Volatility spillovers under difference in the degree of market integration: Evidence from the selected Asian and Eastern European stock markets

This research aims to investigate volatility transmitted from the world market to ten Asian and Eastern European stock markets and from major stock market in the region to the rest of stock markets by considering their degree of integrations. To assess this, we apply GARCH(p,q) model and involve dynamic conditional correlation (DCC) model to generate the dynamic degree of integration. The monthly market indices data over the period from May 2002 to March 2018 are taken from 11 markets -5 Asian ones (China, Indonesia, Malaysia, Pakistan, and the Philippines), 5 Eastern European (Czech Republic, Poland, Romania, Russia, and Ukraine), and the world market data. Furthermore, the volatility spillover was analysed during the global financial crisis period, from May 1, 2008 to May 29, 2009. The findings show that volatility spillovers from the world and the major regional markets to domestic stock markets are conditional on the degree of integrations. Specifically, there is no volatility spillover from the world and regional major markets on segmented Received: September, 2018 1st Revision: October, 2018 Accepted: January, 2019 DOI: 10.14254/20718330.2019/12-1/9 Journal of International Studies S ci en ti fi c P a pe rs © Foundation of International Studies, 2019 © CSR, 2019 Harjum Muharam, Wisnu Mawardi, Erman Denny Arfinto, Najmudin Volatility spillovers under difference in the degree of market integration: Evidence from... 135 stock markets. In contrast, domestic stock markets which are integrated could experience the volatility spillover. Moreover, this confirms for both crisis circumstances and the overall period.


INTRODUCTION
Prior researches have investigated the integration among stock market classes or among stock market types, for instance, between developed and developing stock markets or between conventional and Islamic stock markets (Al Nasser & Hajilee, 2016;Majdoub et al., 2016).Nevertheless, integration of stock markets towards international market has not been revealed yet.Similarly, volatility spillover as an effect of integration discussed in prior researches was analyzed only among the countries bilaterally, that is, when volatility is transmitted from a particular developed country to an emerging country (Neaime, 2012).However, the susceptible strength of volatility spillover from international market has not been disclosed yet.This paper expands both issues focusing on the causality of volatilities from the world market to domestic markets through explanation involving the market integration aspect.It refers to the international portfolio diversification framework which states that financial assets' comovement among stock markets plays its important part in volatility change.
Furthermore, the existing studies examining the presence of volatility spillover have ended up controversial findings.On the one side, some studies conclude that there are volatility spillovers at stock markets, among others, Dungey et al. (2007); Rejeb and Boughrara (2015).On the other side, another study finds no evidence of such a volatility spillover (Majdoub & Mansour, 2014).In addition, Gebka and Serwa (2007) state there is different evidence on volatility spillover among emerging stock markets of Latin America, East Asia and Eastern Europe.It is likely that the existing studies ignore the degree of integration among the observed markets so that the findings of volatility spillover have dissimilar which attempt to investigate dynamic volatilities at emerging markets by considering their integration level towards the world market.Moreover, this paper contributes on the existing literature by employing recent data and comparing it with the crisis circumstances.This paper also provides valuable information for international investors and policy makers on consequences from an integrated domestic market.It could make their decisions more efficient and effective in anticipating the events among stock markets.
Higher integration of international stock markets and correlated stock prices' volatility would weaken the international portfolio diversification (Bekaert et al., 2005).Integration of a stock market to the global market needs to be disclosed because otherwise it would limit the opportunities for investors to benefit from their portfolio diversification and reduce the chances for a number of firms to obtain lower cost of capital.Moreover, side effects from higher integration could generate additional financial disturbances and shocks at local stock markets.For instance, the global financial crisis overspreads and suppresses the emerging stock markets and leads to rapid decline in prices (Neaime, 2012).

LITERATURE REVIEW
There is a wide variety of literature on stock market integration and volatility across markets.Some studies have discussed only returns spillover, while some other studies have looked at both the first and the second moments of equity prices to discuss the cross-border spillover.We investigate, as the second category of the studies, the volatility spillover from international market and the major stock markets regionally to emerging stock markets by considering their dynamic integrations.The literature provides diverse definitions of financial integration.According to the law of one price, Chen and Knez (1995) define integrated markets as markets where investors can, in one country, buy and sell without restriction equities that are issued in another country and as a result, identical securities are issued and traded at the same price across markets after adjustment for foreign exchange rates.
Stock market integration is the situation when the markets have higher and stable relationship due to their stocks prices move together in similar direction for similar period.It could be defined as a unification of a number of separate stock markets operationally in the mechanisms, activities, characteristics of the instruments and interactions of the participants.The markets in which the assets require the same expected returns regardless of the trading locations are said to be integrated.While the markets where the expected returns of an asset depends on its location are said to be segmented (Arouri et al., 2012;Bekaert & Harvey, 2003).
Attention to stock market integration arises mainly because the financial theory states that integrated stock markets will be more efficient than segmented stock markets.When the stock market was integrated, investors from all countries will be able to allocate their capital to the most productive locations.With more flow of cross-border funds, additional trade in any securities may increase the liquidity of stock market.In addition, it could make the cost of capital to fall on companies that are looking for capital and make the transaction costs incurred by investors to be lower.It indicates a more efficient capital allocation (Click & Plummer, 2005).
Financial markets in most developed countries have grown rapidly over the past decade due to various factors such as deregulation, globalization and advances in information technology.There are no restrictions such as regulatory restrictions, transaction costs, taxes, and tariffs on foreign asset trading or portfolio equity flow mobility.The integration of financial markets around the world also appears to grow among them (Marashdeh dan Shrestha, 2010).In recent years, most of studies found that stock markets observed had higher integration level, for instance between Germany and emerging markets (Al Nasser & Hajilee, 2016) and among Malaysia, Indonesia, and Turkey stock markets (Arshad, 2017).Employing international CAPM method, Najmudin et al. (2017) find that there is higher integration on the UK, Japan, Malaysia, Thailand, Indonesia, and Singapore stock markets.
Returns volatility in economics and finance field reflects the degree of variation for the returns of a financial asset such as stock, market index, or exchange rate.The standard deviation and variance of returns are the most common measures of returns volatility.The standard deviation is used in studies which assume that volatility is constant time-series, whereas dynamic conditional variance or residual is used in studies which assume that volatility varies over time.Financial assets that have higher volatility indicate that the assets have higher risk (Kočenda, 2017).Economic and especially financial time series are prone to exhibit periods of high and low volatility.Therefore, it is often misleading to measure volatility by a static standard deviation or unconditional variance.However, exactly such pattern can be modelled using conditional heteroskedastic disturbances.The solution to this problem can be found in the conditional heteroskedasticity models.
The studies on volatility in many stock markets had grown by expanding the issue of how volatility of return in a stock market is contagious and affects the volatility of return in another stock market, also known as volatility spillover.In other words, volatility spillover is a change in volatility of returns in one market because of the transmission of market-specific information from other markets.Cross market linkages in the conditional second moments of stock return is another important topic of international financial relations.In addition to various domestic and global factors, returns volatility of major stock market is one of the important factors of stock returns volatility in a stock market (Mukherjee & Mishra, 2010).
Volatility spillover has been examined by Ng (2000) who investigates the magnitude and changing characteristics from the US and Japan.The evidence suggests that the significant factors of market volatility are regional and international variables.Similarly, Dungey et al. (2007) report developed market has important role in transmitting volatility to emerging market and there is volatility spillover among regions.Furthermore, Rejeb and Boughrara (2015) conclude that there is a volatility transmission across financial markets; geographical proximity is essential factor in enlarging volatility transmission; and the liberalization contributes significantly in enlarging international volatility transmission.Applying GARCH model on India, China, Hong Kong, Indonesia, Japan, Korea, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, and Thailand stock markets, Mukherjee and Mishra (2010) suggest that return spillovers between India and its Asian counterparts are found to be positively significant and bidirectional.Contemporaneous spillover of intraday volatility is stronger from other foreign markets to India.However, transmission of information lagged by one day is not found to be stronger.

METHODOLOGY
The data are obtained from the websites of stooq.com,msci.com,yahoo.finance.com,and the other relevant publications.The first data set covers stock market indices of China, Indonesia, Malaysia, Pakistan, Philippines, Czech Republic, Poland, Romania, Russia, Ukraine, and world markets.MSCI ACWI is used as a proxy for world market index.All data have the same time period from May 2002 to March 2018 on monthly basis.The second data set covers on daily basis during the global financial crisis during period from May 1, 2008to May 29, 2009.The data which consist of five Asian, five Eastern Europe, and world market indices are used to calculate the returns on each market and then used to find the dynamic conditional correlation (DCC) of returns among world market and the ten stock markets, and among a dominant stock market and the four rests in the region.The return of time t for the sample of stock market index i (Ri,t) is the difference between the natural logarithm of the index price at the current time (Pi,t) and the natural logarithm of the index price at previous time (ln Pi,t-1).The formula is expressed as follows Ri,t = ln Pi,t -ln Pi,t-1.
The objectives of this research are specifically as follows.The first objective is to analyze the strength of a stock market as recipient against the volatility spillover from international and regional markets as senders.The second is to analyze the dynamic integration of each stock market in both Asian and Eastern European markets toward international and regional markets.The third is to analyze the existence of volatility spillover involving its explanation with the dynamic degree of integration.
To achieve the first objective we adopt the framework of Balli et al. (2015) as well as Mukherjee and Mishra (2010); Ng (2000); Bekaert and Harvey (1997) in working the volatility spillover models for the equity returns from the originator world market to the ten recipient stock markets.The effects of volatility spillover from major stock markets regionally, China in Asian markets and Russia in Eastern Europe markets, to the rest four stock markets are also taken into consideration to formulate their respective univariate AR-GARCH-M(p,q) models.
The volatility of stock return series is time varying so that this paper examines the spillover of the conditional second moments across markets allowing for changing the variances.The generalized autoregressive conditional heteroscedasticity (GARCH) model proposed by Engle (1982) and developed by Bollerslev (1986) has been employed to account for the time-variant conditional variances (Mukherjee & Mishra, 2010).The mean and variance equations of ARCH(p) and GARCH(p,q) models respectively are generally expressed as follow: Variance equations: Where Yt is the individual returns at time t, c is a specific mean, εt is the error term, It denotes the information available at time t and   2 is the conditional variance of the error term at time t and a function of both  −1 2 (the squared error term in the previous time) and  −1 2 (conditional variance in the previous time).
Our empirical approach to achieve the first objective comprises the following steps.The first step, we estimate the volatility of world market and major stock markets in each region as the senders, namely China in Asian markets and Russia in Eastern Europe markets.To obtain the returns volatility for each world, China, and Russia market, respectively, as determinants of the rest eight stock markets volatilities, we perform volatility modeling steps by following the AR-GARCH(1,1) model.The mean equations of AR-GARCH(1,1) model for the three markets are expressed as follow: Where RWI,t, RCN,t, and RRS,t are market returns of world market, China, and Russia stock markets at time t, respectively; and ε t is error term at time t.
The second step, we estimate how the returns volatilities of the three sender markets are contagious and affect the returns volatility in another stock market as recipient.In order to investigate this volatility spillovers, we apply AR-GARCH-M(p,q) model.Unlike in simple GARCH model, the GARCH-M or GARCH-in-Mean model includes the conditional variance or its square root in the conditional mean equation along with other explanatory variables.Conditional variances or GARCH variance series resulted from estimations of AR-GARCH(1,1) model, as in Eqs. ( 4) -( 6), are then used to estimate volatility series as inputs for AR-GARCH-M(p,q) model.The model is estimated using the maximum likelihood procedure applying the Berndt-Hall-Hall-Hausman (BHHH) algorithm.
The first equation, called as mean equation, of AR-GARCH-M(p,q) model for the recipient domestic stock market i is expressed as follows: The second equation, called as variance equation, is expressed as follows: Where Ri,t is returns of recipient domestic stock market i at time t; σi,t is the square root of conditional variance on stock market i at time t; εt is error term at time t;  , 2 is the conditional variance of the error term at time t;  − 2 is the squared error term at time t-p;  − 2 is conditional variance at time t-q; and Vj,t is volatilities of sender market j at time t.
To achieve the second objective we apply the DCC (dynamic conditional correlation) approach as developed by Engle (2002) and worked by Majdoub and Mansour (2014).We estimate the conditional relationship of returns among world market and ten selected stock markets.The principal advantage of this model is that while it retains the main features of standard GARCH models, it allows us to model explicitly time variation in the conditional covariance and correlation matrix.
DCC model can be described briefly as follows.In the DCC-GARCH(1,1) model, the conditional variance-covariance matrix is defined by Ht = DtRtDt, where Ht takes the following formulation: Dt is a (n x n) diagonal matrix of time-varying standard deviations from univariate GARCH models with (hii,t) 1/2 on the ith diagonal, i = 1, 2, …, n; Rt is the (n x n) time-varying correlation matrix and Rt is conditional correlation matrix: The evolution of the correlation in DCC model is given by: Where Ǭ is the unconditional correlation matrix of the epsilons; Qt = (qii,t) is the (n x n) time-varying covariance matrix of εt; α and β are non-negative scalar parameters satisfying (α + β) < 1.
In the empirical methodology, Arouri and Nguyen (2010) convey that conditional correlation coefficient ρij between two markets i and j at time t is then expressed by the following equation: Where qij refers to the element located in the ith row and jth column of the matrix Qt.
DCC-GARCH model as described above is estimated using a two-stage procedure.In the first stage, a univariate GARCH(1,1) model is estimated for each return series included in the multivariate system.During the second stage, the transformed residuals from the first stage, namely the estimated residuals standardized by their conditional standard deviations, are used to infer the conditional correlation estimators.
The Log likelihood for this estimator can be expressed as: To achieve the third objective we relate the patterns of volatility spillover across markets to the patterns of the degree of integration among those markets.This analysis could confirm the statement that a stock market which has higher comovement with the other stock markets would automatically become more responsive to the volatility of those stock markets.Therefore, in order to understand the patterns of volatility spillover across markets, it is necessary to assess the level and the nature of integration among those markets (Balli et al., 2015).

EMPIRICAL RESULTS AND DISCUSSION
We examine volatility spillover accros stock markets and the degree of markets integration by employing the data of market indices during period from May 2002 to March 2018 monthly totaling 191 observations and during sub-period from May 1, 2008 to May 29, 2009 on daily basis.Table 1 exhibits descriptive statistics for ten observed market returns, namely China (CN), Indonesia (ID), Malaysia (MY), Pakistan (PK), Philippines (PH), Czech Republic (CZ), Poland (PL), Romania (RM), Russia (RS), and Ukraine (UR).It consists of mean, deviation standard, maximum, and minimum values for overall and global financial crisis (GFC) sample periods.
For all sample period, Pakistan is the stock market which provides the highest average returns amount to 1.58 percent.This interesting value, however, was accompanied by the higher risk measured by the standard deviation of returns (7.15) and the spread of returns (65.11 percent) ranging from maximum value (20.23 percent) to minimum value (-44.88 percent).In contrast, China has the lowest average returns (0.39 percent) followed by Malaysia stock market (0.48 percent) and yet investors in China stock market bear the highest risk in Asian region with standard deviation amount of 8.08.Min.
In Eastern European region, Czech Republic stock market has the lowest average returns (0.48 percent) and has lower risk indicated by the standard deviation and spread of returns in this market amount of 5.97 and 48.75 percent, respectively.Conversely, Ukraine stock market has the highest average returns in the region followed by the highest risk.This information was presented by the average returns, standard deviation, and spread of returns for this market which are 1.26 percent, 11.63, and 79.77 percent, respectively.
The lowest risk in Asian region appears in Malaysia stock market with standard deviation and spread of returns are 3.58 and 29.21 percent, respectively.Similar position is found on Poland stock market in Eastern European region with standard deviation and spread of returns are 5.91 and 46.29 percent, respectively.Moreover, Malaysia stock market is the only one stock market that has the lower risk than the world market returns.Standard deviation and spread of returns for world market are 4.50 and 33.07 percent, respectively.
For the GFC period, Russia stock market exhibits the highest standard deviation and spread values in both regions amount of 4.43 and 41.40 percent, respectively.This phenomenon for the GFC period on Russia stock market was dissimilar with condition for all sample period which the highest risk was found on Ukraine stock market.In Asian region, such phenomenon appears on China stock market that has the highest standard deviation for the GFC period (2.50) and for all sample period (8.08) in the region.In general, the data of all stock markets inform that each stock market has a difference characteristic or heterogeneous in rate of returns and its risk.
We consider the stationarity pattern of data to analyze furthermore all variables and to draw an inference from statistical ways.To test the stationarity, we apply one of unit root methods, namely ADF (Augmented Dickey-Fuller) Test.According to unit root test, the result shows that stationer patterns in the level form appear on all observed market returns data.This conclusion prevails on the data for overall sample period (monthly) and for sub-sampel period of global financial crisis (daily).Therefore, it is not necessary to transform or differentiate the data of those eleven markets returns.
The variance equation of the AR-GARCH-M(p,q) model for this research is written in general as follows: The variance equation above becomes operational guidelines to interpret generally the volatililty transmission from one market to the volatility of another market.Table 2 contains the results of ten estimate models for each recipient stock market.These ten estimate models are the best fit regression models which are selected through iteration process from various models, such as ARCH(p,q), GARCH(p,q), ARCH-M(p,q), and GARCH-M(p,q).
The model specifications in variance equation using overall sample period for each ten recipient stock market are expressed as follow: The model specifications in variance equation above, as presented in Table 2, inform that conditional variance of world market (V_WI) has positive effect on conditional variances of China, Indonesia, Malaysia, Philippines, Czech Republic, Poland, Romania, Russia, and Ukraine stock markets.It is indicated by the significant coefficients of V_WI statistically amount of 0.999, 0.791, 0.216, 0.417, 1.176, 0.622, 1.815, 2.838, and 2.642, respectively.Conversely, conditional variance of world market has no effect on conditional variance of Pakistan stock market which is indicated by the insignificant coefficient of V_WI statistically amount of 0.680.These results suggest that there are volatility spillovers for all sample period from world market to nine observed stock markets and there is no volatility spillover on Pakistan stock market.+ Σδn Vj,t.In addition, V_CN, D(V_RS), and V_WI in variance equation stand for returns volatility of China, Russia, and world markets, respectively.The volatility of Russia stock market partially was performed in transformation form, i.e., in first difference form D(V_RS), due to multicollinearity problem with volatility of the world market index (V_WI).The asterisks (***, **, *) indicate that p-value is significant respectively at the 1%, 5%, 10% level.
Regionally, the results of estimate on Asian stock markets inform that conditional variance of China stock market (V_CN) has positive effect on conditional variance of Malaysia stock market.It is indicated by the significant coefficient of V_CN amount of 0.052 at the 5% level.In contrast, conditional variance of China has no effect on conditional variances of Indonesia, Pakistan, and Philippines stock markets.It is indicated by the insignificant coefficients of V_CN amount of 0.132, 0.271, and 0.095, respectively.These evidences suggest that the volatility spillover in Asian region from China stock market only occurs on Malaysia stock market.
In Eastern Europe, conditional variance, in first difference form, of Russia stock market D(V_RS) has significantly positive effect on conditional variances of Czech Republic, Poland, and Romania stock markets.It is indicated by the significant coefficients of D(V_RS) amount of 0.345, 0.281, and 0.755 at the 1% level, respectively.Conversely, conditional variance of Russia stock market has no effect on conditional variance of Ukraine stock market which is indicated by the insignificant coefficient of D(V_RS) amount of 0.829.These results inform that there are volatility spillovers from Russia as a major stock market to all stock markets observed in Eastern Europe region, except to Ukraine stock market.This table contains the results of estimate regressions using AR-GARCH-M(p,q) model for each stock market for global financial crisis period.
The model specifications in variance equation using the GFC sample period for each ten recipient stock market, as presented in Table 3, are expressed as follow: Volatility spillover is the causality in variance among markets (BenSaïda et al., 2018).The results from causality analyses of volatilities using overall sample period are not distantly different with the results using the GFC sample period.The differences are as follow: volatility of world market has no effect on volatilities of China and Philippines stock markets; volatility of China stock market has no effect on volatility of Malaysia stock market; and volatility of Russia stock market has positive effect on volatility of Ukraine stock market.The findings of this paper on the existence of volatility spillover are consistent with studies of Abbas et al. (2013); Balli et al. (2015); Rejeb and Boughrara (2015).For example, Balli et al. (2015) found that there is significant spillover effects from developed stock markets to emerging markets.
This empirical study on volatility spillover from the global market to a stock market has an important role from the particular perspective of portfolio diversification and hedging strategies (Majdoub & Mansour, 2014).Moreover, studying spillover volatility has direct implication in designing optimal portfolios and building policies to prevent harmful shock transmission and to limit the propagation of financial crises across borders (BenSaïda et al., 2018).Therefore, understanding the volatility across markets is crucial for risk managers, hedgers, and policy makers, especially volatility spillover due to the financial crisis.
Table 4 presents pairwaise dynamic conditional correlation (DCC) among market indices returns in average values.More specific, it was divided into two part sub-sample periods: overall sample period in Panel A and global financial crisis sample period in Panel B. Furthermore, Table 4 Panel A exhibits eighteen average series of stock market pairs monthly among the world market and ten stock markets in Asian and Eastern Europe regions.
The pairs of R_CN vs R_PK and R_WI vs R_PK, as presented in Panel A, appear the lowest average dynamic correlation amount to -0.02 and 0.02, respectively.They are followed by the pairs of R_CN-R_ID and R_CN-R_PH amount to 0.22 and 0.23, respectively.This information suggests that Pakistan stock market has the lowest degree of integration in observed markets pairs with world market and major markets in its region.In additon, the pairs of world market with all markets in Eastern Europe have strong average dynamic correlation from 0.43 with Ukraine to 0.65 with Czech Republic and Poland stock markets, respectively.This evidence indicates that the degree of integrations among world market and five stock markets in Eastern Europe region in a whole are higher.
In Asian region, all pairs of China and the four rests markets have weak average correlations from -0.02 with Pakistan to 0.31 with Malaysia.In Eastern Europe region, the pairs of Russia with the four rests markets have strong average correlations from 0.46 with Ukraine to 0.54 with Poland stock market.This fact informs that China has lower degree of integration with entire stock market in Asian region and Russia has higher degree of integration with entire stock markets in Eastern Europe region.The results generally do not support the conclusion of Naranjo and Porter (2007) which state that returns in emerging markets appear very low correlation with returns in developed markets.Moreover, it was partly similar to conclusion of Lean and Smyth (2014) which report that relationship among the major markets and between major market and emerging market have increased over time.In addition, Arshad (2017) and Najmudin et al. (2017) conclude that Malaysia and Indonesia are classified as integrated stock markets.Table 4 Panel B, which contains observations during GFC period, provides confirmation against previous information interpreted from Panel A. It differs to observations for overall sample period in average dynamic correlations only for pairs of R_WI vs R_CN and R_WI vs R_PH.The values of average dynamic correlations between world market and China market returns and between world market and Philippines market returns in the later sample observations are 0.19 and 0.23, respectively.These values are lower than the values of average dynamic correlations for overall sample period observations amount to 0.37 and 0.50, respectively.Therefore, the interpretation of the data at Panel B has much similarity with the interpretation from Panel A. In general, the result informs that there is opportunity for international investors to diversify internationally their fund by involving the stocks from China and Pakistan stock markets into their portfolio formation.
This research shows that stocks in the Asian market region have varied characteristics and are not identical as a whole with the world market stock prices that do not move in the same direction.By contrast, stocks in the Eastern Europe market region have the same expected returns which the investors could trade at any location in this region.The empirical evidence leads to an economic highlight that a segmented stock market could be stronger from the propagation of external volatility such as Pakistan for both the sample periods as well as China and Philippines for the GFC sample period.Conversely, all stock markets in Eastern Europe have a higher level of integration with the world market.Such markets are susceptible to be contaminated by the returns volatility from world market as a result of trade transactions by international investors.This fact was proven in this research which shows that returns volatilities of all stock markets in Eastern Europe are influenced by returns volatility of world market.
The volatility transmission from one stock market to other stock markets found in the investigation of this research has a pattern that is almost similar to the pattern occurring at the level of integration among those stock markets.The returns volatility of world market affects returns volatilities of all observed stock markets, except for the volatility of Pakistan stock market.Similarly, world market also has a higher degree of integration with all observed stock markets, except with Pakistan stock market.These patterns indicate that the volatility from world market would be sent under condition that the level of integration with its recipient stock market is higher.
For integrated domestic markets we interpret that the lower the returns volatility in world market, the lower the returns volatilities in domestic markets and vice versa.Higher integration of a domestic stock market towards the world could accelerate the transmission of volatility.In segmented domestic markets, when returns volatility of world market changes, the returns volatilities of domestic markets would not be affected.Therefore, international investors should distribute their funds also on the stocks in segmented stock markets such as Pakistan that was not affected by the spillover volatility.This decision was taken to compensate for external risks originating from the world market to achieve minimum portfolio risk.
In addition, China stock market as a dominant market in its region does not send its volatility to Indonesia, Pakistan, and Philippines markets.Similar pattern suggests that China market also has a lower degree of integration with these three markets.This evidence indicates that volatility transmission from China would not happen by the condition of lower degree of integration.Exception in the Asian region appears in Malaysia stock market.The lower returns relationship between Malaysia and China market was not reflected in volatility on Malaysia which is significantly affected by the volatility of China market.
Furthermore, the returns volatility of Russia stock market as a dominant stock market in Eastern Europe affects the volatilities of Czech Republic, Romania, and Poland stock markets.In the same pattern, Russia stock market also has a higher degree of integration with these three stock markets.These two corresponding proofs indicate that volatility transmission from Russia would happen on condition that the level of integration with each of the three stock markets is higher.As an exception, Ukraine stock market is not in accordance with these general patterns.For the entire sample period, although Ukraine stock market has a higher level of integration with Russia market, this stock market was not affected by changes in returns volatility of the major market.
The general patterns of spillover volatility and degree of market integration as interpreted in the world level are not distantly different from general patterns in the regional level.However, it does not cover Malaysia and Ukraine stock markets against major stock market in their regions for all sample periods.The results from both regions indicate that change in the degree of integration with major stock market was not reflected in the spread of volatility in Ukraine and Malaysia stock markets, but it was more due to the degree of integration with world market.
According to the results of volatility spillover and market integration that have been examined, it can be argued that the volatility of stock market affected by the volatility of other stock market occurs when both stock markets have a higher degree of integration.In short, the recipient of volatility is integrated with the sender.In contrast, the volatility of a domestic stock market which is segmented toward world or regional market would not change.These empirical evidences corroborate the conseptual framework, for instance, from Rejeb and Arfaoui (2016) who argue that in the last decade, a number of studies have focused on analyzing the transmission of volatility among emerging markets with respect to the degree of financial integration after their liberalization process.Their statement confirms the opinion of Phylaktis and Ravazzolo (2002) that financial liberalization makes financial markets more integrated into global financial movements and thus more sensitive to external shocks.The propagation of volatility is the consequence of financial interdependence across markets.
All information and investor activity including returns volatility from world market would be delivered to integrated domestic markets.When the domestic markets have higher level of integration, the change in returns volatility on world market would be followed by change in returns volatility on these markets.This evidence supports the theoretical framework of volatility spillover and contagion risk hypothesis.It states that volatility spillover and contagion risk could occur among stock markets which have interrelation each other.Alotaibi and Mishra (2015) confirm that as the progress of emerging markets to become increasingly integrated with global market, their response to the volatility spillovers of stock markets increases, their portfolio diversification ability decreases and they become more vulnerable to external shocks.
International investors who trade their stocks in several stock markets in the world would pay more attention on information and development of world market.When the volatility occurs on the world market, they would respond to this information reflected by change in volatility of domestic stock market.Jebran et al. (2017) state that the evidence of financial interdependence indicates that the financial shocks in one market will spill over to other market.Moreover, Gencer and Hurata (2017) find that volatilities among markets are significantly transmitted in varying magnitudes and signs.Observing the patterns of volatility spillover among different stock markets leads for policymakers to make accurate decision and effective intervention at times of instable market and financial system.In addition, investigating the integration among markets returns is of paramount importance in designing portfolio diversification and hedging decisions.Thus, analyzing in-depth the spillover and integration different markets is eminent for all market participants.

CONCLUSION
We investigate volatility transmissions from world market to the ten stock markets in Asian and Eastern Europe regions, and from major stock market of both regions to the four rests stock markets.For overall sample period, the results suggest that volatility spillover from world market as a sender generally occurs on the whole stock markets, except to Pakistan; in Asian region, from China only to Malaysia stock market; and in Eastern Europe region, from Russia to Czech Republic, Poland, and Romania stock markets.These results differ from the findings during the global financial crisis which suggest that returns volatility from world market spreads on the seven stock markets, except to China, Pakistan, and Philippines; in Asian region, there is no volatility spillover from China; conversely, there is volatility spillover from Russia to the four rests markets in its region.
Stock markets that receive external volatility and were exposed against volatility transmissions from other stock markets reflect that investors in these stock markets face uncertainties in returns and higher risks in their securities.Such stock markets have stocks whose price movements are difficult for investors to predict so that they should redesign their portfolio formation with a larger number of stocks and longer analysis time and they could be inconvenient for this situation.In addition, such stocks could result in increased waiting time for transactions so that could reduce the trading liquidity.
Analysis of the volatility transmission was accompanied by observing its degree of integration.The findings on the degree of integrations among world market and ten selected stock markets show that world market has very low degree of integration only with Pakistan stock market; China has lower degree of integration with the four rests markets in Asian region; and Russia has higher degree of integration with entire stock markets in Eastern Europe region.In addition, for the global financial crisis period, world market has lower degree of integration with China, Pakistan, and Philippines stock markets; China has lower degree of integration with entire stock markets in Asian region; and Russia has higher degree of integration with entire stock markets in Eastern Europe region.From this finding, including the stocks from Pakistan stock market is the better design in international portfolio diversification to minimize the portfolio risk.
When the existence of volatility spillover is involved to its degree of integration, the findings suggest that in general there is synchronous pattern on both aspects.We have notion that volatility spillovers are conditional on their degree of integrations.Specifically, domestic stock markets which have higher (lower) degree of integration would (not) receive volatility spillover from world market and major stock markets in their region.This phenomenon happened not only for overall period but also during financial crisis period.Stock market which is more integrated toward international financial movements would be more sensitive against external shock.As the consequence, the volatility from the international market will be easier to transmit to the integrated stock market.
The finding indicates that volatility of financial asset which is integrated across borders could potentially be a source of vulnerability for financial asset in national stock market.Analysis to generate this finding was very simple that only linking the patterns of volatility spillover to the patterns of dynamic degree of integration among markets.For future research, it would be better to expand this issue by utilizing the various causality methods that examine the effect of market integration on volatility spillover.To apply such methods, however, the research should to create a measure for volatility spillover which acts as a dependent variable.Moreover, the challenges for future research are to explore the other factors influencing potentially on volatility spillover and to investigate the consequence that could emerge from the volatility spillover among stock markets.
The implication for decision arising from the findings is that as emerging stock markets become more integrated with world market and major stock market regionally, the market participants should strengthen prudential regulations and actions to prevent harmful shock spillover and to limit the propagation of financial crises across borders.Moreover, according to the findings, risk managers, decision makers, and hedgers should redesign their optimal portfolios and rebuild their policies to prevent rising risks of financial transmission.

Table 1
Descriptive statistics of market indices returns

Table 2
Estimates of GARCH-M(p,q) model for overall period

Table 4
Average dynamic correlations among market indices returnsThis table reports pairwaise cross-market returns correlation.R_CN, R_ID, R_MY, R_PK, and R_PH stand for indices returns of China, Indonesia, Malaysia, Pakistan, and Philippines stock markets, respectively.R_CZ, R_PL, R_RM, R_RS, and R_UR stand for indices returns of Czech Republic, Poland, Romania, Russia, and Ukraine stock markets, respectively.R_WI is world market returns of MSCI AC World Index.