Foreign portfolio investment and economic growth in Malaysia.
Duasa, Jarita ; Kassim, Salina H.
This study examines the relationship between foreign portfolio
investment (FPI) and Malaysia's economic performance. In
particular, the study analyses the relationship between FPI and real
gross domestic product (GDP) using the widely adopted Granger causality
test and the more recent Toda and Yamamoto's (1995) non-causality
test to establish the direction of causation between the two variables.
Similar method is also applied on the relationship between volatility of
FPI and real GDP. Additionally, the study uses an innovation accounting
by simulating variance decompositions and impulse response functions for
further inferences. Using quarterly data coveting the period from 1991
to 2006, the study finds evidence that economic growth causes changes in
the FPI and its volatility and not vice versa.. The findings suggest
that economic performance is the major pull factor in attracting FPI
into the country. Thus, it must be ensured that the Malaysian economy
remains on a healthy and sustainable growth path so as to maintain
investor confidence in the economy.
JEL classification: GI5, C32, C12
Keywords: Foreign Portfolio Investment, Economic Growth, Granger
Causality, Toda-Yamamoto Non-causality, Variance Decomposition
1. INTRODUCTION
Amid several incidences of economic and financial crises in the
1990s and 2000s, there has been renewed research interest in analysing
the impact of foreign portfolio investment (FPI) on the economic
well-being of a host country. While it is widely accepted that
investment flow has its own benefits, lessons learned from the financial
crises highlighted that short-term FPI could have adverse effects on the
host economy. It is therefore critical to analyse the extent to which a
country could benefit from the inflow of FPI.
In general, the merits of capital market integration through
liberalisation of investment regulations are well-documented in the
literature. FPI contributes positively in the development of an
efficient domestic capital market and brings several benefits to the
host country. Increased FPI leads to greater liquidity in the capital
market, resulting in a deeper and broader market [Levine and Zervos
(1996)]. The spill-over effects of positive competitive pressure to
attract foreign investment would necessitate higher industrial standards
and regulations through better corporate governance and greater business
transparency, resulting in stronger investor protection and thus
enhanced investor confidence [Feldman and Kumar (1995); Shinn (2000)].
Increased liquidity in the capital market also means better access to
financing at lower cost of capital which is crucial to support economic
activity [La Porta, et al. (1998); Bekaert and Harvey (2003)]. In this
regard, the inflow of FPI into the stock market helps to alleviate
financial constraints of firms [Laeven (2003); Knill (2004); Beck,
Demirguc-Kunt, and Maksimovic (2005)]. Studies relating to FPI and the
domestic stock markets show favourable contribution of FPI in supporting
the domestic stock market [see for example, Patro and Wald (2005); Kim
and Singal (2000)]. The multiplier effect further propagates the impact
of growth in the stock market through the wealth effect. In this sense,
capital flows act as catalyst to economic growth and contribute towards
increased wealth creation. Ultimately, better access to financing
provided by the free flow of portfolio investments contributes to
efficient allocation of capital [Wurgler (2000); Love (2003); Rajan and
Zingales (1998)].
Despite its numerous virtues, FPI could have adverse effects on the
host economy. The potentially damaging aspects of FPI are rooted in its
nature which is short-term and thus also volatile. In particular, FPI
volatility has often been quoted as the major reason behind financial
market distress, leading to financial crisis. Lessons learned from the
Asian financial crisis of 1997-1998 show that large and abrupt reversal
of portfolio investment often causes panic in the financial market,
since it is taken as a manifestation of impending financial crisis
[Knill (2004); Sula and Willet (2006)]. More importantly, as highlighted
by Henry (2003) and Demirguc-Kunt and Detragiache (1999), based on the
experience of many countries which experienced financial crisis, the
volatility of portfolio investment further exacerbates the impact of a
financial crisis. FPI instability complicates the implementation of
macroeconomic stabilisation policies by the policy-makers. Uncertainties
in the flow of FPI result in unpredictable behaviour of money supply,
exchange rate level and stock market volatility [Patro and Wald (2005)].
In particular, sustained periods of excessive capital inflows due to
high capital mobility could result in the formation of asset price
bubbles, leading to inflationary pressure, while sudden withdrawals in
portfolio investment accompanied by major correction in asset prices can
pose serious risk to the economy [Bank Negara Malaysia (2006)].
In view of its benefits and costs, a number of studies support the
view that the benefits of FPI are long-term with some adverse effects in
the initial stage of the process. The long-term gains of FPI outweigh
its short-term ill effects and bring real benefits to the growth and
development of the domestic financial markets and the economy in general
[Kaminsky and Schmukler (2001)].
This study seeks to analyse FPI in the Malaysian case and provides
recent empirical evidence on whether it is beneficial to the Malaysian
economy of otherwise. Using a battery of tests, the study hopes to
provide conclusive empirical evidence on the relationship between FPI
inflow and economic growth. It is hoped that the findings of the study
would contribute towards enriching the relevant literature on the
relationship between foreign portfolio investment and economic growth,
particularly in the case of developing countries.
The rest of the paper is organised as follows: the next section
provides some background information on FPI based on the Malaysian
experience. In particular, this section highlights Malaysia's
experience in handling FPI during the financial crisis of 1997-1998.
Section 3 presents the empirical methods. Section 4 highlights the
empirical findings including the data preliminaries and the results
based on the causality tests. In this section, further inferences are
also drawn based on the impulse response functions and the variance
decomposition analysis. Finally, Section 5 provides some concluding
remarks.
2. FOREIGN PORTFOLIO INVESTMENT IN MALAYSIA
FPI in Malaysia has been substantial. During the period under
review, total portfolio investment, comprising both inflow and outflow,
recorded a minimum of RM8.1 billion (of 22.2 percent of nominal gross
domestic product GDP) in the first quarter of 1991 and reached a maximum
of RM132.8 billion (or 297.3 percent of GDP) in the fourth quarter of
1993. As shown in Figure 1, FPI has been very volatile particularly in
pre-1997 period. However, the flow of FPI has become less volatile in
the post-1997-1998 Asian crisis period. In terms of share in total GDP,
FPI accounted for an average of around 200 percent of total GDP in the
period 1991-1997 and declined gradually before stabilising at around 50
to 60 percent of GDP during 2004-2006. A clear correlation between the
total FPI and nominal GDP thus could only be seen in the post-crisis
period.
Analysis of the decomposition of total FPI, shows a clear
correlation between portfolio inflow and outflow in the Malaysian case
as shown by Figure 2. During the period under review, portfolio
investment inflow moved closely in tandem with portfolio investment
outflow. Yet, three interesting observations can be made. First, during
the record high level of portfolio investment during 1993 to 1994, the
total FPI inflow exceeded the FPI outflow. This reflects the positive
investor sentiment due to the economic boom experienced by Malaysia
during the period. The second observation, however, reflects the adverse
effects of massive portfolio outflow on the Malaysian economy as there
was a large gap between inflow and outflow in the second and fourth
quarter of 1997. Specifically, the net portfolio investment reached a
record level of minus RM16 billion in the fourth quarter of 1997. In
contrast to the massive inflow due to increased investor confidence in
the 1993-1994 period, this massive outflow was due to dented investor
confidence following the crisis starting in mid-1997. In line with the
massive outflow, the growth of the Malaysian economy turned into a
negative real GDP growth of 7.5 percent in 1998 from a positive growth
of 7.5 percent in 1997.
More recent trends of FPI inflow and outflow have been encouraging.
In the post-2003 period, except for the fourth quarter of 2005, inflow
of FPI has been consistently greater than its outflow. This reflects the
return of investor confidence in the Malaysian economy. The encouraging
trend in the FPI flow reflects the pro-active government policy to
instil investor confidence in the Malaysian financial markets. The
Malaysian central bank--Bank Negara Malaysia (BNM)--fully acknowledges
the merits of FPI, while at the same time keeping an eye on its
drawbacks. In particular, BNM closely monitors any potential risks that
might adversely affect investor confidence in the financial market. The
ability to detect such risks at an early stage helps BNM to act swiftly
by undertaking appropriate and pre-emptive policy measures to address
and mitigate their implications on the Malaysian economy [Bank Negara
Malaysia (2006)].
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
The Malaysian government's decision to impose capital outflow
controls on September 1998, after the failure of adopting the IMF
proposals for one-year, has sparked a revival of interest in the use of
capital controls as there is positive evidence of their implementation.
Most literature, though not all, specifically registers evidence of the
positive consequences of Malaysian capital outflow controls even if the
actual efficiency of the measures is difficult to assess. Doraisami
(2004), Athukorala (2001) and Cooper (1999) found that the Malaysian
measures did lower the interest rates, which enabled monetary expansion.
The controls also reduce the volatility of interest rates [Edison and
Reinhart (2000)], contain capital outflow by eliminating the offshore
market [Ariyoshi, et al. (2000); Athukorala (2001)], reduce stock market
volatility [Doraisami (2004); Kaplan and Rodrik (2000); Cooper (1999)],
insulate the domestic markets from international markets [Kaminsky and
Schmukler (2001)], and bring faster economic recovery, smaller decline
in employment and real wages and increase in foreign exchange reserves
[Rodrik (1998); Cooper (1999)].
On the other hand, such controls are found to discourage capital
inflows more than limiting capital outflows [Fane (2000)] and, in
particular, they contribute to a weak flow of FDI to the country
[Hartwell (2001)]. Furthermore, the controls are criticised on grounds
of efficiency as they tend to safeguard government cronyism [Johnson and
Mitton (2001)], suppress market discipline and reduce efficiency of
stock market prices [Li, et al. (2004)]. Mixed results, however, were
found by Tamirisa (2001) who shows that regulation of bank operations
and foreign exchange rate transactions plus tightening controls on
equity market reduce portfolio (short-term) investment, but regulation
of international transaction in Ringgit increases portfolio investment.
At this point, it should be made clear that the different economic
and fundamental background of Malaysia from other crisis-hit countries
in the region required it to take a different path. Instead of going on
with the IMF policy prescription, on September 1998, the authorities
imposed sweeping controls on capital-account transactions, adopted fixed
exchange rate, cut interest rates, and embarked on a policy of
reflation. The steps were taken in the belief that Malaysia was facing a
different type of crisis compared to other countries in the region. As
substantial capital controls had already been imposed, with reserves at
a lower level, the measures aimed specifically at containing Ringgit
speculation and the outflow of capital by eliminating the offshore
Ringgit market and at stabilising short-term capital flows. The measures
also sought to increase monetary independence and insulate the economy
from prospects of further deterioration in the world financial
environment. Furthermore, accommodative monetary and fiscal policies
were implemented to support economic activity. The financial and
corporate sector reforms, which had commenced in early 1998, were
accelerated to deal with the weak financial institutions and strengthen
the banking system. In the 1997 Asian financial crisis, the emergency
controls on outflows might have been the least bad choice for Malaysia
whose currency was under severe attack from domestic and foreign
speculators. Krugman (1998), for example, has argued that perhaps
capital controls are sometimes the best alternative to the remedy the
IMF has often prescribed in the past on a country that puts tremendous
pressure on its economy and banking system through sharp rises in
interest rates.
3. THE EMPIRICAL METHODS
In terms of methodology, this paper implements the widely used
Granger causality test and the more recent Toda and Yamamoto's
(1995) non-causality test to establish the direction of causation
between the two variables.
Generally, the Granger causality models are as following:
[DELTA]GD[P.sub.t]= [[alpha].sub.1] + [n.summation over(i=1)]
[[beta].sub.i] [DELTA]GD[P.sub.i-1]+ [k.summation
over(i=1)][[theta].sub.i] [DELTA]FP[I.sub.t-i] + Dcrisis +
[[upsilon].sub.t] ... ... (1)
[DELTA]FP[I.sub.t]= [[alpha].sub.2] + [k.summation over(i=1)]
[[theta].sub.i] [DELTA]GD[P.sub.i-1]+ [k.summation
over(i=1)][[phi].sub.i][DELTA]FP[I.sub.t-i] + Dcrisis +
[[upsilon].sub.t] ... ... (2)
where GDP and FPI are real gross domestic product growth and
Foreign Portfolio Investment inflow, respectively, Dcrisis is dummy
variable for the 1997 financial crisis (0 = before 1997 and i=1997 and
after 1997), [DELTA] is first-difference operator and k is the optimal
lag length. The focus of analysis is basically on FPI inflow as it is
perceived to be the one factor that contributes to the growth of the
economy as compared to FPI outflow which is highly volatile. The test
amounts to testing the significance of null hypotheses ([[theta].sub.i]
= 0 and [[theta].sub.i] = 0. To account for the effects of the 1997
Asian financial crisis on the relationship between FPI and GDP, we
include the crisis dummy into the model.
Besides the Granger causality test, we also employ the augmented
level VAR approach suggested by Toda and Yamamoto (1995) to determine
the causal nexus between the variables. Unlike the Granger test, the
Toda-Yamamoto (T&Y) approach to causality does not require a priori
knowledge of the variables' cointegration properties.
Econometrically, it circumvents the problem of pre-testing bias
associated with the Granger test. So long as the order of integration of
the process does not exceed the true lag length of the model, the
approach is applicable in the absence of cointegration and/or of the
stability and rank conditions [Toda and Yamamoto (1995)]. As for Toda
and Yamamoto's (1995) non-causality test, the following
specifications are estimated:
GD[P.sub.t]= [[alpha].sub.1] + [k+d max.summation over(i=1)]
[[beta].sub.i] GD[P.sub.i-i]+ [k+d-max.summation
over(i=1)][[phi].sup.*.sub.i]FP[I.sub.t-i] + Dcrisis + [[upsilon].sub.t]
... ... (3)
FP[I.sub.t]= [[alpha].sub.2] + [k+d max.summation over(i=1)]
[theta].sup.*.sub.i]GD[P.sub.i-i]+ [k+d-max.summation
over(i=1)][[phi].sub.i]FP[I.sub.t-i] + Dcrisis + [[upsilon].sub.t] ...
... (4)
where d-max is the maximal order of integration suspected in the
system. The null hypotheses that ([[theta].sub.i] : 0 and
[[theta].sub.i] = 0 are tested based on a modified Wald test statistic
for parameter restrictions, which is shown to be asymptomatically
chi-square distributed. The null hypothesis set for Equation (3) is
[[theta].sup.*.sub.i] =0[[for all].sub.i] [less than or equal to] k and
for Equation (4) is [[theta].sup.*.sub.i] =0[[for all].sub.i] [less than
or "Granger-causes" GDP if its null hypothesis is rejected and
from Equation (4), GDP "Granger-causes" FPI if its null
hypothesis is rejected. Unidirectional causality will occur between two
variables if either null hypothesis of Equations (3) or (4) is rejected.
Bidirectional causality existed if both null hypotheses are rejected and
no causality existed if neither null hypothesis of Equation (3) nor
Equation (4) is rejected.
Secondly, similar methods of Granger causality and Toda and
Yamamoto's noncausality tests are applied on variables of growth
and volatility of FPI to observe the relationship between the two
variables as it is hypothesised that volatility/instability of FPI might
impact the economic growth of the country. The variable of FPI
volatility is developed by inspecting first the possibility of the
existence of Autoregressive Conditional Heteroskedasticity (ARCH) effect
on residuals of the Autoregressive Moving Average (ARMA) model of FPI.
If there is ARCH effect on the residuals, the volatility of FPI is
developed from residuals of the Generalised Autoregressive Conditional
Heteroskedasticity (GARCH) model. Otherwise, the volatility of FPI is
developed from residuals of the ARMA model itself.
Furthermore, we adopt an innovation accounting by simulating
variance decompositions (VDC) and impulse response functions (IRF) for
further inferences. VDC and IRF serve as tools for evaluating the
dynamic interactions and strength of causal relations among variables in
the system. The VDC indicate the percentages of a variable's
forecast error variance attributable to its own innovations and
innovations in other variables. Thus, from the VDC, we can measure the
relative importance of FPI fluctuation in accounting for the variations
in real GDP. Moreover, the IRF trace the directional responses of a
variable to a one standard deviation shock of another variable. This
means that we can observe the direction, magnitude and persistence of
economic growth to variation in the FPI, not vice versa.
4. EMPIRICAL FINDINGS
4.1. Data Preliminary
Both the series examined in this study, namely real GDP and Foreign
Portfolio Investment inflow, are gathered from Bank Negara
Malaysia's Quarterly Bulletin and International Monetary
Fund's IMF Financial Statistics of various issues. The sample range
is from 1991 to 2006 of quarterly data. The raw data obtained for both
variables are in RM billion and the base year for real GDP is 1987. All
variables are expressed in natural logarithm.
4.2. Results
As a preliminary step, we first subject each variable to Augmented
Dickey Fuller (ADF) and Phillip-Perron (P-P) unit root tests. The
results of the tests are displayed on Table 1. The results generally
suggest that both real GDP (ln GDP) and Foreign Portfolio Investment (In
FPI) are integrated of order one as the null hypothesis that the series
are not stationary is accepted at level but rejected at first
difference. In other words, the variables are stationary at first
difference or I(1).
The next step is to select the optimum lag length (k) which will be
used in the Granger causality model (Equation 1) for real GDP and FPI.
By saving residuals of VAR model (repeatedly starting from VAR with lag
2) and checking the correlogram of its residuals (to avoid the problem
of autocorrelation), the optimum lag length is selected. Using this
method, the results suggest that lag 5 is the optimum lag for both real
GDP and FPI.
The Granger causality test is then conducted on the two variables
using the optimum lag 5, without and with crisis dummy included in the
model. The significance of using crisis dummy in the model is that it
might smooth out the short-term effect of volatility and reinforce the
long-term effects of GDP. Both tests, with and without crisis dummy,
provide almost similar inferences. (Results with crisis dummy are shown
in Table 2). The results indicate the existence of causality running
from GDP to FPI but not from FPI to GDP. This implies that the change in
the Malaysian economic growth causes the change in FPI. The results are
highly supported by Toda and Yamamoto non-causality test using lag 6
(i.e. k +d-max). As displayed in Table 2, Toda and Yamamoto's
nullhypothesis of GDP not causing FPI is rejected at the 1 percent
significance level which also implies that the causality of two
variables is running from GDP to FPI instead of FPI to GDP.
In considering that the volatility of FPI could probably impact
economic performance of the country, the variable of volatility of FPI
is developed from residuals of suitable ARIMA of GARCH model for FPI. By
inspecting correlogram of change in log FPI (d(ln FPI)), the
ARIMA(2,1,2) model is selected and it is also found that this model does
not have problem of ARCH effect in residuals. Thus, we treat the model
as a suitable model of FPI rather than using the GARCH model. By saving
the residuals of ARIMA(2,1,2) model of FPI, the volatility FPI is
developed and it is used for further test of unit root and causality
test. The results of unit root ADF and P-P tests are shown in the last
row of Table 1. As expected, the variable is stationary at level or
I(0).
Since the order of integration of FPI volatility and real GDP is
not similar, i.e. volatility of FPI is I(0) and real GDP is I(1), the
only suitable causality test to both variables is Toda and Yamamoto
non-causality test as this approach does not impose restriction on order
of integration and cointegration. Results of Toda and Yamamoto
noncausality test of both variables are displayed in Table 3. Obviously,
null hypothesis of FPI volatility not causing GDP is accepted and the
null hypothesis that GDP causes volatility of FPI is rejected at only 10
percent level of significance. The results firmly indicate that neither
economic performance of the country is affected by the volatility of FPI
and nor economic growth affects volatility of FPI.
In general, the study found that FPI of the country is neither a
curse nor a blessing for the economy since we found very weak evidence
that FPI flows or its volatility cause economic growth. Rather, the
study finds strong evidence that the economic performance is vital in
attracting FPI inflow as the causality is running from GDP to FPI
inflow.
For further inferences, we compute variance decompositions and
impulse response functions from estimated VAR. The results of impulse
response functions (IRF) and variance decomposition (VDC) of variables
GDP and FPI are displayed on Figure 3 and Table 4, respectively. From
Figure 3, the IRF shows that FPI does react significantly to real GDP
innovation for the first 2 quarters before it subsides to zero.
Obviously, the positive response of FPI to GDP in the first 2 quarters
implies that economic growth is important in attracting high flow of FPI
to the country. On the other hand, response of real GDP to FPI seems
insignificant.
[FIGURE 3 OMITTED]
As discussed earlier, the variance decomposition is an alternative
method to IRF for examining the effects of shocks on the dependent
variables. It determines how much of the forecast error variance for any
variable in a system is explained by innovations to each explanatory
variable, over a series of time horizons. Usually, it is the shocks that
explain most of the error variance, although they will also affect other
variables in the system. From Table 4, the VDC substantiate the
significant role played by real GDP in accounting for fluctuations in
Malaysian FPI. At one year horizon, the fraction of Malaysian FPI
forecast error variance attributable to variation in real GDP is only
about 12 percent. But then it further increases to almost 20 percent in
8 years (32 quarters). On the other hand, the percentage of real GDP
forecast variance explained by innovation in FPI is very small which is
less than 10 percent though at a longer time horizon. Thus, the VDC
results also highly support the importance of growth to FPI in Malaysia
rather than the other way around.
Further investigation is done using IRF and VDC on variables of
real GDP and volatility of FPI. Figure 4 and Table 5 displayed both
results, respectively. From Figure 4, it shows that volatility of FPI
also reacts significantly to real GDP innovation for the first 2
quarters before it subsides to zero. Positive response of volatility of
FPI to GDP in the first 2 quarters indicates the importance of economic
growth in affecting volatility of FPI in the country. However, similar
to previous results, the response of real GDP to volatility of FPI seems
insignificant. The output of investigation is further strengthened by
results from VDC. In Table 5, VDC confirms the significant role played
by real GDP in accounting for fluctuations in volatility of FPI. The
fraction of Malaysian volatility of FPI forecast error variance
attributable to variation in real GDP is increasing from almost 13
percent in first quarter to almost 19 percent in 32 quarters. But the
percentage of real GDP forecast variance explained by innovation in
volatility of FPI is very small with only around 11 percent at a longer
time horizon. Therefore, the VDC results highly support the importance
of growth not only to FPI but also the volatility of FPI. Again, the
impact of FPI volatility on growth is found to be insignificant.
[FIGURE 4 OMITTED]
5. POLICY IMPLICATIONS AND CONCLUSIONS
This study analyses the relationship between FPI inflow and
economic growth in the Malaysian case. In particular, it attempts to
determine the direction of causality between FPI inflow and economic
growth and explores empirical evidence as to whether FPI inflow or its
volatility has an impact on Malaysia's economic performance or
otherwise.
The study finds that economic growth causes the FPI inflow but not
its volatility. However, neither the FPI nor its volatility causes
economic growth. Thus, the findings of this study suggest that FPI or
its volatility is not a crucial factor in determining the economic
performance of Malaysia. Rather, the study finds that economic growth is
highly significant in determining the flows of FPI. Interestingly, the
1997 government policy of regulating FPI outflows does not appear to
have dampened the "causality" relationship between GDP and FPI
inflow. Theoretically, these inferences indicate that regulation of
outflows should be a disincentive for inflows; but if regulation
sustains GDP then the growth effect would outweigh the disincentive
effect. The results are consistent and robust based on the battery of
tests undertaken in this study.
It is an important caveat that the findings of this study are
confined by the empirical restrictions resulting from the nature of the
data. In particular, the selection of the optimum lag length of 5
quarters is necessitated by the statistical need to avoid the serial
correlation in the residuals, as mentioned in the methodology section.
However, it is important to note that such a relatively long lag of 15
months could result in the dominance of the GDP variable in causing the
FPI as suggested by the Granger causality test. In other words, the long
lag dictated by the optimum lag selection procedure might smooth out the
short-run effects of FPI on the GDP.
As such, it can be anticipated that when the lag is reduced to
shorter lags, the results could change in such a way that causality
might exist from FPI to GDP, a finding which is consistent with the
expectation that the effects of FPI volatility on GDP are likely to be
felt relatively quick, in a time span of shorter than 5 quarters.
However, the shorter lag used in the VAR model suffers from the problem
of serial correlation in residuals and the number of lags used is also
not supported by the lag length criteria such as LR (sequential modified
LR test) statistic, FPE (Final prediction error), AIC (Akaike
information criterion), SC (Schwarz information criterion) and HQ
(Hannan-Quinn information criterion). When all this taken into
consideration, it is difficult to capture the short-run effects of FPI
volatility on GDP.
The results of the study imply that economic performance is the
major pull factor in attracting FPI into the country. This was basically
due to the 1997 pro-active government policy which was successful in
mitigating the possible adverse effects in the post-1997 crisis period.
The evidence to show that either FPI inflow is a blessing or a curse is
rather very weak. Therefore, it is necessary to ensure that the
Malaysian economy remains on a healthy and sustainable growth path in
order to maintain investor confidence in the economy. Indeed, the
experience during the 1997 financial crisis has clearly shown that the
lower FPI inflow and the massive FPI outflow resulted from the
anticipation of weaker economic performance due to the crisis.
Regardless of the directions of causation, it is crucial for the
policy-makers to provide a conducive environment to attract FPI inflow
due to its numerous advantages for the economy.
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Jarita Duasa <jarita@iiu.edu.my> is Associate Professor,
Department of Economics, Kulliyyah of Economics and Management Sciences,
International Islamic University Malaysia, Kuala Lumpur, Malaysia.
Salina H. Kassim <ksalina@iiu.edu.my> is Associate Professor,
Department of Economics, Kulliyyah of Economics and Management Sciences,
International Islamic University Malaysia, Kuala Lumpur, Malaysia.
Table 1
Unit Root Test
ADF Test Statistic P-P Test Statistic
(with Trend and (with Trend and
Intercept) Intercept)
Level First Level First
Variable Difference Difference
L FPI (Foreign -2.46 -8.84 *** -2.50 -8.81 ***
Portfolio Investment
Inflow)
L GDP (Real GDP) -2.39 -3.66 ** -3.19 * -9.42 ***
Vol_L FPI -8.65 *** -9.38 ***
(Volatility of FPI)
Note: *** , ** and * denote significance at 1 percent,
5 percent and 10 percent level, respectively.
Table 2
Results for Causality Tests
Granger Causality Test, with Optimum Lag (k) = 5
Test Statistic
Null Hypothesis ([chi square]) p-value
FPI not Causes GDP 6.055 0.300
GDP not Causes FPI 15.573 0.008
Toda and Yamamoto Non-causality Test,
with k + d-max = 6
FPI not Causes GDP 3.552 0.615
GDP not Causes FPI 16.463 0.006
Table 3
Results Causality Test, GDP and Volatility FPI
Toda and Yamamoto Non-causality Test, with k + d-max = 6
Test Statistic
Null Hypothesis ([chi square]) p-value
Volatility FPI not 3.832 0.699
Causes GDP
GDP not Causes 11.279 0.080
Volatility FPI
Table 4
Variance Decompositions of GDP and FPI
Variance Decomposition of D(LFPII)
Period S.E. D(LFPII) D(LGDPR)
(QTR)
3 0.356 87.309 12.691
6 0.371 85.294 14.706
9 0.380 83.170 16.830
12 0.381 82.601 17.399
15 0.382 82.417 17.583
18 0.384 81.895 18.105
21 0.385 81.505 18.495
24 0.385 81.259 18.741
27 0.386 81.120 18.880
30 0.386 80.928 19.072
Variance Decomposition of D(LGDPR)
3 0.024 6.495 93.505
6 0.027 8.792 91.208
9 0.030 8.653 91.347
12 0.031 9.139 90.861
15 0.032 9.439 90.561
18 0.033 9.605 90.395
21 0.034 9.426 90.574
24 0.034 9.633 90.367
27 0.035 9.685 90.315
30 0.035 9.709 90.290
Table 5
Variance Decompositions of GDP and Volatility of FPI
Variance Decomposition or LGDPR
Period S.E. LGDPR VOL_LFPII
(Quarter)
3 0.044 92.027 7.973
6 0.063 90.000 9.999
9 0.070 88.527 11.473
12 0.075 88.109 11.891
15 0.081 88.571 11.428
18 0.086 88.800 11.200
21 0.090 88.661 11.339
24 0.093 88.450 11.549
27 0.096 88.588 11.412
30 0.098 88.681 11.319
Variance Decomposition VOL_LFPII
3 0.352 12.709 87.291
6 0.366 14.477 85.523
9 0.374 15.939 84.061
12 0.377 16.874 83.125
15 0.378 16.931 83.069
18 0.379 17.516 82.484
21 0.380 17.913 82.087
24 0.381 18.205 81.795
27 0.381 18.300 81.703
30 0.382 18.513 81.487
