No abstract

Purpose 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). Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

This paper represents an analysis of the spillover effects and time-frequency connectedness between crude oil prices and agricultural commodity markets using both the spillover index of Diebold and Yilmaz (2012) and the wavelet coherence model to evaluate whether the time-varying return spillover index exhibited the intensity and direction of transmission during the Covid-19 outbreak. Overall, the current results shed light on that in comparison with the pre-Covid-19 period, and the return spillover is more apparent during the Covid-19 crisis. However, levels of the intensity of this relationship vary through the period of research, with several intervals witnessing both negative and positive interactions. Further, our findings indicate significant heterogeneity among agriculture commodity markets in the degree of spillover to crude oil prices over time, amplifying our understanding of the economic channels through which the agriculture commodity markets are correlated. More importantly, there exist significant dependent patterns about the information spillovers across the crude oil and agriculture commodity markets might provide prominent implications for portfolio managers, investors, and government agencies.

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

No abstract

This article attempts to examine the changing nature of volatility spillovers among foreign exchange markets of select Central and Eastern European countries (CEEs-5), namely, Hungary, the Czech Republic, Croatia, Romania and Poland in the pre- and post-2007 financial crisis period. Daily data ranging from April 2000 to September 2017 are used for the purpose of analysis. In order to capture volatility transmission and its asymmetry, the multivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model is utilized to catch the effect of good and bad news. The key findings of the study provide useful insights into how information is transmitted and disseminated across CEEs-5 foreign exchange markets. In particular, the estimation presents the precise measures of return spillovers and volatility spillovers. The analysis highlights that the foreign exchange markets become more independent after crises. Similarly, in such time, the volatility spillover among the foreign exchange markets decreases dramatically and financial markets have not been transmitted during the crisis period. Also, we find that positive shocks generate more volatility spillovers than negative shocks of the same magnitude. The asymmetric spillover effect is evident for price shocks originating from CEEs-5 foreign exchange markets. Further, our findings have essential portfolio management implications for international investors and policymakers.

This study investigates the connectedness between Bitcoin prices and major stock indices in the Asia-Pacific region from February 2012 to August 2019. Based on the wavelet transform framework, we find evidence of significant unidirectional association from Bitcoin to the selected markets in the short, medium, and long-run in the Asia-Pacific region. Overall, Asia-Pacific equity markets and Bitcoin cryptocurrency are weakly correlated at higher frequencies throughout the sample period, but the dependence of Bitcoin on the equity markets steadily increases at lower frequencies. Further, we construct the wavelet-based Granger causality test at different time scales to provide additional support to our connectedness results. Our findings provide important implications for policymakers, portfolio managers, and investors who are invited to take into account the dynamic linkages between Bitcoin and equity markets.

No abstract