Can the Baidu Index predict realized volatility in the Chinese stock market?

Wei Zhang,Dehua Shen,Kai Yan

doi:10.1186/s40854-020-00216-y

Abstract

This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index. Furthermore, the predictability of the Baidu Index is found to rise as the forecasting horizon increases. We also find that continuous components enhance predictive power across all horizons, but that increases are only sustained in the short and medium terms, as the long-term impact on volatility is less persistent. Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

Highlights

Forecasting return volatility is a crucial task in investment, option pricing, and risk management
This paper focuses on the Chinese stock market because this market is dominated by individual investors and there is a large number of “netizens.” A recent survey of Shenzhen Stock Exchange (2018) shows that individual investors accounts for 75.1% of the total in Mainland China equities market
This section provides an empirical definition of volatility and of the components extracted from intraday data and the Baidu Index that will be used in our models

Summary

Introduction

Forecasting return volatility is a crucial task in investment, option pricing, and risk management. Methodology This section provides an empirical definition of volatility and of the components extracted from intraday data and the Baidu Index that will be used in our models (i.e., continuous components, semivariance, signed jumps, and investor attention). Model 8: HAR‐RV‐SJ The HAR-RV-SJ model investigates the effect of signed jumps by replacing the daily realized volatility with continuous component and signed jumps in HAR-RV models It is specified as: RVt+1,t+h = β0 + βδJ1 Jt + βC1Ct + β5RVt−4,t + β22RVt−21,t + εt (20). Model 11: HAR‐CSJd The HAR-CSJd was proposed by Sévi (2014) and considers many previously stated factors, including dividing signed jumps into positive and negative parts, long-period variables and continuous components.

11 HAR-CSJd

Findings

Conclusion