What Are Their Relationships among the Indices of Emerging ...Document Transcript
What Are Their Relationships among the Indices of Emerging Markets?
Tam-Kang University, Taiwan
Tam-Kang University, Taiwan
In this study, we employed VECM to analyze the relationship of indices among the
three emerging markets, USA and the world. The variable of the three emerging
markets adopts MSCI Emerging Market Series Index, the variable of world index
MSCI World Index, and the variable of USA stock price S&P 500 Index. The
conclusion of this study are: 1. The three emerging markets tends to be convergent in
the long term; 2.USA stock price still influences the three emerging markets greatly;
3.The impact of world factor on the three emerging markets is not significant; 4.
Emerging Asia market is on the rise.
Keywords: emerging market, stock index, VECM
Since the 1980s, there have been substantial changes in political and economic
environments in many regions, such as China, India, Eastern Europe, and Latin
America. As a result, massive capital flows in and out of emerging stock markets last
decade, and emerging markets now represent a feasible investment alternative for
international investors (Bilson et al., 2001). Returns and risks in emerging markets have
been found to be higher relative to developed markets (Errunza, 1983; Claessens et al.,
1993; Harvey, 1995). Emerging market equities have had: (1). higher average returns;
(2). lower correlation’s with developed markets; (3). greater serial correlation; and (4).
greater volatility (Errunza, 1997; Harvey, 1995; Eaker et al., 2000).
The main emerging markets are grouped in three areas: emerging Asia, emerging East
Europe, and emerging Latin America. Emerging Asia not only is the world's most
populous area but also owns huge markets. The most notable advantage of emerging
East Europe is the rich natural resources. Meanwhile, emerging Latin America has
remarkable quantity of commercialization of agricultural product. All of them are
attractive areas for the investors. However, they are so different from each other in
many aspects. The relationships among them are ambiguous and unclear. Are they
integrated or segmented? It is hard to answer.
There have been numerous studies that have focused on the issue of market
integration and segment. The integration and interdependence of stock markets
underlies a major cornerstone of modern portfolio theory that addresses the issue of
diversifying assets (Masih and Masih, 2001). For example, Wheatly (1988) who argues
that even without market integration, assets that are diversified internationally could be
“mean-variance efficient”. The advantages of asset diversification have already been
widely discussed in the literature in which much effort was devoted to quantify risk-
reduction and its associated benefits available to the internationally diversified portfolio
(Solnik, 1991). Determining the extent to which a national equity market is segmented
from international financial markets is thus an empirical question of great interest to
both investors and researchers.
Our major aim in this study is to make an attempt to extend the existing literature by
focusing on the patterns of linkages among the three emerging regions, the USA, and
the world portfolio. This paper is organized in the following manner: Section 2 briefly
describes the theoretical basis. A discussion of the data is then presented in Section 3.
Section 4 contains the methodology to be adopted, and the estimation results. Finally,
the conclusions and limitations of the study are presented in Section 5.
2. Literalities Review
A persistent issue of international finance is the extent to which international financial
markets are integrated. One important implication of integrated markets is that assets
associated with similar levels of risk in different countries should also lead to a similar
level of return. This issue has been empirically addressed in several studies (Errunza
and Losq, 1985; Hietala, 1989; Brealey et al., 1999) as well as placed under critical
scrutiny due to inconsistent results. That is, the same asset pricing relationships apply in
all countries regardless of their geographical location in fully integrated capital
markets. In financial economics, the assumption of the markets are integrated is often
employed (Peresetsky & Ivanter, 2000). For example, the Black-Scholes model
assumed that stock, bond and options markets are perfectly integrated so that no cross-
market arbitrage opportunities exist.
On the other hand, when markets are segmented, the risk return relationship varies
across markets and a project which might be considered to provide unequal return in
different market. Financial market segmentation could arise, for example, from market
imperfections, differences in taxes or other restrictions on the ownership of securities
(e.g., Eun, 1985; Eun & Janakiramanan, 1986). Research over the past suggests that
different national markets exhibit different level of integration to international financial
markets and that the degree of integration vary over time (e.g., Hodrick, 1981; Bekaert
& Harvey, 1995; Stulz & Wasserfallen, 1995; Carrieri et al., 2002).
In last decades, empirical studies investigating financial integration have tended to
focus on developed markets (e.g., Jorion & Schwartz, 1986; Korajczyk & Viallet, 1989;
Campbell & Hamao, 1992;Bekaert & Harvey, 1995; Carrieri et al., 2002;). Recently,
more papers have focused on emerging markets, and several studies have implied
significant benefits from adding emerging markets to global portfolios (e.g., Bekaert &
Urias, 1996; Errunza & Losq, 1985, 1989; De Santis & Imrohoroglu, 1997; Bekaert et
al., 1998; Bekaert, 1999; Rockinger & Urga, 2001; Bilson et al., 2001; Gerard et al. ,
2003; Verma & Ozuna, 2005; Tai, 2007).
However, the focuses of these studies are always on countries not areas, none study
has ever worked on regions including several domestic countries. Now, the exchange
traded funds (hereafter ETFs) make that kind of diversification available. They offer, in
one trade, the benefits of diversification and index tracking at a low cost. ETFs assets
under management grew at a 132 percent average annual rate from 1995 to 2000
(Investment Company Institute, 2005). Apparently, ETFs make it easier to invest
directly in a region. We investigate whether three key emerging regions are fully
integrated into or partially segmented from each, and the world financial markets.
Our sample covers the period from January 2007 to December 2008. We use daily
percentage returns on stock indices for three emerging markets, Emerging Asia,
Emerging East Europe, Emerging Latin America, one developed market, USA, and
world portfolio returns. There are two reasons for choosing the returns on stock indices:
first, we could observe the relationships of areas instead of countries; second, the effect
of exchange rate could be deleted.
The emerging markets returns as well as the world portfolio returns series are from
Morgan Stanley Capital International (hereafter MSCI). MSCI Emerging Asia Index,
MSCI Emerging Eastern Europe Index, and MSCI Latin America Index are the indices
of the three emerging areas respectively. Each index is a weighted average portfolio of
some notable countries of that area. Table 1 reports the countries and the relevant
Table 1 Countries and Weights of the Stock Indices
MSCI Emerging Asia Index MSCI Latin America Index MSCI Emerging Eastern Europe Index
CHINA (28.34%) ARGENTINA (2.27%) CZECH REPUBLIC (7.82%)
INDIA (13.17%) BRAZIL (63.92%) HUNGARY (6.89%)
INDONESIA (3.22%) CHILE (5.75%) POLAND (15.49%)
KOREA (25.91%) COLOMBIA (2.36%) RUSSIA (69.80%)
MALAYSIA (5.05%) MEXICO (23.02%)
PAKISTAN (0.27%) PERU (2.68%)
Source: the website of MSCI, 09/30/2008
Note that our proxy for world equity market portfolio is the MSCI world index
(hereafter WI), which is a value-weighted portfolio of 20 developed markets, and hence
does not include the three emerging markets in our sample. And the USA market index
data is S&P 500 index.
For the five indices, we calculate the percentage return respectively, and name the
percentage returns of MSCI Emerging Asia Index as EMA, the ones of MSCI Emerging
Eastern Europe Index as EMEE, the ones of MSCI Latin America Index as EMLA. WI
is for the percentage returns of MSCI world index, and S&P 500 is represented as the
rates of return of USA market. During the study period, the percentage returns are
illustrated following figures.
Fig.1 Time data of EMA Fig.2 Time data of EMEE
Fig.3 Time data of EMLA Fig.4 Time data of WI
Fig.5 Time data of S&P 500
4. METHODOLOGY AND EMPIRICAL RESULTS
A large proportion of the empirical literature concerning stock market dynamics
which employs times series techniques can be broadly classified into two groups. One
group follows the work initiated by Kasa (1992) which uses multivariate cointegration
techniques to examine the number of common stochastic trends in a system of national
stock market prices. Relevant studies include Chung and Liu (1994) and Corhay et al.
(1995) on Pacific-Rim country stock markets, Blackman et al. (1994) on 17 OECD
markets, Jeon and von Furstenberg (1990) and Kwan et al. (1995) on major world
equity markets. The second group has attempted to investigate lead–lag relationships
among prices of national stock markets (Eun & Shim, 1989; Cheung & Mak, 1992;
Malliaris & Urrutia, 1992; Arshanapalli & Doukas, 1993; Smithi et al., 1993; Brocato,
We estimate a vector error-correction model (VECM) to test the relationship among
the percentage returns of stock indices of Emerging Asia (EMA), Emerging East
Europe (EMEE), Emerging Latin America (EMLA), USA (S&P500), and World
index(WI). Since the use of VECM is contingent upon stationary conditions of the data
set, a brief discussion of unit root test and cointegration test is in order.
4.1 Unit root tests
A series is considered stationary if its mean, variance and covariances are time-
independent. Augmented Dickey-Fuller (ADF, 1981) tests are used to test for the
presence of unit root in the series. The ADF method was applied to assess the existence
of unit roots and identify the order of integration for each variable. The results, as
summarized in Table 2, suggest that all series are I(0).
Table 2 Tests for Unit Root
VARIABLE ADF(t-statistic) VARIABLE ADF(t-statistic)
EMA -6.42356 *** EMLA -4.33137 ***
EMEE -5.71298 ***
S&P 500 -5.64968 ***
WI -12.99637 ***
Note: *** denotes statistical significance at the 1% level
The ADF statistics for the level of four series are significant at the 1% significance
level, implying that the null hypothesis of a unit root can be rejected. Therefore, this
study proceeded with a long-run equilibrium analysis using the cointegration technique.
4.2 Johansen Cointegration Test
Cointegration test is a test for the existence of a long run equilibrium relationship
between variables. In a bivariate case, each of the two series, xt and yt can individually
be non-stationary, say I(1), but a linear combination of the two, say, zt = xt-σyt, can
either be non-stationary, I(1), or stationary, I(0). A test of cointegration involves
subjecting the residuals from the cointegrating equation to DF and ADF tests. However,
the critical values used in the cointegration tests are different from those used in the
unit root tests. The variables are said to be cointegrated if these residuals are found to
be integrated of order zero, or I(0). In general, two variables are said to be cointegrated
if both are integrated of order k, but a linear combination of the two is integrated of
order less than k. Thus, a necessary condition for cointegration is that all variables
should have the same order of integration. The two most frequently used methods to
test cointegration are the two-step Engle and Granger test (Engle and Granger, 1987)
and the Johansen test (Johansen, 1988). We employed the Johansen (1988)
methodology to test for existence of cointegration.
Table 3 presents the results of cointegration test. As shown in Table 3, the trace
statistics and maximum eigen-value statistics suggest that EMA, EMEE, EMLA, S&P
500, and WI variables cointegrated. There are some long-term relationships among the
five variables. This implies that the EMA, EMEE, EMLA, S&P 500, and WI would not
move too far away from each other. And the tests are compared against the asymptotic
critical values of the two test statistics by MacKinnon-Haug-Michelis (1999).
Table 3 Tests for Cointegration
Hypothesized Trace 0.05 Prob. 1 Max-Eigen 0.05 Prob. 1
No. of CE(s) Statistic Critical Statistic Critical
111.745 60.061 0.0000 0.0106
None 35.556 30.440
76.189 40.175 0.0000 0.0040
At most 1 31.696 24.159
44.493 24.276 0.0000 0.0316
At most 2 19.105 17.797
25.387 12.321 0.0002 0.0088
At most 3 15.377 11.225
10.010 4.130 0.0018 10.010 4.130 0.0018
At most 4
1: MacKinnon-Haung-Michelis(1999) p-value
4.3 Vector Error-Correction Model (VECM)
After determining the number of cointegration vectors through the trace test and
maximal eigen-value test, this study continue to estimate the following vector error-
correction model (VECM) of the EMA, EMEE, EMLA, WI, and S&P 500. The result is
shown in Table 4. The regression equations are reported in the appendix.
There are three lagged error-correction terms, CointEq1, CointEq2, and CointEq3,
significant in this study. They indicate the speed of adjustment from a short-run
disequilibrium. In all three cases, the coefficients of the lagged error-correction terms
are quite high, suggesting that convergence to the long-run equilibrium path following a
shock is fast.
For EMA, the coefficients of the explanatory variables EMEE, EMLA, and
S&P500 are significant at the 10 percentage level, and the signs of the coefficients
reveal the causal effect on EMA is positive or negative. The explanatory variables
EMA, WI, and S&P500 are significant at the 10 percentage level on EMEE. For
EMLA, the significant explanatory variables are EMA, EMEE, and S&P500. The
explanatory variables EMA, EMLA, and S&P500 are significant on WI. And for
S&P500, the significant explanatory variables are EMA, EMEE, and WI.
Undoubtedly, S&P500 is the most important explanatory variable for the three main
emerging markets, and WI. EMA could play another important role to produce an effect
on the other two emerging markets, S&P500, and WI. Relatively, S&P 500, and EMA
seem to be influenced less by EMLA, and EMEE.
2. CointEq1= EMA(-1) - 1.539473*SP500(-1); CointEq2 = EMEE(-1) - 1.708376*SP500(-1);
CointEq3 = EMLA(-1) - 1.523868*SP500(-1)
Using a five-variable VEC model, we have investigated the nature of the relationships
of the returns of the stock index among the three emerging regions (Asia, East Europe,
and Latin America), USA and the world from January 2007 to December 2008. The
empirical results show that the indices of the three emerging markets, USA, and the
world tend to be in equilibrium in the long term but not so in the short term. Then with
VECM, the findings are that USA stock returns have the greatest positive effect on all
indices while the effect of world index returns on emerging markets is not significant;
on the contrary, the index returns of Emerging Asia have negative effect on almost all
index returns and th lag-time 2-3 weeks.
The conclusion of this research: 1. The three emerging markets tends to be
convergent in the long term; 2.USA stock price still influences the three emerging
markets greatly; 3.The impact of world factor on the three emerging markets is not
significant; 4. Emerging Asia market is on the rise.
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