 0.132* ?0.065 0.116* 1.000
RER ?0.138* ?0.113* 0.089 ?0.100* ?0.113* 0.110* 1.000
Inf ?0.246* ?0.510* ?0.035 ?0.189* ?0.164* 0.117* 0.134* 1.000
Notes: This table presents pairwise correlation coeffcients among all variables at hand over the full
sample. The 5% critical value (two-tailed) for the null that the population correlation coeffcient is zero
is 0.098 for total number of observation equaling 405; *indicates rejection of the null of no correlation
at 5% level of signifcance
197
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seen, most of the correlation coeffcients are statistically signifcant. The correlation
coeffcient between FPI and FDI is positive and signifcant. A coeffcient as high in
magnitude, as 0.510 encourages us to inquire estimating the regression models to measure
their causal relationship. Individually, FPI appears to be signifcantly correlated with all
variables except for Return. Similarly, FDI is also signifcantly correlated with all other
variables except for GDPGr which is being used as a proxy for return in the real economy.
5.2 Estimation methodology
The primary focus of this paper is to see how FDI outfow affects the FPI outfow in the
presence of some other determinants as well as in isolation from any other variables.
The empirical specifcations outlined in the previous section, however, indicate the
presence of possible reverse causality between FPI and FPI outfows from a country to
international destinations. This creates the endogeneity problemwhere the explanatory
variables may be correlated with the error terms violating assumptions of classical
linear regressions. A well-known result in the econometrics literature is that ordinary
least squares (OLS) estimates are biased (also known as the simultaneity bias) and
inconsistent in the presence of endogeneity problem. Asolution to the simultaneity bias
is to use the instrumental variable approach. In particular, in this paper, the TSLS
method is used to obtain consistent estimates.
The order and rank conditions must be satisfed to estimate a systemof simultaneous
equations As for the order condition in the systems, let us consider the baseline model,
equations (1) and (2): the system of two equations has two endogenous variables,
namely, FPI
it
and FDI
it
and also two predetermined variables, FO
it?1
and TO
it?1
, and the
order condition for the systemis met. Also, this condition is met for the extended and the
full model which have the same two endogenous variables, but there are more than two
excluded exogenous variables. The rank condition for equation (1) is satisfed, as we
have trade openness, TO, in equation (2) and not included in equation (1), which is
assumed to be an important determinant of FDI outfow. Similar arguments can be made
in relation to equation (2), which also satisfes the rank condition. Given that we have
both order and rank conditions satisfed for our system at hand, each equation is
identifed and can be consistently estimated as a single equation using instrumental
variable method using the TSLSs.
A number of diagnostic tests are reported with the estimation results. The Hausman
(1978) test assumes consistency of OLS estimates as against TSLS estimates and is
distributed as chi-squared with degrees of freedom equaling the number of endogenous
variable. This test is usedtochoose betweenthe OLSandTSLSresults. The endogeneitytest
corresponds to test of endogeneitypresentedbyWooldridge (2002), whichassumes absence
of endogeneity under the null hypothesis (not reported here). The Sargan test concerns with
the null hypothesis that all instruments used in the estimation are valid. This test is used
whenthe model is over-identifedwithmore instruments beingavailable thanthe number of
endogenous variables. The weak instrument test by Stock and Yogo (2005) tests the
assumption that instruments used are weak.
6. Empirical results
Table Vpresents the estimation results of the extended model given in equations (3) and
(4)[7]. Results from both OLS and TLS are presented in Table V. The results for the FPI
equation show that FDI
it
is signifcant in both OLS and TSLS. As for other variables
JFEP
7,3
198
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Estimation results of
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outfow
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included in the model, FO
it?1
is signifcant in OLS only, Return
it
and RER
it
are signifcant
in both, while Inf
it
is insignifcant in both estimation. The estimate of FDI
it
has the
positive sign as expected a priori, which indicates positive contemporaneous
relationship between FPI and FDI outfows. Similarly, in the results for the FDI
equation, FPI
it
is signifcant both in the OLS and TLS estimation and TO
it?1
is
signifcant in the OLS, but not in the TSLS estimation.
The results support a theoretical model developed by Goldstein and Razin (2006).
This fnding supports the notion of strategic complementarity between these outfows.
In terms of the magnitude of the estimated coeffcient on FDI
it
, (0.067 in TSLS) is higher
than that on FPI
it
, (13.357 in TSLS). The coeffcients are positive and signifcant in both
OLS and TSLS regressions. The results confrm the strategic complementarity
hypothesis. Given that the coeffcient on FPI
it
in the FDI equation is much larger than
that on the FDI
it
in the FPI equation, we also support for the H2.
Panel B in Table V reports the diagnostics tests for a comparison between the OLS
and TSLS regressions. The Hausman test statistic rejects the null hypothesis that OLS
estimates are consistent in favor of the TSLS estimates for both equations (3) and (4).
The Sargan overidentifcation test is rejected for the FDI equation, but not for the FPI
equation. The third diagnostic test statistic reported is the weak instrument test which
rejects the null hypothesis that the instruments are weak or mostly invalid for both
equations. As mentioned in the estimation methodology part, we adopt a TSLS
estimation method to avoid the simultaneity bias that would arise fromthe endogenous
relationship between FPI
it
and FDI
it
. This possibility is formally tested following the
methodology outlined by Wooldridge (2002). The test results show that these variables
are indeed endogenously related to each other[8]. Because the endogeneity tests
indicate presence of endogeneity in the regression models, the TSLS method is
proper to use instead of the OLS. In this respect, we frst estimated the FPI
it
and FDI
it
using the exogenous and predetermined variables on the right-hand sides. The frst
stage regression results for the FPI and FDI equations are reported in Panel C of the
same table. As can be seen from the reported results which indicate that fnancial and
trade openness variables (in their lagged terms) are signifcant determinant for both
FPI
it
and FDI
it
. Anumber of other included variables are also signifcant. This fnding is
not surprised, given the weak instrument test results reported in Panel B of Table V.
In the results reported in Table Vfor equations (3) and (4) and the unreported results
based on equations (1) and (2), we obtain evidences in favor of the both hypotheses
outlined earlier. In particular, the FDI outfow has empirically signifcant predictive
power in explaining the FPI outfow. Similarly, the FPI outfow also has signifcant
explanatory power for the observed level of FDI outfow. Second, the FPI has greater
explanatory power for FDI outfow than the FDI for the FPI outfow.
Given the preliminary results reported in Table V, we check for the robustness of our
fndings on the complementary relationship between FPI and FDI. First, we replace the
returns in equation (3) with the risk-adjusted returns. The risk-adjusted returns are
calculated as the difference between raw returns and the world market returns proxied
by the MSCI world market returns[9]. Our fndings are presented in Table VI. The
results showthat the newly replaced risk-adjusted returns are still signifcant, while the
magnitude and signifcance of other variables are not much affected by this
replacement. Table VI also presents the diagnostic test results and the frst stage
regression results for the TSLS method.
JFEP
7,3
200
D
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:
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2
2
4
J
a
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a
r
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2
0
1
6
(
P
T
)
Table VI.
Estimation results of
the full model with
risk-adjusted return
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201
Portfolio
investment
outfow
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
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N
I
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Y
A
t
2
1
:
5
2
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table VII.
Estimation results of
the full model with
market dummy
T
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JFEP
7,3
202
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2
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6
(
P
T
)
This paper includes data from the developed, emerging markets and frontier markets.
We are interested in fnding out if the results found earlier are different for different
types of markets. To see this, we modify equations (3) and (4) with a dummy variable for
the type of market, with 0 for developed, 1 for emerging and 2 for frontier markets. The
results, reported in Table VII, indicate that the market dummy is insignifcant in the FDI
equation. In the FPI equation, the market dummy is negative and signifcant in both
OLS and TSLS estimation. Magnitude and signifcance of other coeffcients remain
similar to what have been reported earlier. A negative value of the estimated dummy
coeffcient would lower the value of the intercept for emerging market and frontier
markets.
7. Concluding remarks
This paper aims at understanding the empirical relationship between FPI and FDI
outfows. In the absence of any general model incorporating both types of outfows from
a nation to the rest of the world, it outlines three different specifcations to examine the
direction and magnitude of such interrelationship. Following the literature, albeit
scanty, two hypotheses are developed. The frst one postulates a positive and
complementary relationship between FPI and FDI outfows. The second hypothesis
suggests greater impact of FPI on FDI outfowthan the infuence of FDI on FPI outfow.
To take the potential presence of simultaneity bias in the relationship between
FPI and FDI, the econometric specifcations followed simultaneous equations
approach. The parameters are then estimated using TSLS method. Anumber of post
estimation diagnostic tests are conducted to verify the consistency of the estimates
obtained. Overall, the estimation results support for both hypotheses. As for the FPI
equation, the sign of the coeffcient on FDI is positive in all three specifcations. On
the other hand, the FDI equation in all three specifcations yields a positive sign on the
coeffcient of FPI. We observe that the impact of FPI on FDI is greater than the impact
of FDI on FPI across the national border at an aggregate level.
Future extension of this paper could be directed to address some of the
shortcomings of the current paper. First, a future research requires the theoretical
foundation for empirical investigation. The current literature is scanty in terms of
economics models incorporating both FPI and FDI, and more so for their outfow
rather than infow. Second, although this paper looks at the whole data set as a
sample of uniformand single universe, the fact that the countries in the sample come
from different types of markets necessitates verifying results across different types
of markets. Third, different exogenous variables and predetermined endogenous
variables could be used as instruments to obtain better and more robust results. Any
future extension of this paper should take these issues in to account and reach to a
conclusion regarding the interrelationship between foreign portfolio and direct
investment outfow.
Notes
1. Refer to Prasad et al. (2003) and Stulz (2005) for global fnancial liberalization and its impact
on international fnance and investment.
2. For a review, see Coakley et al. (2004).
3. For a recent survey of different measures of fnancial openness, see, Clark et al. (2012).
203
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(
P
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4. The openness measures are contemporaneously correlated with the FPI and FDI. Therefore,
to avoid simultaneity bias, we add lagged terms of the openness variables which are
predetermined, and so, not endogenous to the system. We thank the reviewer for bringing this
to our attention.
5. www.imf.org/external/np/sta/pi/cpis.htm
6. http://web.pdx.edu/?ito/Chinn-Ito_website.htm
7. The baseline model given in equations (1) and (2) are also estimated. The results are not
reported here for the sake of brevity and available upon request.
8. The results are available from the authors upon request.
9. The MSCI ACWI Investable Market Index is used to calculate the world market returns that
consist of large, mid and small cap assets across 24 Developed Markets and 21 Emerging
Markets countries.
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pp. 80-108.
Stulz, R. (2005), “The limits of fnancial globalization”, Journal of Finance, Vol. 60 No. 4,
pp. 1595-1638.
Tesar, L. and Werner, I. (1995), “Home bias and high turnover”, Journal of International Money
and Finance, Vol. 14 No. 1, pp. 467-492.
Woo, J. (2009), “Productivity growth and technological diffusion through foreign direct
investment”, Economic Inquiry, Vol. 47 No. 2, pp. 226-248.
Wooldridge, J.M. (2002), Econometric Analysis of Cross Section and Panel Data, MIT Press,
Cambridge, MA.
Further reading
Borensztein, E., De Gregorio, J. and Lee, J. (1998), “How does foreign direct investment affect
economic growth?”, Journal of International Economics, Vol. 45 No. 1, pp. 115-135.
Herzer, D., Klasen, S. and Nowak-Lehmann, F. (2008), “In search of FDI-led growth in developing
countries: the way forward”, Economic Modelling, Vol. 25 No. 5, pp. 793-810.
Lintner, J. (1965), “The valuation of risk assets and the selection of risky investments in stock
portfolios and capital budgets”, Reviewof Economics and Statistics, Vol. 47 No. 1, pp. 13-37.
Schwert, W. (2002), “Stock volatility in the new millennium: how wacky is Nasdaq?”, Journal of
Monetary Economics, Vol. 49 No. 1, pp. 3-26.
Corresponding author
Abdullah Noman can be contacted at: [email protected]
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
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doc_263375456.pdf
This paper aims to uncover potential contemporaneous relationship between foreign
portfolio investment (FPI) and another popular type of cross-border investment outflow, namely,
foreign direct investment (FDI).
Journal of Financial Economic Policy
Portfolio investment outflow and the complementary role of direct investment
Abdullah Noman Mohammad Nakibur Rahman Atsuyuki Naka
Article information:
To cite this document:
Abdullah Noman Mohammad Nakibur Rahman Atsuyuki Naka , (2015),"Portfolio investment outflow
and the complementary role of direct investment", J ournal of Financial Economic Policy, Vol. 7 Iss 3
pp. 190 - 206
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Portfolio investment outfow and
the complementary role of direct
investment
Abdullah Noman
Department of Accounting and Finance, Nicholls State University,
Thibodaux, Louisiana, USA
Mohammad Nakibur Rahman
Department of Accounting and Finance, University of North Carolina,
Pembroke, North Carolina, USA, and
Atsuyuki Naka
Department of Economics and Finance, University of New Orleans,
Louisiana, USA
Abstract
Purpose – This paper aims to uncover potential contemporaneous relationship between foreign
portfolio investment (FPI) and another popular type of cross-border investment outfow, namely,
foreign direct investment (FDI).
Design/methodology/approach – The relationship between FPI and FDI are modeled using
simultaneous equations approach to take potential endogeneity in to account. In a panel of 45 countries
over the period of 2001-2009, FPI and FDI are found to be strategically complimentary to each other.
Findings – The two-stage least square estimates suggest existence of both statistically and
economically signifcant relationship between these two types of outfows. In particular, the FDI
outfow has empirically signifcant predictive power in explaining the FPI outfow. Similarly, the FPI
outfow also has signifcant explanatory power for the observed level of FDI outfow. Second, the FPI
has greater explanatory power for FDI outfow than the FDI for the FPI outfow.
Originality/value – The authors believe that the paper would contribute to the relevant literature in
terms of its originality and scope. The empirical fndings of the paper have valuable policy implications.
Keywords Investment, Endogeneity, International fnance, Foreign direct investment, Home bias,
Foreign portfolio investment, Simultaneous equations
Paper type Research paper
1. Introduction
In the wake of fnancial globalization, investors have larger access to international
markets for the purpose of portfolio diversifcation than ever before. Increased
integration among fnancial markets has also pushed the horizon of investment
opportunities[1]. Investment theories have demonstrated the benefts of international
diversifcation in terms of enhanced expected returns and reduced risk. Standard asset
JEL classifcation – F21, F30, F32
The authors would like to thank the editor and the anonymous reviewers for the comments and
suggestions that have resulted in substantial improvement of this paper.
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
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190
Received31 March2014
Revised20 October 2014
Accepted9 December 2014
Journal of Financial Economic
Policy
Vol. 7 No. 3, 2015
pp. 190-206
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-03-2014-0024
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pricing theories, like the modern portfolio theory of Markowitz (1952) and the capital
asset pricing model of Sharpe (1964), suggest that investors are better off holding the
most diversifed portfolio possible.
One attraction of international portfolio diversifcation is the potential beneft from
the growth differential of various fnancial markets. The growth rates of the mature and
developed economies are generally slower than those of emerging ones. Investment
portfolios can take advantage of this differential. An important condition for successful
diversifcation is, therefore, that fnancial markets across borders are not highly
correlated. If returns across borders are highly correlated, international diversifcation
might not offer any benefts. Nevertheless, given the fact that national economies rise
and fall following different cycles, a carefully chosen set of national fnancial markets
should offer prospects of lower risks to investors.
Despite the potential benefts of international portfolio diversifcation, the observed
pattern of portfolio weights allocation across international fnancial markets actually
shows a substantial lack of diversifcation. Chan et al. (2005, pp. 1505-1508) report
evidence of home bias between 1999 and 2000. Fidora et al. (2007) report comparative
fgures on the extent of home bias across mature and emerging fnancial markets. There
is an extensive body of literature available that investigates the nature and
determinants of cross-border investment fows in international fnance. Early studies
including French and Poterba (1991) and Tesar and Werner (1995) identify existence of
the home bias. A related stream of literature studies the correlation between domestic
savings and investment at macro level. Feldstein and Horioka (1980) discover the
apparent puzzle that there is high correlation between domestic savings and domestic
investment even among the fnancially developed countries. This observed high
correlation is indicative of low level of integration among fnancial markets across
national borders[2]. Existence of home bias and the low level of observed capital
mobility have led researchers to inquire in to which determinants contribute to the
capital fow beyond national borders.
There are relatively few researches that directly investigate the role of past
foreign direct investment (FDI) in determining foreign portfolio investment (FPI)
fow in international investments. Andrade and Chhaochharia (2010) present that
past history of FDI positions predict the US portfolio investment worldwide. A
possible explanation is that history of FDI position generates information about
risk-sharing characteristics of the host country and absorption capacity of the local
market. Their fndings present relative importance of distant past history (a decade
earlier) with backward looking expectation. Theories developed by Goldstein and
Razin (2006) and Razin and Sadka (2007), however, predict contemporaneous
relationship between FDI and FPI. With regards to the relationship between foreign
portfolio and direct investment outfow, there is strong possibility that these fows
would highly correlate with each other.
This paper seeks to uncover potential contemporaneous relationship between
FPI and another popular type of cross-border investment outfow, namely, FDI. The
relationship between FPI and FDI are modeled using simultaneous equations
approach to take potential endogeneity in to account. In a panel of 45 countries over
the period of 2001-2009, FPI and FDI are found to be strategically complimentary to
each other. The two-stage least square (TSLS) estimates suggest existence of both
statistically and economically signifcant relationship between these two types of
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outfows. In particular, the FDI outfow has empirically signifcant predictive power
in explaining the FPI outfow. Similarly, the FPI outfow also has signifcant
explanatory power for the observed level of FDI outfow. Second, the FPI has greater
explanatory power for the FDI outfow than the FDI for the FPI outfow. The
fndings are robust to alternative specifcations. A number of diagnostic test results
also validate the fndings.
2. Literature review
2.1 Benefts of foreign investment
The important role of international capital fow in promoting fnancial and economic
well-being can hardly be overemphasized. Cross-border equity investments have been
considered as a virtue by many economists. For example, Rogoff (1999) suggests that
equity fnance is more favorable than debt fnance for economic growth. Foreign
investment may provide with necessary capital to countries that fall short of required
level of domestic saving. Furceri and Borelli (2008) outline such importance of foreign
capital for emerging Europe and The Commonwealth of Independent States (CIS)
countries. Foreign investment transfers not only fnancial capital to recipient nations, it
also provides advanced technology and managerial know-how. Li et al. (2012) survey the
relevant literature highlighting the interdependence between macroeconomic
conditions and international trade and capital fow across different countries and
regions. More recently, Reinhardt et al. (2013) have presented empirical fndings that
underline the importance of fnancial openness in cross-border capital fows and
economic development.
Sadik and Bolbol (2001) document evidence of technology spillover, resulting
from foreign investment in a set of Arab countries. Woo (2009) investigates impact
of foreign investment on factor productivity and technological transfer in a large set
of developing countries and fnds robust results of positive impact of foreign
investment on productivity growth. On the other hand, Durham (2004) does not fnd
any lasting impact of FDI and FPI on economic growth in a set of 80 countries but
uncovers some evidence of positive effects of foreign capital fows being contingent
on the “absorptive capacity” of the host economies. The study employs data over the
period of 1979 to 1998 and includes both developed markets like USA and the UK
and developing ones like Kenya and Indonesia. The fndings of this paper need to be
re-examined using latest data set, as the absorptive capacity has signifcantly
increased over the past decade. Similarly, it is also important to break down the
large panel into small ones according to their stage of fnancial development, so that
much deeper insights could be obtained. In a recent paper, Boubakri et al. (2013)
show that FDI and FPI fow can foster privatization process in an economy by
creating favorable investment environment. Using data from 55 developing nations
for the period of 1984-2006, the authors show that both FDI and FPI can contribute
to enhancing fnancial and economic development.
In addition to macroeconomic benefts of foreign investment, the benefts to
individual investors have also been extensively studied. Empirical evidence supports
the theoretical outcome of portfolio diversifcation across-borders. For example,
Driessen and Laeven (2007) study the potential benefts of global diversifcation for a
large cross-section of countries and fnd that these benefts are largest for investors in
developing countries even after adjusting for currency risks. Further, Chiou (2009)
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reports evidence of similar benefts for US investors too, even in the presence of some
investment constraints.
Persistent level of home bias and the resulting low cross-border international capital
fows are costly in terms of low degree of risk sharing across nations. Lau et al. (2010)
study the impact of home bias on global risk sharing in terms of increasing cost of
capital. Using a large data set, they fnd that cross-sectional variation in cost of equity
can be attributed to differences in the degree of home bias across countries. They
conclude that absence of global fnancial markets integration creates low degree of risk
sharing among nations pushing up the costs of capital.
2.2 Determinants of FPI
Portes and Rey (2005) investigate bilateral cross-border equity fows over the period of
1989-1996. They report usefulness of the gravity model in explaining the volume of
foreign capital fows. Two infuencing factors in this regard that they identify are local
market size and trading costs. Their panel includes mostly advanced markets and a few
emerging markets. Liljeblom and Lofund (2005) study the determinants of FPI in
Finland where the capital market restrictions have recently been removed. They fnd
that investment barriers and risk–return related variables are highly relevant for FPI
infow in Finland.
De Santis and Luhrmann (2009) investigate the determinants of net capital fows across
a large number of countries. Theyuse a diverse set of institutional andfnancial explanatory
variables ranging from aging to uncovered interest parity, and to predict capital fow. A
problem with this study is that it requires acquisition of huge amount of socio-economic
information by the mutual funds managers, which is not without its costs.
There are mainly two alternative theories that attempt to explain the observed lack of
international portfolio diversifcation. The frst theory holds different forms of local
restrictions responsible for low international capital fows. Empirical investigations
(Errunza and Losq, 1985 and Bonser-Neal et al., 1990) support this line of argument.
However, with increasing level of fnancial liberalization and removal of restriction, it is
argued that persistence in the home bias needs some other explanation. Information
immobility across international markets is thought as an alternative theory, explaining
suboptimal international capital fows. The core of the argument is that domestic investors
have comparative informational advantage about the local market than the foreign
investors.
Chan et al. (2005) document importance of information associated with familiarity
variables in determining cross-sectional differences in FPI fow in a set of 26 developed
and developing nations. Similarly, Mishra (2007) fnds that information fow is crucial
for bilateral equity investment. In an interesting study, Daude and Fratzscher (2008)
uncover the existence of pecking order in international capital fow. Their fndings
suggest that information frictions signifcantly affect foreign investment in both direct
and portfolio.
Recent literature recognizes the importance of information in determining both type
and volume of the international capital fows. Kirabaeva and Razin (2009) survey the
existing literature to uncover the current theoretical understanding on the composition
of international capital fows. They identify informational asymmetry and pattern of
risk sharing among investors to be the most infuential factors determining the nature
and volume of different capital fows across borders.
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3. Theoretical background and the hypotheses
3.1 Theoretical background
A theoretical reason for such complementarity between FDI and FPI could arise from
fnancial liberalization in the investing country itself and in the rest of the world.
Another reason may be the similarity of economic or other shocks in the investing and
host country (Acharya et al., 2007). For example, the recent global fnancial crisis has
affected many countries at the same time in a very similar fashion. A third reason for
complementarity may arise fromthe strategic behavior of corporations facing options to
invest directly or in portfolio (Pfeffer, 2008). There are some other papers that have
documented a rising level of FDI infowin many countries along with falling level of FPI.
(Aguiar and Gopinath, 2005; Acharya et al. 2007). While this might be reasonable to
think of an inverse relationship between FPI and FDI infow, but the focus of this paper
is capital outfow.
Goldstein and Razin (2006) develop a theoretical model incorporating both FPI and
FPI, and being able to predict several stylized facts of the cross-border equity fows.
Their model predicts that FDI infow is more volatile than the FPI infow. Also, FPI can
quickly response to changes in market environment, whereas FDI cannot do so because
of the complications involved in liquidation process. As result, the model predicts that if
investors anticipate possibility of liquidity crisis, they would prefer FPI to FDI. Another
prediction of their model is that as the legal environment and investors’ protection
improves in a nation, the FDI infowwill rise compared to FPI. The focus of this paper is
different fromtheir paper’s central idea. In particular, the current paper revolves around
the idea of FPI and FDI outfow, rather than infow. Capital infow and outfow can be
affected considerably differently, as documented by the contemporary literature.
3.2 The hypotheses
The following two hypotheses summarize the arguments which we empirically
investigate in the subsequent sections:
H1. The FPI outfowis a signifcant determinant of the FDI outfowfroma country.
Similarly, the FDI outfow is an important determinant of the FPI outfow from
the same country.
International portfolio and direct investors receive relevant information on certain
cross-border market from each other in that particular market. Therefore, FPI outfow
will be positively infuenced by FDI outfow from the same country. Similarly, FDI
outfow would also be directly affected by the level of FPI from the same country. In
brief, the relationship between these two types of foreign investments is that of strategic
complementarity:
H2. The predictive power of FPI outfow in explaining FDI outfow is greater than
that of FDI outfow explaining the FPI outfow.
Given the nature of liquidity of portfolio investment and less liquidity of direct
investment, it is expected that FPI investors can respond to the changes in the market
environment quicker than the FDI investors. This enables the portfolio investments to
mobilize greater amount of information than the direct investment for the prospective
investors. This observation leads to the second hypothesis of this paper. Together, these
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two hypotheses provide a basis for understanding the empirical relationship between
FPI and FDI outfows.
4. The empirical specifcation
4.1 The baseline model
We begin with a baseline model that outlines the relationship between FPI and FDI.
Consider the following set of simultaneous equations:
FPI
it
? ?
0P
? ?
1P
FDI
it
? ?
2P
FO
it?1
? u
it
(1)
FDI
it
? ?
0D
? ?
1D
FDI
it
? ?
2D
TO
it?1
? v
it
, (2)
where FPI
it
and FDI
it
are expressed as percentage of the gross domestic product (GDP),
FO
it?1
is fnancial openness for which the Chinn–Ito index (KAOPEN)[3] is used (Chinn
and Ito, 2006, 2008), TO
it?1
is trade openness, as measured by the total import and export
as percentage of GDP, u
it
and v
it
are the classical error terms. Subscripts i and t are
indexes for country and time and subscripts P and D with the coeffcients indicate
portfolio investment and direct investment, respectively.
Equations (1) and (2) indicate that both FPI and FDI are determined jointly, and there
may exist of reverse causality between these variables which are investments across
borders. In this baseline model, the predetermined variables are two openness
measures[4], one for FPI (namely, FO
it?1
) and the other is for FDI (namely, TO
it?1
).
According to the theoretical expectation, both of these openness measures are supposed
to have a positive impact on the dependent variables on both equations. As for the
relationship between FPI and FDI, the equations postulate a contemporaneous fow of
information between them. As both of these are investment outfows, one would expect
a positive relationship between them.
A particular weakness of the above model is that it does not take into account the
overall fnancial environment of the economy. Inclusion of economic determinants to the
baseline model would allowus to ensure robust results. The extended model we consider
in this paper is:
FPI
it
? ?
0P
? ?
1P
FDI
it
? ?
2P
FO
it?1
? ?
3P
Return
it
? ?
4P
RER
it
? ?
5P
Inf
it
? u
it
(3)
FDI
it
? ?
0D
? ?
1D
FPI
it
? ?
2P
TO
it?1
? ?
3D
GDPGr
it
? ?
4P
RER
it
? ?
5P
Inf
it
? v
it
(4)
where, FPI, FDI, FO, TO, Return and GDPGr are as defned before. Two other variables
that are RER, the real exchange rate with reference to the US dollar, and Inf, the infation
rate. These variables are common to both equations, as they are assumed to be affecting
both types of investment outfows.
As for the H1 discussed in the previous section, one would expect ?
1P
?0 and ?
1D
?0
in all specifcations. The acceptance of the null hypothesis would indicate the existence
of strategic complementarity between FPI and FDI outfows. In terms of the regression
coeffcients, the H2 implies that the magnitude of FPI in the FDI equation will be larger
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than the size of the coeffcient on FDI in the FPI equation. In other words, we would
expect that ?
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??
1P
.
5. Data and estimation methodology
5.1 Data description
FPI data are collected from the Coordinated Portfolio Investment Survey (CPIS)[5] of
International Monetary Fund (IMF). The survey collects information on cross-border
portfolio investment directly from the reporting country. We begin with all countries in
the survey and then exclude those not having data. This leaves us with 45 countries with
annual data from 2001 to 2009, for which we do not have any missing data. Although
researchers employed the CPIS data with many different objectives, few papers have
used the full set of data for the period between 2001 and 2009. For example, Lane and
Milesi-Ferretti (2008) and Aviat and Coeurdacier (2007) used for 2001 only; Mishra
(2007) for 1997, 2001 and 2002; and Lee et al. (2012) from 2001 to 2007.
Table I presents the list of the countries. The countries of our sample are divided into
three market categories following the Morgan Stanley Capital International (MSCI)
market classifcation. They consist of 24 developed markets, 15 emerging markets and
six frontier markets. FDI data are fromthe Global Development Finance database of the
World Bank. Both FPI and FDI are expressed as percentage of current value of GDP
which is from the World Development Indicators (WDI) of the World Bank.
For fnancial openness (FO) the Chinn–Ito index (KAOPEN) (Chinn and Ito, 2006,
2008) is collected from the Web site of its authors[6]. The trade openness (TO) is
calculated as the total value of imports and exports as percentage of GDP and both
current dollar values of imports and exports are collected from the WDI of the World
Bank. The variable Return is the yearly return (log difference) on the MSCI equity index
for each country of our sample and the variable GDPGr is the growth rate in the GDP.
The real exchange rate (RER) is obtained from the International Financial Statistic and
the infation rate (Inf) is from World Economic Outlook, both maintained by the IMF.
Table II provides description all variables as well as their sources.
Table III presents the descriptive statistics of the variables that appear inall three models
considered. The correlations among these variables are presented in Table IV. As can be
Table I.
List of countries in
the sample
Developed markets (24) Emerging markets (15) Frontier markets (6)
Australia Italy Brazil Malaysia Argentina
Austria Japan Chile Philippines Estonia
Belgium Netherlands Colombia Poland Kazakhstan
Canada New Zealand Czech Republic Russia Lebanon
Denmark Norway Egypt South Africa Romania
Finland Portugal Hungary Thailand Ukraine
France Singapore Indonesia Turkey
Germany Spain South Korea
Greece Sweden
Hong Kong Switzerland
Ireland United Kingdom
Israel United States
Note: The countries of the sample are categorized following the MSCI market classifcation
JFEP
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Table II.
Variable description
Variable Description Source
FPI FPI outfow (as % of GDP) The CPIS, IMF
FDI FDI outfow (as % of GDP) Global Development Finance, The World
Bank
FO Financial openness The Chinn–Ito index (KAOPEN) (Chinn and
Ito, 2006; 2008)
TO Trade openness: imports and exports
(as % of GDP)
WDI, The World Bank
Return Local equity market return Morgan Stanley Capital International (MSCI)
GDPGr GDP growth WDI, The World Bank
RER Real exchange rate International Financial Statistic (IFS)
Inf Infation rate World Economic Outlook, IMF
Market Dummy variable 0 ?Developed; 1 ?Emerging; 2 ?Frontier
Table III.
Descriptive statistics
Variable Mean Median SD Minimum Maximum
FPI 0.245 0.104 0.393 0.000 2.577
FO 1.482 2.478 1.346 ?1.844 2.478
Return 0.073 0.182 0.408 ?1.825 0.934
FDI 3.528 1.837 5.825 ?4.876 49.079
TO 0.945 0.729 0.740 0.205 4.459
GDPGr 2.997 3.070 3.664 ?14.800 13.500
RER 1.976 1.280 2.489 ?0.707 9.474
Inf 1.363 ?0.206 5.380 ?5.926 51.409
Notes: Descriptive statistics of the sample are presented in this table. For description on the variables,
see table II above; FPI and FDI are FPI outfow and FDI outfow measured in US dollar and expressed
as percentage of current GDP; TO is total current value of imports and exports as percentage of GDP;
the only variable that is converted in to natural logarithm is real exchange rates (RER)
Table IV.
Pearson’s correlation
coeffcients among
variables
Variable FPI FO Return FDI TO GDPGr RER Inf
FPI 1.000
FO 0.386* 1.000
Return ?0.018 ?0.095 1.000
FDI 0.510* 0.341* ?0.122* 1.000
TO 0.491* 0.176* ?0.046 0.502* 1.000
GDPGr ?0.108* ?0.274* 0.132* ?0.065 0.116* 1.000
RER ?0.138* ?0.113* 0.089 ?0.100* ?0.113* 0.110* 1.000
Inf ?0.246* ?0.510* ?0.035 ?0.189* ?0.164* 0.117* 0.134* 1.000
Notes: This table presents pairwise correlation coeffcients among all variables at hand over the full
sample. The 5% critical value (two-tailed) for the null that the population correlation coeffcient is zero
is 0.098 for total number of observation equaling 405; *indicates rejection of the null of no correlation
at 5% level of signifcance
197
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seen, most of the correlation coeffcients are statistically signifcant. The correlation
coeffcient between FPI and FDI is positive and signifcant. A coeffcient as high in
magnitude, as 0.510 encourages us to inquire estimating the regression models to measure
their causal relationship. Individually, FPI appears to be signifcantly correlated with all
variables except for Return. Similarly, FDI is also signifcantly correlated with all other
variables except for GDPGr which is being used as a proxy for return in the real economy.
5.2 Estimation methodology
The primary focus of this paper is to see how FDI outfow affects the FPI outfow in the
presence of some other determinants as well as in isolation from any other variables.
The empirical specifcations outlined in the previous section, however, indicate the
presence of possible reverse causality between FPI and FPI outfows from a country to
international destinations. This creates the endogeneity problemwhere the explanatory
variables may be correlated with the error terms violating assumptions of classical
linear regressions. A well-known result in the econometrics literature is that ordinary
least squares (OLS) estimates are biased (also known as the simultaneity bias) and
inconsistent in the presence of endogeneity problem. Asolution to the simultaneity bias
is to use the instrumental variable approach. In particular, in this paper, the TSLS
method is used to obtain consistent estimates.
The order and rank conditions must be satisfed to estimate a systemof simultaneous
equations As for the order condition in the systems, let us consider the baseline model,
equations (1) and (2): the system of two equations has two endogenous variables,
namely, FPI
it
and FDI
it
and also two predetermined variables, FO
it?1
and TO
it?1
, and the
order condition for the systemis met. Also, this condition is met for the extended and the
full model which have the same two endogenous variables, but there are more than two
excluded exogenous variables. The rank condition for equation (1) is satisfed, as we
have trade openness, TO, in equation (2) and not included in equation (1), which is
assumed to be an important determinant of FDI outfow. Similar arguments can be made
in relation to equation (2), which also satisfes the rank condition. Given that we have
both order and rank conditions satisfed for our system at hand, each equation is
identifed and can be consistently estimated as a single equation using instrumental
variable method using the TSLSs.
A number of diagnostic tests are reported with the estimation results. The Hausman
(1978) test assumes consistency of OLS estimates as against TSLS estimates and is
distributed as chi-squared with degrees of freedom equaling the number of endogenous
variable. This test is usedtochoose betweenthe OLSandTSLSresults. The endogeneitytest
corresponds to test of endogeneitypresentedbyWooldridge (2002), whichassumes absence
of endogeneity under the null hypothesis (not reported here). The Sargan test concerns with
the null hypothesis that all instruments used in the estimation are valid. This test is used
whenthe model is over-identifedwithmore instruments beingavailable thanthe number of
endogenous variables. The weak instrument test by Stock and Yogo (2005) tests the
assumption that instruments used are weak.
6. Empirical results
Table Vpresents the estimation results of the extended model given in equations (3) and
(4)[7]. Results from both OLS and TLS are presented in Table V. The results for the FPI
equation show that FDI
it
is signifcant in both OLS and TSLS. As for other variables
JFEP
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Estimation results of
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Portfolio
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l
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a
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d
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2
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5
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J
a
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2
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1
6
(
P
T
)
included in the model, FO
it?1
is signifcant in OLS only, Return
it
and RER
it
are signifcant
in both, while Inf
it
is insignifcant in both estimation. The estimate of FDI
it
has the
positive sign as expected a priori, which indicates positive contemporaneous
relationship between FPI and FDI outfows. Similarly, in the results for the FDI
equation, FPI
it
is signifcant both in the OLS and TLS estimation and TO
it?1
is
signifcant in the OLS, but not in the TSLS estimation.
The results support a theoretical model developed by Goldstein and Razin (2006).
This fnding supports the notion of strategic complementarity between these outfows.
In terms of the magnitude of the estimated coeffcient on FDI
it
, (0.067 in TSLS) is higher
than that on FPI
it
, (13.357 in TSLS). The coeffcients are positive and signifcant in both
OLS and TSLS regressions. The results confrm the strategic complementarity
hypothesis. Given that the coeffcient on FPI
it
in the FDI equation is much larger than
that on the FDI
it
in the FPI equation, we also support for the H2.
Panel B in Table V reports the diagnostics tests for a comparison between the OLS
and TSLS regressions. The Hausman test statistic rejects the null hypothesis that OLS
estimates are consistent in favor of the TSLS estimates for both equations (3) and (4).
The Sargan overidentifcation test is rejected for the FDI equation, but not for the FPI
equation. The third diagnostic test statistic reported is the weak instrument test which
rejects the null hypothesis that the instruments are weak or mostly invalid for both
equations. As mentioned in the estimation methodology part, we adopt a TSLS
estimation method to avoid the simultaneity bias that would arise fromthe endogenous
relationship between FPI
it
and FDI
it
. This possibility is formally tested following the
methodology outlined by Wooldridge (2002). The test results show that these variables
are indeed endogenously related to each other[8]. Because the endogeneity tests
indicate presence of endogeneity in the regression models, the TSLS method is
proper to use instead of the OLS. In this respect, we frst estimated the FPI
it
and FDI
it
using the exogenous and predetermined variables on the right-hand sides. The frst
stage regression results for the FPI and FDI equations are reported in Panel C of the
same table. As can be seen from the reported results which indicate that fnancial and
trade openness variables (in their lagged terms) are signifcant determinant for both
FPI
it
and FDI
it
. Anumber of other included variables are also signifcant. This fnding is
not surprised, given the weak instrument test results reported in Panel B of Table V.
In the results reported in Table Vfor equations (3) and (4) and the unreported results
based on equations (1) and (2), we obtain evidences in favor of the both hypotheses
outlined earlier. In particular, the FDI outfow has empirically signifcant predictive
power in explaining the FPI outfow. Similarly, the FPI outfow also has signifcant
explanatory power for the observed level of FDI outfow. Second, the FPI has greater
explanatory power for FDI outfow than the FDI for the FPI outfow.
Given the preliminary results reported in Table V, we check for the robustness of our
fndings on the complementary relationship between FPI and FDI. First, we replace the
returns in equation (3) with the risk-adjusted returns. The risk-adjusted returns are
calculated as the difference between raw returns and the world market returns proxied
by the MSCI world market returns[9]. Our fndings are presented in Table VI. The
results showthat the newly replaced risk-adjusted returns are still signifcant, while the
magnitude and signifcance of other variables are not much affected by this
replacement. Table VI also presents the diagnostic test results and the frst stage
regression results for the TSLS method.
JFEP
7,3
200
D
o
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b
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P
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1
:
5
2
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table VI.
Estimation results of
the full model with
risk-adjusted return
T
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201
Portfolio
investment
outfow
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
5
2
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table VII.
Estimation results of
the full model with
market dummy
T
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e
F
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JFEP
7,3
202
D
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6
(
P
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)
This paper includes data from the developed, emerging markets and frontier markets.
We are interested in fnding out if the results found earlier are different for different
types of markets. To see this, we modify equations (3) and (4) with a dummy variable for
the type of market, with 0 for developed, 1 for emerging and 2 for frontier markets. The
results, reported in Table VII, indicate that the market dummy is insignifcant in the FDI
equation. In the FPI equation, the market dummy is negative and signifcant in both
OLS and TSLS estimation. Magnitude and signifcance of other coeffcients remain
similar to what have been reported earlier. A negative value of the estimated dummy
coeffcient would lower the value of the intercept for emerging market and frontier
markets.
7. Concluding remarks
This paper aims at understanding the empirical relationship between FPI and FDI
outfows. In the absence of any general model incorporating both types of outfows from
a nation to the rest of the world, it outlines three different specifcations to examine the
direction and magnitude of such interrelationship. Following the literature, albeit
scanty, two hypotheses are developed. The frst one postulates a positive and
complementary relationship between FPI and FDI outfows. The second hypothesis
suggests greater impact of FPI on FDI outfowthan the infuence of FDI on FPI outfow.
To take the potential presence of simultaneity bias in the relationship between
FPI and FDI, the econometric specifcations followed simultaneous equations
approach. The parameters are then estimated using TSLS method. Anumber of post
estimation diagnostic tests are conducted to verify the consistency of the estimates
obtained. Overall, the estimation results support for both hypotheses. As for the FPI
equation, the sign of the coeffcient on FDI is positive in all three specifcations. On
the other hand, the FDI equation in all three specifcations yields a positive sign on the
coeffcient of FPI. We observe that the impact of FPI on FDI is greater than the impact
of FDI on FPI across the national border at an aggregate level.
Future extension of this paper could be directed to address some of the
shortcomings of the current paper. First, a future research requires the theoretical
foundation for empirical investigation. The current literature is scanty in terms of
economics models incorporating both FPI and FDI, and more so for their outfow
rather than infow. Second, although this paper looks at the whole data set as a
sample of uniformand single universe, the fact that the countries in the sample come
from different types of markets necessitates verifying results across different types
of markets. Third, different exogenous variables and predetermined endogenous
variables could be used as instruments to obtain better and more robust results. Any
future extension of this paper should take these issues in to account and reach to a
conclusion regarding the interrelationship between foreign portfolio and direct
investment outfow.
Notes
1. Refer to Prasad et al. (2003) and Stulz (2005) for global fnancial liberalization and its impact
on international fnance and investment.
2. For a review, see Coakley et al. (2004).
3. For a recent survey of different measures of fnancial openness, see, Clark et al. (2012).
203
Portfolio
investment
outfow
D
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b
y
P
O
N
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6
(
P
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)
4. The openness measures are contemporaneously correlated with the FPI and FDI. Therefore,
to avoid simultaneity bias, we add lagged terms of the openness variables which are
predetermined, and so, not endogenous to the system. We thank the reviewer for bringing this
to our attention.
5. www.imf.org/external/np/sta/pi/cpis.htm
6. http://web.pdx.edu/?ito/Chinn-Ito_website.htm
7. The baseline model given in equations (1) and (2) are also estimated. The results are not
reported here for the sake of brevity and available upon request.
8. The results are available from the authors upon request.
9. The MSCI ACWI Investable Market Index is used to calculate the world market returns that
consist of large, mid and small cap assets across 24 Developed Markets and 21 Emerging
Markets countries.
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Corresponding author
Abdullah Noman can be contacted at: [email protected]
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