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This paper aims to study the daily returns and volatility spillover effects in common stock prices between China and four countries in Southeast Asia (Vietnam, Thailand, Singapore and Malaysia).

The analysis uses a vector autoregression with a bivariate GARCH-BEKK model to capture return linkage and volatility transmission spanning the period including the pre- and post-2008 Global Financial Crisis.

The main empirical result is that the volatility of the Chinese market has had a significant impact on the other markets in the data sample. For the stock return, linkage between China and other markets seems to be remarkable during and after the Global Financial Crisis. Notably, the findings also indicate that the stock markets are more substantially integrated into the crisis.

The results have considerable implications for portfolio managers and institutional investors in the evaluation of investment and asset allocation decisions. The market participants should pay more attention to assess the worth of across linkages among the markets and their volatility transmissions. Additionally, international portfolio managers and hedgers may be better able to understand how the volatility linkage between stock markets interrelated overtime; this situation might provide them benefit in forecasting the behavior of this market by capturing the other market information.

This paper would complement the emerging body of existing literature by examining how China stock market impacts on their neighboring countries including Vietnam, Thailand, Singapore and Malaysia. Furthermore, this is the first investigation capturing return linkage and volatility spill over between China market and the four Southeast Asian markets by using bivariate VAR-GARCH-BEKK model. The authors believe that the results of this research’s empirical analysis would amplify the systematic understanding of spillover activities between China stock market and other stock markets.

The purpose of this paper is to examine the conditional correlations and spillovers of volatilities across CEE markets, namely, Hungary, Poland, the Czech Republic, Romania and Croatia, in the post-2007 financial crisis period.

The authors use five-dimensional GARCH-BEKK alongside with the CCC and DCC models.

The estimation results of the three models generally demonstrate that the correlations between these markets are particularly significant. Also, own-volatility spillovers are generally lower than cross-volatility spillovers for all markets.

These results recommend that investors should take caution when investing in the CEE equity markets as well as diversifying their portfolios so as to minimize risk.

Unlike the previous studies in this field, this paper is the first study using multivariate GARCH-BEKK alongside with CCC and DCC models. The study makes an outstanding contribution to the existing literature on spillover effects and conditional correlations in the CEE financial stock markets.

At the onset of the Global Financial Crisis in 2007–2008, majority of the analysts and policymakers have anticipated contagion from the markets volatility in the advanced economies (AEs) to the emerging markets (EMs). This chapter examines the volatility spillovers from the AEs’ equity markets (Japan, the United States and Europe) to the four key EMs, the BRIC (Brazil, Russia, India and China).

The period under study, from 2000 through mid-2014, reflects a time of varying regimes in markets volatility, including the periods of dot.com bubble, the Global Financial Crisis and the European Sovereign Debt Crisis, the Great Recession and the start of the Russian-Ukrainian geopolitical crisis. To estimate volatility cross-linkages between the AEs and BRIC markets, we use multivariate GARCH-BEKK model across a number of specifications.

We find that, the developed economies weighted return volatility did have a significant impact on volatility across all four of the BRIC economies returns. However, contrary to the consensus view, there was no evidence of volatility spillover from the individual AEs onto BRIC economies with the exception of a spillover from Europe to Brazil. The implied forward-looking expectations for markets volatility had a strong and significant spillover effect onto Brazil, Russia and China, and a weaker effect on India.

The evidence on volatility spillovers from the AEs markets to EMs puts into question the traditional view of financial and economic systems sustainability in the presence of higher orders of integration of the global monetary and financial systems. Overall, data suggest that we are witnessing less than perfect integration between BRIC economies and AEs markets to-date can offer some volatility hedging opportunities for investors.

Our chapter contributes to the growing literature on volatility spillovers from the AEs to the EMs in a number of ways. Firstly, we provide a formal analysis of the spillovers to the BRIC economies over the periods of recent crises. Secondly, we make new conclusions concerning longer-term spillovers as opposed to higher frequency volatility contagion covered by the previous literature. Thirdly, we consider a new channel for volatility contagion – the trade-weighted AEs volatility measure.

The purpose of this paper is to examine the price discovery and volatility spillovers in spot and futures prices of four currencies (namely, USD/INR, EURO/INR, GBP/INR and JPY/INR) and between futures prices of both stock exchanges namely, Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India.

The study applies cointegration test of Johansen’s along with VECM to investigate the price discovery. GARCH-BEKK model is used to examine the volatility spillover between spot and futures and between futures prices. The other two models namely, constant conditional correlation and dynamic conditional correlation are used to demonstrate the constant and time-varying correlations. In order to confirm the volatility spillover results, the study also applies test of directional spillovers suggested by Diebold and Yilmaz (2009, 2012).

The results of the study show that there is long-term equilibrium relationship between spot and futures and between futures markets. Between futures and spot prices, futures price appears to lead the spot price in the short-run. Volatility spillover results indicate that the movement of volatility spillover takes place from futures to spot in the short-run while spot to futures found in the long-run. However, the results of between futures markets exhibit the dominance of MCX-SX over NSE in terms of volatility spillovers. By and large, the findings of the study indicate the important role of futures market in price discovery as well as volatility spillovers in India’s currency market.

The results highlight the role of futures market in the information transmission process as it appears to assimilate new information quicker than spot market. Hence, policymakers in emerging markets such as India should focus on the development of necessary institutional and fiscal architecture, as well as regulatory reforms, so that the currency market trading platforms can achieve greater liquidity and efficiency.

Due to recent development of currency futures market, there is dearth of literature on this subject. With the apparent importance of currency market in recent time, this study attempts to study the efficient behavior of currency market by way of examining the price discovery and volatility spillovers between spot and futures and between futures prices of four currencies traded on two platforms. The study has strong implications for India’s stock market especially at the time when its currency is under great strain owing to the adverse impact of global financial crisis.

The purpose of this paper is to examine volatility transmissions between portfolios of cross‐listed equities and exchange rate differences and also the volatility persistence for home, foreign equities, and exchange rate differences in the UK and German markets.

A primary focus of this paper is to see if there is an impact first on the volatility persistence for foreign equities that are listed in the UK and German markets, second on the respective home portfolios of cross‐listed equities, and third on the exchange rate differences. In addition, whether there are any bilateral spillovers between the following equity portfolios: foreign cross‐listed equities, home cross‐listed equities, and also local or global exchange rate differences are investigated.

The paper finds that the volatility persistence is more prominent than error persistence from cross‐listed equities, foreign or home, and the exchange rate differences. Furthermore, the transmission mechanism indicates a bilateral integration process in some of the cases that were examined. Based on these results, it is concluded that in the UK market the foreign cross‐listings affect less the domestic equities compared to the German market.

This paper examines the interdependence of portfolios of home and foreign equities for cross‐listings that belong to the same stock exchange with two exchange rates, a local and a global one in order to provide more evidence in this area of literature.

The purpose of this paper is to study the impact of some corporate governance mechanisms on the market risk (stock price return and volatility, exchange rate) and on the exchange rate and Treasury Bill during the financial crisis. In order to better clarify the firms’ resistance to financial crises, the effect of exchange rate, Treasury Bill and the market risk are also considered.

The study uses a sample data of the SBF 120 on a panel of 99 French firms over the period between 2006 and 2015 divided into three sub-periods: the first sub-period, which covers the period between December 31, 2006 and December 31, 2009, was characterized by the outbreak of the subprime crisis. The second sub-period considers the sovereign debt crisis in Europe between December 31, 2010 and December 31, 2012. The last sub-period includes the post-crisis period (December 31, 2013 to December 31, 2015). The GARCH and BEKK models are used to capture the effect of volatility and conditional heteroskedasticity of both corporate governance and market risk.

The paper found that during the financial crisis (first sub-period, the sovereign crisis period), the high shareholders’ protection had a positive and significant impact on the stock market returns. Furthermore, the shareholders’ protection, the Treasury Bill, the institutional investors, the board’s size, had a negative and significant effect on the stock returns volatility. During the post-crisis period, the high protection and the board’s size had a negative and significant effect on the volatility of the stock returns.

This result implies that during the financial crisis, the high shareholders’ protection played a role in increases the stock market return and minimized the stock return volatility.

This study helps in improving the legal protection of investors and helps managers, shareholders and investors to evaluate their investments. This study also provides implications for policymakers and legal environment in order to evaluate the importance of the current corporate governance frameworks in place.

This result implies that the institutional investors, as the results suggest, should follow the shareholders’ protection in all the countries to make decisions about their investments since the high shareholders’ protection increases the firm’s stock returns and decreases the stock return volatility.

The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020. The authors also compare the hedging performance of in-sample and out-of-sample analyses.

For the modeling purpose, the authors combine the GARCH-BEKK model with the machine learning approach to predict the transmission of shocks between the financial markets and the oil market. The authors also examine the hedging performance in order to obtain well-diversified portfolios under both Financial Stress cases, using a One-Dimensional Convolutional Neural Network (1D-CNN) model.

According to the results, the in-sample analysis shows that investors can use oil to hedge stock markets under positive Financial Stress. In addition, the authors prove that oil hedging is ineffective in reducing market risks for bond markets. The out-of-sample results demonstrate the ability of hedging effectiveness to minimize portfolio risk during the recent pandemic in both Financial Stress cases. Interestingly, hedgers will have a more efficient hedging performance in the stock and oil market in the case of positive (negative) Financial Stress. The findings seem to be confirmed by the Diebold-Mariano test, suggesting that including the negative (positive) Financial Stress in the hedging strategy displays better out-of-sample performance than the in-sample model.

This study improves the understanding of the whole sample and positive (negative) Financial Stress estimates and forecasts of hedge effectiveness for both the out-of-sample and in-sample estimates. A portfolio strategy based on transmission shock prediction provides diversification benefits.

The purpose of this paper is to analyze the existence of volatility spillover effect in frontier markets. This study also examines whether any linkages exist among these markets or not.

Monthly data of regional frontier markets, from 2009 to 2016, are analyzed using Multivariate GARCH (BEKK and Dynamic Conditional Correlation (DCC)) models.

The result of cointegration test shows that the sample frontier markets are not linked in long run, and Granger causality test reveals that the markets under consideration do not cause each other even in the short run. BEKK test says that the effect of the arrival of shock from the own market does not last for longer, whereas shock from other markets lasts with the stronger persistence, and according to DCC test, the volatility spillover exists for all the markets.

The results of present study suggest that the frontier markets are not cointegrated in the long run as well as in the short run, which opens the doors for long-term investments in these markets in future, which may lead to decent returns. Long-term investors may draw the benefits from including the financial assets in their portfolios from these non-integrated frontier markets; nevertheless, they have to consider and implement diversification and hedging strategies during the period of financial turmoil, so as to protect themselves against economic and financial distress.

Significant work has been done on developed, developing and emerging markets but frontier markets are not explored much so far. This paper is an attempt to see the status of frontier stock markets as potential financial markets for diversification benefits.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.