Dynamic Hedging Based on Markov Regime-Switching Dynamic Correlation Multivariate Stochastic Volatility Model

It is important to consider the changing states in hedging.The Markov regime-switching dynamic correlation multivariate stochastic volatility( MRS-DC-MSV) model was proposed to solve this issue. DC-MSV model and MRS-DC-MSV model were used to calculate the time-varying hedging ratios and compare the hedging performance. The Markov chain Monte Carlo( MCMC) method was used to estimate the parameters. The results showed that,there were obviously two economic states in Chinese financial market. Two models all did well in hedging,but the performance of MRS-DCMSV model was better. It could reduce risk by nearly 90%. Thus,in the hedging period,changing states is a factor that cannot be neglected.

Financial Integration Among the ASEAN 5 ＋ 3 Stock Markets： A Preliminary Look at the First 10 Years of the New Millenium

The purpose of this study is to investigate the financial integration of the stock markets of the ASEAN 5 ＋ 3 countries. These countries include Indonesia, Malaysia, Philippines, Singapore, Thailand, China, Japan, and South Korea. The research determined the stock return volatility for each country＇s index during the first decade of the new millennium. The findings showed that there is the presence of integration and co-integration with Philippine index＇s return with the index＇s returns of the following countries： Indonesia, Singapore, and Thailand. Furthermore, there is evidence of volatility clustering in these stock markets. The study concluded with the policy implications of greater integration in light of the planned cross trading among four ASEAN bourses, namely, Philippines, Singapore, Thailand, and Malaysia by 2012.

Stock Returns, Volatility, and Cointegration among Chinese Stock Markets

This paper examines stockreturns, volatility, and cointegration among three Chinese stock marketsbeforeand afterHong Kong’sreturn to China. Theaverage daily returnsaremuch higherduring the first sub-period (from April1991 to June1997)and significantlyloweror even negativeduring the second sub-period (from July1997 to December2002). The mean adjusted changein volatilityis negativelyand significantly correlated with thelagged returns. This negative relation is mainly caused by a contemporaneous and significantly positive correlation between returnsand volatilityinthe firstsub-period. Thissignificant relationship disappears forthe Shanghai and Shenzhen Stock Exchanges and is even negative for the Hong Kong Stock Exchange during the second sub-period. Three Chinese stock markets arecointegrated over the entiresampleperiod and becomemore closelyrelated after Hong Kong’s return to China. Our results have important implications for both policy makers and individual investors.

Effect of economic policies on the stock and bond market under the impact of COVID-19

The global epidemic of COVID-19 has made a huge impact on global health and financial markets.And the spread of the virus has stalled economic development in many parts of the world.As stocks and bonds are two important financial assets,how to take appropriate economic policies to restore the stock and bond markets is the focus of governments as they are seeking for quick recovery.Based on the Event Study method and the GARCH model,data from 1 October 2019 to 1 April 2020 were collected from 26 countries or regions as analytic samples.The results show:1)COVID-19 has made greater impacts on the stock market than the bond market;2)the economic policy responses after the COVID-19 has brought impacts on both of the stock and the bond markets;3)the monetary policy responses has brought greater volatility to the stock market than the fiscal policy responses,while the fiscal policy responses has brought greater volatility to the bond market than the monetary policy;4)the fiscal policy has brought more positive effects on the stock market,and monetary policy has brought more positive effects on the bond market.This research is helpful to understand the mechanism of COVID-19’s impacts on the stock and bond market.And it is of great practical significance to the governments’decisions to make economic policy responses after an epidemic.

Coherence,Connectedness,Dynamic Linkages Among Oil and China's Sectoral Commodities with Portfolio Implications

This paper investigates the time-frequency dependence,return and volatility connectedness,dynamic linkages,and portfolio diversification gains among oil and China’s sectoral commodities,namely,Petrochemicals(CIFI),Grains(CRFI),Energy(ENFI),Non-ferrous metals(NFFI),Oil&Fats(OOFI),and Softs(SOFI),utilizing a proposed research framework that contains the wavelet coherence,novel TVP-VAR based connectedness,and the cDCC-,DECO-FIAPARCH(1,d,1)model.The empirical results demonstrate that global oil market exhibits a relatively higher(lower)coherence with ENFI,NFFI,and OOFI(CRFI)on the long-term time horizon and the oil market leads China’s sectoral commodities during most sample periods.The crude oil market transmits significant connectedness to China’s sectoral commodities,especially the energy commodity sector(ENFI).The dynamic return and volatility total spillovers tend to intensify and exhibit significant fluctuations during the GFC and the oil price collapse.Further,the time-varying linkages among oil and China’s sectoral commodities are positive and fluctuant,mainly at a relatively low level.The dynamic return and volatility connectedness,multi-view linkages,optimal portfolio weights,and hedging ratios display significant time-varying features.The oil-commodity nexus offers diversification benefits and the optimal-weighted portfolio presents the best variance and downside risk reduction performance.Furthermore,risk management effectiveness is market-condition-dependent and heterogeneous across different commodity sectors and sub-samples.This paper can not only help investors and market regulators to capture the complex interconnectedness and risk transmission trajectory among oil and China’s sectoral commodities but also benefits for investors and portfolio managers to construct optimal portfolios and hedging strategies.