Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.03
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MACRO-ECONOMIC DETERMINANT AND INTERDEPENDENCE OF THE
STOCK MARKETS: EVIDENCE FROM EMERGING ECONOMIES
Asim Rafiq
Department of Public Administration, Karachi University, Karachi, (Pakistan)
E-mail: asim_r83@hotmail.com
Shahbib Hassan
Department of Public Administration Karachi University, Karachi, (Pakistan)
E-mail: shassan@uok.edu.pk
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.03
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ABSTRACT
The purpose of the study is threefold. First, is to examine the long-term
interdependence between China and the ten emerging economies, including
Pakistan, Malaysia, Philippine, Indonesia, India, Hungary, Mexico, Russia, South
Africa and Brazil using Johansen co-integration. Second, is to measure the time-
varying interdependence between China and the other emerging economies using
DDC GARCH model. Third, is to examine the impact of macroeconomic
determinants on stock markets conditional correlations using panel regression.
Monthly data from 2010 to 2016 is used. Results indicate that there is long-term
interdependence between China and the other ten emerging economies.
Furthermore, the results of DDC GARCH model support that China has a higher
positive significant correlation with Pakistan, India, China, Indonesia, Malaysia,
Philippine, Hungary, Mexico, Russia and South Africa. Finally, the results of the
panel regression show that macroeconomic determinants have no significant effect
on the equity market correlations between China and its companion emerging
economies. It this, therefore, we can conclude that there is long run
interdependence between the Chinese and the other emerging economies.
Furthermore, this interdependence is also dynamic over the time. However, there
is no significant impact of the macroeconomic determinants on the stock market
interdependence between Chinese and the ten emerging economies.
KEYWORDS
Co-integration, DCC GARCH, Macro-economic determinants, Panel regression.
1. INTRODUCTION
Over the last few decades, government agencies and the key policymakers of both
developing and the developed nations have attempted a few measures to abolish the
hindrances among the nations to ensure the free stream of resources. This has
significantly contributed to the interdependence of the economies and the effect of
this association on their equity markets linkages. However, there are predominantly
two distinct features in the realm of stock market interdependence. First, to what
extent stock markets move together over the period. Second, what are the possible
factors behind such a process? Earlier studies paid attention to the first aspect of the
stock market interdependence for e.g.[1]–[4]. On the one hand, the more
contemporary studies investigated the developed and the developing stock markets
of USA, European, ASEAN and Asian markets. On the other hand, most of the
previous studies examined the stock market interdependence in terms of
correlation. Whereas, it is commonly believed that correlation has several
deficiencies including the existence and the instability of lags. Therefore, even if the
low correlation exists among the stock markets, this can be deceptive if it is time-
varying [5]–[9].
Moreover, recently financial researchers have mainly devoted their attention to the
[10]emerging economies stock markets for e.g. [6], [11], [12]. In view of that fact,
emerging markets have distinctive characteristics from of the developed markets in
terms of economic conditions, political structure, higher volatility, high
interdependence, mean returns, currency, and the low correlation with the
developed stock markets [13]–[15]. However, in these studies interdependence has
been measured between emerging and the developed economies stock markets.
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
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On the one hand, some previous studies found a strong linkage between
macroeconomic factors and the equity market, while some other found that these
linkages are rather not robust [16], [17]. On the other hand, [18] investigated what
are the factors behind the stock market interdependence of emerging economies.
[19]–[21] studies the cross-market linkages between Australia and its trading
partners and found trade ties result in equity market interdependence.
But to our best knowledge, there is no major contribution regarding stock market
interdependence in emerging economies after the Pretorius because financial crises
divert the attention of the researchers. So, in this study it is endeavor first, to fill up
the gap and identify what macroeconomic factors are behind the stock market
interdependence in emerging economies context, as emerging economies grow in
number since last twenty years and secondly, there is dearth of empirical
investigation on stock market interdependence that takes the viewpoint of emerging
economies, the use of emerging economies perspective provides an opportunity to
address this particular gap.
This paper is also different from the other papers in the sense that in this paper the
major driver of equity market belongs inside the emerging economy which is
Chinese stock market rather than any developed world like in other studies for e.g.
US equity markets are considered to be the benchmark equity market as the
significance of China is the second largest and shares the major world output among
the top ten economies. Moreover, presently China share has grown to 15.1%, while
the share of Japan and the USA has fallen down to 31.1%% by 2017 [22].
The aim of this paper is threefold. First, is to observe the interdependence among
the emerging economies stock markets, second, measure the time varying
relationship among the stock markets of emerging economies. Finally, is to detect
the possible macroeconomic determinants behind the interdependence among
these markets. The subject of stock market interdependence has immense,
theoretical, policy and practical significance. The foremost benefit of the
interdependent market is that cost of and access to foreign investment lower and
easier, respectively. To achieve the objective of the study first, we employ the
Johansen multivariate co-integration test to ascertain long term association
(interdependence). Second, to examine the time-varying association we employ
DCC GARCH model. Finally, to detect the influence of macroeconomic variables on
the security market interdependence this study employs a panel regression model.
2. LITERATURE REVIEW
Stock market interdependence has been tested employing several techniques but
the empirical shreds of evidence are mixed. Studies focus on the co-integration
techniques includes: [23] was among the first to implement the co-integration
technique for the analysis of the interdependence among the United Kingdom and
Japan, Germany and USA after the abolition of the currency restrictions in the
United Kingdom. It was found that UK equity market was correlated with all except
the USA market.
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
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Researchers commonly belief that global diversification benefits can only be reaped
if the correlation between the equity markets is low. However, the correlation
between the equity markets is not stable over the time. It is, therefore, crucial to
understand the instability of the correlation over the time. Initially, [24] researched
the instability of the correlation and the covariance and found that correlation is
stable over a fifteen year period during 1967-82. The results from past studies are
mixed because most of the previous literature examined the market
interdependence under the liner frame.
However, the liner co-integration technique unable to present whether the equity
markets have become more integrated or whether the process of integration is
gradual. According to [13] market interdependence is time-varying. To address this
issue several studies consider the non-linear framework. [8] Uses the complex
network analysis and the corresponding correlation measure to examine the
underlying dynamic interdependence of the equity markets. [6] examined the
twenty-two emerging equity markets situated in Europe, Asia, America, and the
Africa/Middle East with the US market by applied a different approach as most of
the researchers used, which is wavelet theory for empirical testing and It was found
that the integrated intensity of the stock markets is time varying. [25] Tested the
dynamic conditional correlation between the Chines and the international stock
markets. It is substantiated through the results that correlations across the markets
are time-varying. It is also identified that dynamic correlation is compactly linked
with the geographic location. [26] Also confirmed that dynamic conditional
correlation between S&P 500 and S&PGSCI energy sub-index is time- varying. It is
therefore, we can infer that interdependence between the equity markets is dynamic
and it is a gradual process.
So based on the literature the DDC GARCH methodological approach has
succeeded in capturing is the dynamic conditional correlation. It permits the
researchers to comprehend the change in the conditional correlation and the
volatilities which is the more precise representation of the fact.
Literature gives an exposition of the theoretical understanding of why the co-
movement between the stock markets exist. on the one hand, variables those are
perceived to be the main driver of the interdependence of the stock market in the
developed economies are bilateral trade, exchange rate volatility, Size differential,
market volatility, size differential, real interest rate differential, term structure
differential, industrial composition and return on world market index.
Alternatively, the variables that influence the stock market interdependence are still
mainly undiscovered in case of emerging equity markets. In general changes in
these variables over the time also affects the stock market interdependence.
According to the early studies, foreign trade promotes business cycle harmonization
through the countries and consequently impacts the degree of their market
interdependence for example [27]–[30] ascertained that trade is a significant
variable in describing the correlation between the stock markets. similarly, Pretorius
(2002) if interdependence between two economies due to bilateral trade,
consequently, one can anticipate that there stock market and the economy will move
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.03
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in the similar route. Based on these theoretical and empirical pieces of evidence it
is concluded that trade is a significant factor in describing the interdependence.
Numerous past studies use exchange rates volatility and the inflation differential to
observe their impact on stock market interdependence [18]. The outcomes from
these limited studies indicate that the exchange rate has a significant effect on the
equity market interdependence [31]–[33]. Similarly, [34] also confirm the previous
findings and documented negative effect of exchange rates on stock market
interdependence.
Based on the above critical review of the literature it can be argued that market
interdependence is a conflicting issue. This implies that it is a time-varying
phenomenon even among the similar markets and furthermore, it also varies
among countries development level for e.g. developed, developing,
underdeveloped and emerging.in addition to this, bilateral trade relationships,
industrial production, inflation, exchange rate are possible factors in describing the
stock market interdependence.
In the process of the review of the literature, we find the gap in three areas first,
there is a number of studies conducted in the most mature and the developed world
and mostly the benchmark economy have been US stock market. Secondly, most of
the studies use correlation or the co-integration to measure the stock market
interdependence. Lastly, there is no comprehensive study after the [18] which re-
examine the interdependence of the stock markets in terms of economic
interdependence among the emerging economies stock market using the DDC
GARCH model to also consider time-varying behavior.
3. DATA AND METHODOLOGY
On the one hand, we Johansen co-integration to examine long run static
interdependence. On the other hand we applied DCC GARCH model to assess the
long run time-varying interdependence. Following are the representative index of
each country. KSE 100 index (Pakistan), S&P BSE SENSEX (India), SSE Composite
(China), JSKE (Indonesia), FTSE Bursa Malaysia KLCI (Malaysia), PSEi
(Philippine), BVSP (Brazil), BUX (Hungary), MMX (Mexico), MICEX (Russia) and
FTSE/JSE (South Africa).
Further, to study the impact of macroeconomic determinant on stock market
interdependence we run a Panel regression model. For this purpose we collected
data of differential of bilateral trade, inflation (CPI), interest rate and exchange rates
(local currency) from IMF Financial statistics database. Only bilateral trade data is
transformed into natural logarithm form and the other factors are used in their
initial form.
In this study, we apply the well- known multivariate GARCH model, namely the
DDC GARCH model. The key benefit of the DDC GARCH model as compared to
other time-varying models, for instance, Flexible Least Square and Kalman filters
are that it permits the researcher to understand the shifts in conditional correlations
and volatilities which is the more correct picture of the reality. Lastly, the panel
regression techniques is an effective and efficient in terms of measuring the cause
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
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and effect of industrial production, bilateral trade, exchange rate, inflation and
interest rate on time-varying conditional correlations of stock market returns.
4. EMPIRICAL RESULTS
4.1. Multivariate co-integration test
After unit root testing as a prerequisite condition for co-integration testing, we
examine the long-term co-movement between China and the other emerging
economies by employing the multivariate VAR based co-integration technique
developed by [35], [36]. Results of the multivariate co-integration are discussed
below.
Table 1. Multivariate co-integration.
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
No. of CE(s)
Eigenvalue
Statistic
Prob.**
None *
0.620
406.469
0.000
At most 1 *
0.543
323.234
0.000
At most 2 *
0.516
255.852
0.000
At most 3 *
0.468
193.523
0.000
At most 4 *
0.435
139.285
0.005
At most 5
0.270
90.238
0.113
At most 6
0.245
63.132
0.152
At most 7
0.198
38.960
0.262
At most 8
0.123
19.959
0.426
At most 9
0.089
8.6380
0.310
At most 10
0.007
0.609
0.435
Trace test indicates 5 co-integrating eqn(s) at the 0.05 level
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
Max-Eigen
No. of CE(s)
Eigenvalue
Statistic
Prob.**
None *
0.620
83.235
0.002
At most 1 *
0.543
67.382
0.026
At most 2 *
0.516
62.329
0.020
At most 3 *
0.468
54.238
0.032
At most 4 *
0.435
49.047
0.024
At most 5
0.270
27.106
0.626
At most 6
0.245
24.171
0.443
At most 7
0.198
19.001
0.415
At most 8
0.123
11.321
0.615
At most 9
0.089
8.029
0.376
At most 10
0.007
0.609
0.435
Max-eigenvalue test indicates 5 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
Grounded on these outcomes, the null hypothesis of no co-integration between
these markets can be rejected (see. Table. 2). These outcomes are similar to [4], [21],
[37], [38]. It is, therefore, on the basis of results we reject the null hypothesis as
stated below:
Ho: There is no long-run relationship between emerging and Chinese stock
markets.
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
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Table 2. Time-varying conditional correlations Between China and each country.
Country
Index
Correlation
coefficient
P-value
Pakistan
KSE
0.845
0.000
Malaysia
KLCI
0.813
0.000
Philippine
PSEi
0.834
0.000
Indonesia
JKSE
0.834
0.000
India
BSENSEX
0.860
0.000
Hungary
BUX
0.947
0.000
Mexico
MMX
0.915
0.000
Russia
MICEX
0.957
0.000
South Africa
JSE
0.986
0.000
Brazil
BVSP
-0.708
0.000
Table 2 testifies the conditional correlations between China and the rest of emerging
economies in MSCI index. The calculated correlations in the above table support
that China has the higher positive significant correlation with Pakistan, India,
China, Indonesia, Malaysia, Philippine, Hungary, Mexico, Russia and South Africa.
However, Brazil is an exception with higher negative significant correlation with
China stock market. On the basis of the empirical analysis of the time-varying
behavior of stock markets, we can reject the below mentioned null hypothesis.
H
o:
There is no dynamic relationship between china and the other emerging
economies.
4.2. Impact of macro-economic determinants on stock market correlations
using Panel regression model
To analyze the influence of the macroeconomic determinants on the stock market
correlations between the China and the emerging economies we apply the panel
regression model.
Table 3. Result of Random Effect Model.
Dependent Variable: Correlation
Method: Panel EGLS (Cross-section random effects)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
4.633
8.500
0.545
0.585
D(Trade)
8.899
5.788
0.015
0.987
D(Exchange rate)
-9.811
9.988
-0.098
0.922
D(Industrial Production)
2.499
1.277
0.196
0.845
D(INFLATION)
-2.688
2.699
-0.999
0.318
D(Interest rate)
-0.001
0.000
-1.753
0.080
The result of random effect model presents that there is no single macroeconomic
determinant among bilateral trade, inflation differential, interest rate differential,
industrial production differential and exchange rate differential, which report the
significance. The results show that macroeconomic determinants have no significant
effect on the stock market correlations between China and its companion emerging
economies. It is, therefore, we cannot reject the null hypothesis as stated below:
Macro-Economic determinant and interdependence of the stock markets: evidence from emerging economies
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Ho: There is no significant impact of macroeconomic determinants on stock market
correlations between China and emerging economies.
It is therefore, we can conclude macroeconomic determinants have insignificant
impact on the stock market correlations between China and the emerging
economies stock markets. However, the results of this study are dissimilar to those
of [39], [40] who document that macroeconomic linkages among the countries can
drive their stock market interdependence.
5. CONCLUSION
In this paper, first, we investigate the interdependence from the perspective of
China and its companion emerging economies stock markets including Chinese,
Pakistan, Malaysia, Indonesia, Philippine, Brazil, Mexico, Hungary, Russia, South
Africa, and India, through the application of multivariate Johansen co-integration
technique. Second, we determine the degree of the interdependence between these
markets, through the application of DDC GARCH model. Third, we examine what
macroeconomic determinants are significant in establishing the interdependence
between China and its companion emerging economies, this study has empirically
analyzed the dynamic association between the equity markets and the
macroeconomic determinants using panel regression analysis.
Results indicate that Chinese stock market are co-integrated with stock market of
the other emerging markets. Centered on these outcomes, the null hypothesis of no
co-integration between these markets can be rejected. These outcomes are
consistent with the earlier studies like (Masih and Masih, 1999; Shamsuddin and
Kim, 2003; Kazi, 2008; Paramati, Gupta and Roca, 2015). In addition to this, The
results of DDC GARCH model support that China has a higher positive significant
correlation with Pakistan, India, China, Indonesia, Malaysia, Philippine, Hungary,
Mexico, Russia and South Africa. However, Brazil is an exception with higher
negative significant correlation with the Chinese stock market.
It also confirms that the relationship between China and the other emerging
economies has been increasing over the time except for Brazil. Finally, the results
of the panel regression show that macroeconomic determinants have no significant
effect on the equity market correlations between China and its companion emerging
economies. It this, therefore, we can conclude that there is long run
interdependence between the Chinese and the other emerging economies.
Furthermore, this interdependence is also dynamic over the time. However, there
is no significant impact of the macroeconomic determinants on the stock market
interdependence between Chinese and the other emerging economies. The
outcomes of this study will significantly contribute to the current literature, from
the perspective of both the investors and the policymakers.
6. ACKNOWLEDGEMENTS
We thanks all who support in this paper.
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