This paper investigates the dynamic hedging performance of the high frequency data based realized minimum-variance hedge ratio (RMVHR) approach. We comprehensively examine a number of popular time-series models to forecast the RMVHR for the CSI 300 index futures, and evaluate the out-of-sample dynamic hedging performance in comparison to the conventional hedging models using daily prices, as well as the vector heterogeneous autoregressive model using intraday prices. Our results show that the dynamic hedging performance of the RMVHR-based methods significantly dominates that of the conventional methods in terms of both hedging effectiveness and tracking error volatility in the out-of-sample forecast period. Furthermore, the superiority of the RMVHR-based methods is robust in different market structures and different volatility regimes, including China's abnormal market fluctuations in 2015 and the US financial crisis in 2008.

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Dynamic hedging using the realized minimum-variance hedge ratio approach – Examination of the CSI 300 index futures

PurposeThe focus of the current research is to examine whether mixed-frequency investor sentiment affects stock volatility in the China A-shares stock market.Design/methodology/approachMixed-frequency sampling models are employed to find the relationship between stock market volatility and mixed-frequency investor sentiment. Principal analysis and MIDAS-GARCH model are used to calibrate the impact of investor sentiment on the large-horizon components of volatility of Shanghai composite stocks.FindingsThe results show that the volatility in Chinese stock market is positively influenced by B–W investor sentiment index, when the sentiment index encompasses weighted mixed frequencies with different horizons. In particular, the impact of mixed-frequency investor sentiment is most significantly on the large-horizon components of volatility. Moreover, it is demonstrated that mixed-frequency sampling model has better explanatory powers than exogenous regression models when accounting for the relationship between investor sentiment and stock volatility.Practical implicationsGiven the various unique features of Chinese stock market and its importance as the major representative of world emerging markets, the findings of the current paper are of particularly scholarly and practical significance by shedding lights to the applicableness GARCH-MIDAS in the focused frontiers.Originality/valueA more accurate and insightful understanding of volatility has always been one of the core scholarly pursuits since the influential structural time series modeling of Engle (1982) and the seminal work of Engle and Rangel (2008) attempting to accommodate macroeconomic factors into volatility models. However, the studies in this regard are so far relatively scarce with mixed conclusions. The current study fills such gaps with improved MIDAS-GARCH approach and new evidence from Shanghai A-share market.

How does investor sentiment impact stock volatility? New evidence from Shanghai A-shares market

In this paper, we empirically investigate the impacts of the circuit breaker mechanism installed in the Shanghai Stock Exchange using high-frequency intraday data. We find that trade impediments have liquidity effects. In the presence of circuit breakers, investors' behavior will accelerate the arrival of price limits. We also document significant downward magnet effects for individual stocks and for the market index but find no significant volatility spillover effects.

The impacts of circuit breakers on China's stock market

We investigate the volatility spillover between the crude oil (West Texas Intermediate) and G7 countries' equity markets with high-frequency data. Considering the trading period difference among different countries, the volatility spillover of oil market intraday and overnight sessions is studied with G7 countries, respectively. The empirical findings suggest that the US stock market dominates intraday volatility spillover to the oil market, while the European and Asian stock markets dominate overnight spillovers. The asymmetric spillover result shows that stock markets mainly spillover bad volatility to the oil market in the intraday trading session, and spillover good volatility to the oil market in the overnight trading session. Our results provide empirical evidence that crude oil market overnight trading information is also important for understanding information diffusion and volatility forecasting. 

Trading around the clock: Revisit volatility spillover between crude oil and equity markets in different trading sessions

This paper proposes a sequential methodology to forecast the stock markets downside extreme risk during overnight and daytime periods. We jointly characterize Value at Risk (VaR) and Expected Shortfall (ES) dynamics during overnight and daytime using a sequential hybrid GAS model conditional on lagged information. We implement the Chernozhukov-Hong MCMC methods for parameter estimation and model evaluation. The results show that the sequential models in forecasting VaR and ES outperform the non-sequential ones, and accumulated information during non-trading overnight hours can help improve the forecasting of daytime downside extreme risk. Moreover, overnight and daytime extreme risks evidently increase during the economic downturn.

Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆

Purpose




The purpose of this paper is to examine the difference between the daytime (open-to-close) and overnight (close-to-open) returns of CSI 300 index and its derivative futures.




Design/methodology/approach




The paper explores the difference between the daytime and overnight time returns by using nonparametric techniques. Moreover, investigation on some factors such as short selling, trading rules, risks are made to seek the sources of the day and night effects based on a large number of empirical analysis. In the end, further analyses on daytime and overnight returns are given by the use of high-frequency data and linear regression technique.




Findings




The authors show that the daytime returns of CSI 300 index are no less than its overnight returns, while the daytime returns of CSI 300 index futures are no more than its overnight returns, even after removing the heteroscedasticity of the researched time series. Specifically, the PM returns (13:05 to close) play a quite important role in the intra-day time. The findings also suggest that the unique “T+1 trading rule” in China may be a reason that incurs the lower opening price in the morning and the higher closing price in the afternoon, resulting in the statistically significant differences between the daytime and overnight returns.




Practical implications




The findings are of great importance for investors to decide when to buy and sell stock and futures portfolios in Chinese financial markets.




Originality/value




This study empirically analyzes why there the higher daytime returns and the lower overnight returns exist in the Chinese stock markets from different aspects and contributes the existing literature on day and night effects because of periodic market closures.

Bin Tong

Papers

Does a unique “T+1 trading rule” in China incur return difference between daytime and overnight periods?