Financial market integration in Pakistan: evidence using post-1999 data.
Khalid, Ahmed M. ; Rajaguru, Gulasekaran
1. INTRODUCTION
The recent wave of financial sector reforms and
internationalisation in emerging markets has increased perceived
interlinkages within various sectors of national financial markets. For
example, the existence of a strong linkage between stock prices and
exchange rates is a popular topic in academic research. Similarly,
changes in stock prices and exchange rates are expected to influence
movements in interest rates. A number of hypotheses suggest such a
causal relationship. For instance, using a goods market approach, any
changes in the value of currency would affect the competitiveness of
multinational firms and hence influence stock prices [Dornbusch and
Fischer (1980)]. Similarly, the hypotheses of 'exchange rate
pass-through' and 'interest rate pass-through' suggest
that changes in exchange rates and/or interest rates could affect stock
prices. The portfolio balance model suggests that fluctuations in stock
prices influence exchange rate changes.
Empirical research on the validity of the above stated hypotheses
have returned mixed results. In this paper, we only discuss recent
empirical work with a focus on emerging economies. Fukuda and Kano
(1997) investigated how prices in East Asian economies correlated with
those in Japan and the United States and found that overall price levels
in East Asia are more correlated to the price level in the United
States. Aggarwal and Kyaw (2005) studied equity market integration in
the NAFTA region and found evidence supporting an integrated market.
Khalid and Kawai (2003) found support for currency market linkages
within the East Asian region. Khalid and Rajaguru (2006) used a
multivariate GARCH model and found evidence indicating that East Asian
markets are interlinked. However, they did not find inter-linkage
between Indian and Pakistani currency markets. Pan, Fok and Liu (2006)
examined dynamic linkages between exchange rates and stock prices for
seven East Asian countries and found evidence of a causal relationship
between the two markets in all countries except Malaysia. (1)
Like many other emerging economies, Pakistan also implemented
policies of financial sector reform and liberalisation as early as the
1990s. (2) These reforms and other external factors had a positive
impact on the economy and led to a slight appreciation of the currency
as well as improvement in the country's credit rating. Table I
provides a summary of the basic economic indicators for Pakistan since
the 1970s with a focus on the period since 2000. (3) As a result of
these reforms and deregulation of many sectors of the economy, the
movements of important financial market indicators such as exchange
rates, stock prices and interest rates became reflective of market
forces. At the same time and due to minimum intervention by the State
Bank of Pakistan in the foreign exchange and domestic money markets,
these variables are subject to external and internal shocks. If markets
are interlinked then a shock (positive or negative) to one market is
expected to have an impact on other markets as well. This testable
hypothesis is the focus of our paper.
From Pakistan's perspective, there is limited research
available investigating inter-linkages and volatility spillover within
Pakistan's financial market. The empirical evidence in Qayyum and
Kemal (2006) suggests that volatility spillover takes place from the
stock market to the foreign exchange market but not vice-versa. In this
paper, we use high frequency data to investigate inter-linkages among
the three sectors of the financial market in Pakistan. We investigate if
the currency, stock and money markets in Pakistan are linked together.
We use three financial market indicators, namely; the exchange rate, the
stock price and interest rate, and investigate the presence of linkages
within these three markets. We use daily observations and Granger
causality, variance decomposition and impulse responses, in a VAR to
establish evidence of any market inter-linkages. The paper is organised
in the following manner. Section 2 following the Introduction discusses
the data and methodological details. The results of the empirical model
are presented in Section 3. Finally, some conclusions are drawn in
Section 4.
2. DATA AND METHODOLOGICAL DETAILS
We use daily observations on the exchange rate (WMR; against US
dollar), stock prices (MSCI index) and the interest rate (30-day repo)
for the period 12th October 1999 (the military takeover date) to the
13th September 2006. For empirical estimation, we split the sample into
two: a full sample and a sub-sample. The sub-sample covers the period
from 25th September 2001 to the 13th September 2006, thus focusing on
the post September 11 period. All data is obtained from the DataStream
database.
Unit Root Testing
The time series property of the data is examined by conducting ADF,
PP and KPSS unit root tests on the logarithm of exchange rates, stock
prices and interest rates. If the variables are non-stationary then
these three tests are conducted on a logged differenced series in order
to determine the correct order of integration. Both ADF and PP tests set
the null of non-stationary while the KPSS tests the stationary null
hypothesis. Together these three results determine the most robust
estimates for the order of integration.
Co-integration Analysis
In order to capture the dynamic relationships between the three
variables, we tested for any co-integration relationship among the
logarithm of exchange rates, stock prices and interest rates. If all
three variables are I (1) and are co-integrated then the linear
combination (co-integrating vectors) of one or more of these series may
exhibit a long-run relationship. And the dynamic linkages (causality)
between the variables could be determined through the vector error
correction model. On the other hand, it could be modelled as vector
autoregression (VAR) if either (i) all of these series are stationary or
(ii) these series are non-stationary but are not co-integrated. In our
study, we use the multivariate co-integration test based on the
Johansen-Juselius (1990) procedure to test for the existence of long-run
relationships between the exchange rate, stock prices and interest rate.
We begin the analysis by letting a vector of n-variables [z.sub.t]
possess the p-th order Gaussian vector autoregression (VAR) process
[Z.sub.t] = [mu] + [p.summation over (i=1)] [[PI].sub.i][z.sub.t-I]
+ [[epsilon].sub.i], t = 1,2, ..., T ... ... ... ... ... (3)
Where [mu] is a vector of constants and [epsilon], is a normally
and independently distributed ndimensional vector of innovations with
zero-mean non-singular covariance matrix [OMEGA]. And [z.sub.t] is a
vector of an endogenous variable. It is convenient to rewrite the above
process in the following error correction form:
[DELTA][z.sub.t] = [mu] + [p-1.summation over (i=1)]
[[GAMMA].sub.i] [DELTA][z.sub.t-1] - [[GAMMA].sub.0][z.sub.t-1] +
[[epsilon] ... ... ... ... ... (4)
Where [[GAMMA].sub.0] = I -([[PI].sub.1] + [[PI].sub.2] + ... +
[[PI].sub.p]) and [[PI].sub.2] + ... [[PI].sub.p]) and [[GAMMA].sub.i]
=- [p.summation (j=i+1] [[PI].sub.j], i = 1,2, ..., p. The long run n x
n matrix
[PI] is equal to [[GAMMA].sub.0] and it determines how many linear
combinations of [z.sub.t] are stationary. In particular, the rank of the
matrix [PI] r gives the number of independent co-integrating vectors.
The co-integrating ranks r (0<r<n) and hence, the number of
distinct co-integrating vectors can be formally tested with
[[lambda].sub.trace] and [[lambda].sub.max] statistics. The
[[lambda].sub.trace] statistic tests the null hypothesis that [H.sub.0]:
r = g vectors against the alternative that [H.sub.1]: r [less than or
equal to]g and it is given by
[[lambda].sub.trace](g) = -T [n.summation over
(i=g+1)ln(l-[[lambda].sub.i]). ... ... ... ... ... (5)
The [[lambda].sub.max] statistic tests the null hypothesis that
[H.sub.0]: r = g vectors against the alternative that [H.sub.1] r = g+ 1
and it is given by
[[lambda].sub.max](g) = -T log(l - [[lambda].sub.g+1]) ... ... ...
... ... (6)
where [[lambda].sub.i]'s are the Eigen values of [PI] such
that [[lambda].sub.1] > [[lambda].sub.2] > ... >
[[lambda].sub.n]. The optimal lag length p is determined by Schwartz
criteria.
Error Correction Models and Vector Autoregressions
As discussed earlier, if all variables are co-integrated then they
are modelled as a vector error correction model to capture both long-run
and short-run linkages between exchange rates, stock prices and interest
rates. On the other hand, if these three markets are not co-integrated
then they will be modelled as a vector error correction model. As we
shall see later, all three markets are not co-integrated; thus we
proceed with the discussion on vector autoregressions. The vector
autoregression (VAR) is commonly used for analysing the dynamic impact
of random disturbances on a system of variables. The VAR approach models
every endogenous variable in the system as a function of the lagged
values of all of the endogenous variables in the system and can be
specified as:
[y.sub.t] = [A.sub.l] [Y.sub.t-l] + ..... + [A.sub.p][y.sub.t-p] +
[[epsilon].sub.t] ... ... ... ... ... (7)
Where [y.sub.t], is a k vector of endogenous variables, [A.sub.1],
..., [A.sub.p] are matrices of coefficients to be estimated, and
[[epsilon].sub.t] is a vector of innovations that may be
contemporaneously correlated with each other but are uncorrelated with
their own lagged values and uncorrelated with all of the lagged
endogenous variables. It is very important to determine the lag length
before estimating a VAR. Rather than using a lag length arbitrarily, we
use three different criterions, namely, Akaike information criteria (AIC), Schwartz information criteria (SIC) and likelihood ratio (LR) to
determine the appropriate lag length. Surprisingly, a lag length of one
was justified by both the AIC and SIC while the LR was inconclusive. We
therefore chose a lag of one for the VAR system used in this study.
Since we have the same lag length, the system may be estimated using
Ordinary Least Squares (OLS).
The parameter estimates obtained from the estimated VAR model are
then used to identify any causal relationships among different markets.
This is accomplished by testing Granger causality and running a VAR on
the system of equations and testing for zero restrictions on the
appropriate VAR coefficients.
Next, we compute the variance decomposition to evaluate dynamic
linkages between the three markets. Variance decomposition decomposes
the forecast error variances (at different time-horizons) of one
variable into all variables in the system.
Later we analyse the impulse responses by introducing a shock in
each of the markets to analyse its impact on other markets. An impulse
response function traces the effect of one unit of shock of the
innovations on current and future values of the endogenous variables. A
shock to i-th variable directly affects the i-th variable, and is also
transmitted to all of the endogenous variables through the dynamic
structure of the VAR. Since innovations are usually correlated, one
cannot isolate the effect of the i-th variable on the j-th variable
without disturbing other variables. These shocks can be orthogonalised
by applying Cholaskey decomposition. However, orthogonalising the shocks
through Cholaskey decomposition add additional problems to this impulse
response analysis as the response functions are very sensitive to the
ordering of variables.
3. EMPIRICAL RESULTS
In this section we perform econometric tests to determine if the
three markets are interlinked over the sample period. We first perform a
unit root test to determine the order of integration of the three
series. The results reported in Table 2 indicate that all three series
are stationary in first differences. These results are consistent using
the three different tests (ADF, PP and KPSS) on both sample periods. The
next step is to determine if the series have any long-run relationships.
The co-integration test results are reported in Table 3 and do not
support any long-run relationship between the three variables for either
of the sample periods. Since the series are not co-integrated, any
possible market inter-linkages can be tested using the Granger causality
method. The results of Granger causality for the full sample period are
reported in Table 4a. These results suggest that changes in exchange
rates did cause fluctuations in stock prices in Pakistan during the
sample period. However, the same changes did not have any influence on
interest rates. There is also ernpirical evidence of a causal
relationship between stock prices and interest rates. Finally, the
results suggest that conversely, changes in interest rates did not
affect exchange rates or stock prices over the period under
investigation. The results of Granger causality for the sub-sample
period (see Table 4b) are consistent with the above findings. In
summary, these results establish a link between the three markets where
changes in the currency market influence the stock market which then
lead to some changes in the money market. (4)
Next, we perform variance decomposition analysis using the same two
sample periods. The results of these tests are presented in Tables 5a
(full sample) and 5b (sub-sample). These results suggest that most of
the variations in each market are due to its own lag(s). The impact of
cross-market variations is very small. These results are consistent with
the results obtained using the Granger causality tests.
Finally, we use impulse response analysis to verify the robustness
of our findings. Here, we introduce a shock in each of the markets and
observe the intensity and duration of these shocks across markets. The
results are reported in Figure l a (full sample) and l b (sub-sample)
which are, again, consistent with the earlier results. In general, the
results of these three different empirical methodologies suggest that
there is no long-run relationship between the three financial market
variables but one can find a short-term link from the currency market to
the stock market to the money market.
[FIGURE 1a OMITTED]
[FIGURE 1b OMITTED]
4. CONCLUDING REMARKS
This study examined whether dynamic linkages existed amongst the
currency (foreign exchange), stock and money markets in Pakistan. We
used high frequency data (daily observations for the exchange rate,
stock prices and interest rate) and three different empirical testing
procedures to determine if the three markets are interlinked in
Pakistan. Based on co-integration tests, the empirical results failed to
find support for a long-run relationship among the three markets. The
Granger causality tests, however, found empirical evidence suggesting a
causal relation from the currency market to the stock market and from
the stock market to the money market, thus suggesting a link amongst the
three markets. The results for the sub-sample are similar. The empirical
findings based on variance decomposition and impulse response analysis
are consistent with the above findings where most of the variations in
each market variable can be explained by its own lag. It is interesting
to note that our results are consistent with the theoretical hypothesis
mentioned in Section 1 of this paper. Given that the three markets are
linked, any internal or external shock would affect all three markets in
a direct or indirect way. This is an important finding and could have
important policy implications. For example, policy makers, while making
a decision on internal policy should be mindful of the implications of
their decision. On the flip side, policy makers could take a priori measure in one of the markets (e.g. interest rate) if an external shock
is forthcoming and expected to hit a market (e.g. foreign exchange). A
possible future extension of this research could be to use data from
actual shocks and analyse its impact in one of the markets and then see
if the shock is transmitted to the other two markets. This issue will be
explored in a separate paper.
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in Asia: 21st Century Challenges. UK: Edward Elgar Publishing Company,
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the Current Account. American Economic Review 70:5, 960-71.
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and Inference on Cointegration: With Application to the Demand for
Money. Oxford Bulletin of Economics and Statistics 52, 169-210.
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Contagion: Evidence from Asian Crisis Using a Multivariate GARCH Model.
Paper presented at the Asian Finance Association Conference, Auckland,
New Zealand, July.
Khalid, Ahmed M. and M. Ali Kemal (2005) The Choice of an Anchor
Currency under a Tri-polar Regime: The Case of South Asian Countries.
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Linkages in South Asia: Evidence Using a Multivariate GARCH Model. The
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Pan, Ming-Shiun, Robert Chi-Wing Fok, and Y. Angela Liu (2006)
Dynamic Linkages Between Exchange Rates and Stock Prices: Evidence from
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(forthcoming.)
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Stock Market and Forex Market in Pakistan. PIDE Seminar, October.
Comments
The study investigates the linkages among three markets of the
financial sector of Pakistan, namely, currency, stock, and money, using
three financial market indicators for them--exchange rate, the stock
price, and the interest rate. It uses various methods, namely, Granger
causality, variance decomposition, and impulse responses to explore the
linkages among the three markets. However, I have the feeling that I
should read all their papers before giving comments. Because it seems to
me that they assume that the readers have sufficient knowledge for the
terms used in the studies. Or they assume that the readers have read all
their papers.
Here are some aspects on which they may focus.
(1) The title of the paper indicates that the main object of the
paper is to measure the impact of political shocks. Whereas in the paper
it is a secondary issue, the first issue is to find the linkages between
three markets, currency, stock and money.
(2) Impulse response analysis has been conducted to verify the
robustness of the findings by introducing a shock. The study does not
mention what type of shock it is? However, in the beginning of the paper
study mentioned about 9/11 event. Is it that?
(3) The authors use the terms like 'Political event' or
'certain political event' but did not say explicitly which
political event? Though, in the introduction the event of September 11
has been discussed? If you are referring September 11 event. Do you
consider it a political event?
(4) In the presentation it is said that 9/11 led to stability in
financial market. So what you suggest from these results? I think we
should explicitly mention that large foreign capital inflow causes the
stability. Link is from 9/11 to capital inflow and to stability.
(5) Variance decomposition for dynamic analysis show that 99
percent variation in each market is due to its own lag. The impact of
its cross market variation is very small even less than 0.5 percent
(Table 5a). How robust the results are to conclude that markets are
interlinked.
(6) It will be good if authors give full reference for various
methods/terms used in the paper such as Cholaski decomposition, Markov
Switching process, Impulse response, Akaik information criteria,
Schwartz information criteria and likely hood ratio. The interested
reader can read the detail.
(7) The authors wrote that the results of Granger causality for the
sub-sample period are consistent with the results from the results of
the full sample period without any empirical testing. There are some
statistical methods to test if the two regressions are same or not i.e.,
chow test. If we want to test change in impact over two periods then we
have to brake down sample into two and estimate two separate function
then we can test the difference in the impact.
(8) The results need to be explained more as they show that there
is a causal relationship which run from exchange rate (currency
market-CM) to stock prices (stock market-SM) and then to interest rate
(money market-MM). The reverse relationship does not exist from MM to SM
to CM. But they did not explain the reasons. Or is it plausible or not?
(9) The results show that the recursive model as causation runs
from money market to stock market to currency market, but not in the
reverse order. This type of model can be estimated by SURE method.
(10) Abstract should be changed as it does not seem to fit the
paper. From the title and the abstract it seems to me that main
objective is to investigate the impact of a political shock. Whereas I
found a very small part of the paper focusing on this issue. Most of the
time it discusses the various methods to find linkages between the
markets.
(11) ADF, Phillip-Perron (PP) and KPSS unit root test are used to
check the stationary property of the data. The authors wrote that
'all the three tests determine the most robust estimates for the
order of integration'. But, they did not mention the order of
integration.
(12) I found missing references or proper references. For instance,
they used data stream data base. It is fine but it will be better if
they give exact web address.
(13) Last I would say that the relationship between the three
markets may be strong and valid in the case of complete flexible
exchange rate and if all prices are determined by valid in the case of
complete flexible exchange rate and if all prices are determined by
market forces. Week link between the market show that economy is still
under control.
If authors can clarify the above-mentioned points, the paper will
become more reader-friendly.
Rizwana Siddiqui
Pakistan Institute of Development Economics,
Islamabad.
(1) Abdalla and Murinde (1997), Chen and Rui (2002), Click and
Plummer (2005), and Dekker, Sen, and Young (2001) also discuss
interlinkages within exchange rate and stock markets in some emerging
economics.
(2) For a detailed discussion on financial sector reforms and
development in Pakistan, see Ariff and Khalid (2005). Hussain and Qayyum
(2005) and Hussain (2006) also look at stock market libcralisation in
Pakistan and the region.
(3) It can be seen from Table 1 that by 2003, for the first lime in
the last three decades, Pakistan experienced a growth rate of 5.1
percent with single digit inflation (3.3 percent). This momentum
continued until 2004 when the economy registered a growth rate of 6.8
percent. Although the overall economic growth rate continued to increase
in 2005 (reaching a level of 7.8 percent), some other indicators showed
a slowing down in the economy. For example, inflation jumped from 4.5
percent to 9.3 percent in 2005. Fiscal deficit (as a ratio to GDP)
increased from (-) 2.1 percent to (-) 4.1 percent. Similarly. trade
balance (as a ratio to GDP) rose from (-) 1.3 per cent to (-) 4.1
percent. These declining trends suggest that the economy experienced
some negative shocks during 2004.
Ahmed M. Khalid <
[email protected]> is Associate Professor
of Economics and Finance and Gulasekaran Rajaguru
<
[email protected]> is Assistant Professor of Economics, School
of Business, Bond University, Gold Coast, Australia.
Table 1
Basic Economic and Social Indicators of Development in
Pakistan
Indicators 1961-70 1971-80 1981-90
National Accounts
GDP Growth (US$) 3.35 4.81 6.19
Per Capita GDP (USS) 138.86 180.18 327.06
Private Consumption/GDP 77.71 79.00 76.92
Government Consumption/GDP 12.51 13.79 17.06
Financial Indicator (%)
Gross Domestic Savings/GDP -- 13.81 13.83
Fixed Capital Formation/GDP 15.37 15.38 16.96
Inflation (per Year) 3.51 12.42 6.98
M2/GDP 36.14 41.76 41.25
Fiscal Balance/GDP -5.17 -7.41 -6.74
Trade Balance/GDP -- -8.06 -9.31
Current Account Balance /GDP -- -5.35 -2.91
Total Trade/GDP 21.20 28.00 33.59
Debt/Exports 403.90 606.09 509.28
Debt/GDP 33.91 61.96 64.15
Foreign Reserves; Imports 21.27 17.98 11.52
Indicators 1991-95 1996-2000 2000
National Accounts 4.85 3.07 4.26
GDP Growth (US$) 404.85 438.82 -326.64
Per Capita GDP (USS) 70.81 73.99 74.43
Private Consumption/GDP 18.16 15.51 15.01
Government Consumption/GDP
Financial Indicator (%) 14.81 13.29 14.4
Gross Domestic Savings/GDP 18.07 15.41 14.37
Fixed Capital Formation/GDP 11.20 7.30 4.37
Inflation (per Year) 43.39 46.63 46.92
M2/GDP -7.67 -6.91 -5.47
Fiscal Balance/GDP -5.15 -3.73 -2.4
Trade Balance/GDP -4.49 -3.17 -0.14
Current Account Balance /GDP 36.73 35.16 34.30
Total Trade/GDP -- -- 550.66
Debt/Exports -- -- 90.00
Debt/GDP 14.24 10.56 14.23
Foreign Reserves; Imports
Indicators 2001 2002 2003
National Accounts 2.72 4.41 5.0
GDP Growth (US$) 380.54 439 542
Per Capita GDP (USS) 75.15 74.96 73.6
Private Consumption/GDP 13.65 15.25 8.9
Government Consumption/GDP
Financial Indicator (%) 14.6 13.6 17.5
Gross Domestic Savings/GDP 14.29 12.33 16.9
Fixed Capital Formation/GDP 3.15 3.29 3.19
Inflation (per Year) 48.3 51.74 47.0
M2/GDP -4.71 -4.62 -4.1
Fiscal Balance/GDP -2.3 -0.5 -0.4
Trade Balance/GDP 3.41 4.5 4.9
Current Account Balance /GDP 37.37 35.75 --
Total Trade/GDP 260.7 211.2 * 189.1 *
Debt/Exports 45.7 ** 48.7 ** 44.8 **
Debt/GDP 34.05 71.86 --
Foreign Reserves; Imports
Indicators 2004 2005
National Accounts 6.4 7.8
GDP Growth (US$) 610 709
Per Capita GDP (USS) 73.3 80.0
Private Consumption/GDP 8.4 7.8
Government Consumption/GDP
Financial Indicator (%) 18.4 12.2
Gross Domestic Savings/GDP 17.3 17.8
Fixed Capital Formation/GDP 4.49 9.3
Inflation (per Year) 49.4 48.9
M2/GDP -2.1 -4.1
Fiscal Balance/GDP -1.3 -4.l
Trade Balance/GDP l.9 -1.4
Current Account Balance /GDP 31.7 34.1
Total Trade/GDP 176.3 * --
Debt/Exports 38.0 ** --
Debt/GDP -- --
Foreign Reserves; Imports
Source: IMF International Financial Statistics (CD-ROM),
World Development Report (Various Issues) and Asian Development
Outlook (Various Issues); Ariff and Khalid (2005).
* Numbers are for external debt to exports ratio.
** Numbers are for to GDP ratio.
Table 2
Unit Root Tests
Full Sample
(12 October 1999 - 13
September 2006)
ADF PP KPSS
ER -2.26 -2.24 1.29 ***
SP -1.88 -1.91 0.79 ***
IR1 -1.39 -1.98 1.41 ***
[DELTA]ER -26.9 *** -38.72 *** 0.34
[DELTA]SP -41.1 *** -41.09 *** 0.12
[DELTA]IRI -44.7 *** -51.9 *** 0.17
Sub-sample
(25 September 2001 - 13
September 2006)
ADF PP KPSS
ER -2.81 -2.86 0.91 ***
SP -3.01 -3.01 0.49 ***
IR1 -2.68 -1.92 1.26 ***
[DELTA]ER -15.77 *** -35.88 *** 0.32
[DELTA]SP -35.18 *** -35.18 *** 0.12
[DELTA]IRI -37.98 *** -45.04 *** 0.28
(4) These findings are consistent with Khalid and Kemal (2005)
and Khalid and Rajaguru (2004).
Table 3
Co-integration Test
Co-integration between the Exchange Rate, Stock Prices and
Interest Rate
Full Sample Sub-sample
(12 October 1999 - 13 (25 September 2001 - 13
September 2006) September 2006)
Trace Max Trace Max
r = 0 14.67 9.74 29.06 16.99
r = 1 4.93 4.50 12.07 11.35
r = 2 0.43 0.43 0.72 0.72
Table 4a
Granger Causality between the Exchange Rate, Stock Prices and
Interest Rate (Full Sample: 12 October-1999 - 13 September 2006)
[DELTA]ER [DELTA]SP [DELTA]IR
[DELTA]ER -- 2.54 ** 0.29
[DELTA]SP 0.12 -- 0.20 *
[DELTA]IR 1.01 1.38 --
Table 4b
Granger Causality between the Exchange Rate, Stock Prices and
Interest Rate (Sub-.sample: 25 September 2001- 13 September 2006)
[DELTA]ER [DELTA]SP [DELTA]IR2
[DELTA]ER -- 2.53 ** 0.74
[DELTA]SP 0.12 -- 2.44 *
[DELTA]IR2 0.09 1.55 --
Table 5a
Variance Decomposition of Exchange Rate, Stock Prices and Interest
Rate (Full Sample: 12 October 1999 -13 September 2006)
Decomposition of [DELTA]ER
[DELTA]ER [DELTA]SP [DELTA]IR1
1 100.00 0.000 0.000
2 99.98 0.001 0.022
3 99.96 0.009 0.027
4 99.80 0.027 0.177
5 99.80 0.028 0.177
6 99.79 0.028 0.180
7 99.79 0.028 0.182
8 99.79 0.028 0.182
9 99.79 0.028 0.182
10 99.79 0.028 0.182
Decomposition of [DELTA]SP
[DELTA]ER [DELTA]SP [DELTA]IR1
1 0.04 99.96 0.00
2 0.04 99.88 0.07
3 0.07 99.86 0.07
4 0.49 99.28 0.23
5 0.49 99.27 0.23
6 0.50 99.27 0.24
7 0.50 99.27 0.24
8 0.50 99.27 0.24
9 0.50 99.27 0.24
10 0.50 99.27 0.24
Decomposition of [DELTA]IR1
[DELTA]ER [DELTA]SP [DELTA]IR1
1 0.16 0.02 99.83
2 0.16 0.35 99.50
3 0.18 0.35 99.47
4 0.20 0.38 99.43
5 0.20 0.38 99.43
6 0.20 0.38 99.42
7 0.20 0.38 99.42
8 0.20 0.38 99.42
9 0.20 0.38 99.42
10 0.20 0.38 99.42
Table 5b
Variance Decomposition of Exchange Rate, Stock Prices and Interest
Rate (Sub-.sample: 25 September 2001 - 13 September 2006)
Decomposition of [DELTA]ER
[DELTA]ER [DELTA]SP [DELTA]IR1
1 100.00 0.000 0.000
2 99.97 0.000 0.026
3 99.97 0.001 0.026
4 99.97 0.001 0.026
5 99.97 0.001 0.026
6 99.97 0.001 0.026
7 99.97 0.001 0.026
8 99.97 0.001 0.026
9 99.97 0.001 0.026
10 99.97 0.001 0.026
Decomposition of [DELTA]SP
[DELTA]ER [DELTA]SP [DELTA]IR1
1 0.08 99.92 0.00
2 0.08 99.84 0.08
3 0.08 99.84 0.08
4 0.08 99.84 0.08
5 0.08 99.84 0.08
6 0.08 99.84 0.08
7 0.08 99.84 0.08
8 0.08 99.84 0.08
9 0.08 99.84 0.08
10 0.08 99.84 0.08
Decomposition of [DELTA]IR1
[DELTA]ER [DELTA]SP [DELTA]IR1
1 0.14 0.02 99.84
2 0.14 0.36 99.49
3 0.14 0.37 99.49
4 0.14 0.37 99.49
5 0.14 0.37 99.49
6 0.14 0.37 99.49
7 0.14 0.37 99.49
8 0.14 0.37 99.49
9 0.14 0.37 99.49
10 0.14 0.37 99.49