This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The margin...This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.展开更多
This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,K...This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,Kuwait,Qatar,Saudi Arabia,and the United Arab Emirates,under different financial and economic circumstances.The empiri-cal investigation is conducted using daily data from June 1,2005 to July 1,2019.The analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies,asymmetries in conditional variances,non-linearity,and multiple seasonality or time-varying correlations.Our study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and volatility.The in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market returns.Moreover,the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations.A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important,asymmetric,and differ in their volatility responses to unexpected shocks.Our evidence suggests that these markets are not completely efficient in processing regional news,thus providing a sound alternative for regional portfolio diversification.展开更多
This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)pandemic.We propose a novel method for calculating stock pri...This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)pandemic.We propose a novel method for calculating stock price index correlations based on open-high-low-close(OHLC)data.More intraday information can be utilized compared with the widely used return-based method.Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial threshold.The topologies of the global stock market complex network constructed using 70 important global stock price indices before(2017-2019)and after(2020-2022)the COVID-19 outbreak were examined.The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network.The global stock market co-movement characteristics are revealed,and the financial centers of the developed,emerging,and frontier markets are identified.Using centrality indicators,we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic.Based on these findings,we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.展开更多
文摘This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.
文摘This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,Kuwait,Qatar,Saudi Arabia,and the United Arab Emirates,under different financial and economic circumstances.The empiri-cal investigation is conducted using daily data from June 1,2005 to July 1,2019.The analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies,asymmetries in conditional variances,non-linearity,and multiple seasonality or time-varying correlations.Our study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and volatility.The in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market returns.Moreover,the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations.A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important,asymmetric,and differ in their volatility responses to unexpected shocks.Our evidence suggests that these markets are not completely efficient in processing regional news,thus providing a sound alternative for regional portfolio diversification.
基金the financial support from the Beijing Municipal Social Science Foundation(No.20GLC054)the National Natural Science Foundation of China(Nos.72021001,72174020,71904009)+1 种基金the Natural Science Foundation of Beijing Municipality(No.9232014)the Humanities and Social Science Fund of Ministry of Education of China(No.18YJC840041).
文摘This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)pandemic.We propose a novel method for calculating stock price index correlations based on open-high-low-close(OHLC)data.More intraday information can be utilized compared with the widely used return-based method.Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial threshold.The topologies of the global stock market complex network constructed using 70 important global stock price indices before(2017-2019)and after(2020-2022)the COVID-19 outbreak were examined.The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network.The global stock market co-movement characteristics are revealed,and the financial centers of the developed,emerging,and frontier markets are identified.Using centrality indicators,we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic.Based on these findings,we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.
