投资者关注能提高市场波动率预测精度吗？——基于中国股票市场高频数据的实证研究

doi:10.16381/j.cnki.issn1003-207x.2018.0509

摘要/Abstract

摘要： 本文选取百度网络搜索数据，构建了新的投资者关注指标；以上证指数和深证成指高频数据为研究样本，研究了不同的投资者关注水平与市场波动率之间的领先滞后关系；之后，本文将投资者关注因子纳入到ARMA类和HAR类模型，建立了新的投资者关注波动率预测模型；通过与传统模型的样本外预测比较，重点研究了投资者关注能否提高市场波动率预测精度这一问题。本文实证结果表明，投资者关注不仅可以提高现有波动率预测模型的样本内拟合能力，而且在投资者高关注时期，投资者关注可以显著且稳健的提高波动模型的样本外预测能力。这说明，投资者关注具有对股票市场的解释能力及更强的预测能力。此外，本文的研究结论还具有一定的应用价值：对个人和机构投资者来说，可以"先人一步"的把握市场发展趋势，增加获利机会；对监管部门而言，可以强化市场监管绩效，加快形成完备有效的股票交易市场。

关键词: 波动率预测, 百度指数, 投资者关注, 市场波动

Abstract: In this paper, it is argued that investor attentioncan provide more information in forecasting realized volatility of the SSE composite index and SZSE component index. Time-delay detrended cross-correlation analysis (DCCA)is employed to investigate the lead-lag relationships between investor attention and realized volatility. We further use various ARMA and HAR models as benchmarks to verify the possible effect of investor attention on stock market volatility forecasting. The empirical results show that, in a low volatile market environment with few Internet search activities, investor attention represented by Baidu search volume cannot improve the volatility forecasting accuracy in the Chinese stock market. While in a high turbulent market condition with plentiful Internet search activities, the extended ARMA and HAR models incorporating investor attention can offer more accurate volatility forecasts than the benchmarks. Our findings have important implications in practice. For retail investors and institutional investors, they can previously identify stock market tendency and get profit opportunities. As for regulators, they can strengthen the market regulation and foster an efficient stock market.

Key words: volatility forecasting, Baidu Index, investor attention, stock market volatility

中图分类号:

F803.91

张同辉, 苑莹, 曾文. 投资者关注能提高市场波动率预测精度吗？——基于中国股票市场高频数据的实证研究[J]. 中国管理科学, 2020, 28(11): 192-205.

ZHANG Tong-hui, YUAN Ying, ZENG Wen. Can Investor Attention Help to Predict Stock Market Volatility? An Empirical Research Based on Chinese Stock Market High-frequency Data[J]. Chinese Journal of Management Science, 2020, 28(11): 192-205.

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