引入投资者关注的中国股市协方差预测——基于多元HAR类模型

doi:10.16381/j.cnki.issn1003-207x.2019.2148

摘要/Abstract

摘要： 众多经济事实表明投资者并非完全理性。一方面，投资者由于有限关注，无法及时掌握市场上所有投资决策相关信息，可能导致资产价格对信息的反应不足；另一方面，某些信息会诱导投资者过度关注和过度交易，导致价格信号中包含更多噪声。这些都会造成短暂的错误定价，引起资产价格波动和资产间相关性的变化。鉴于此，本文认为投资者关注是影响资产多元波动率的一个重要外生因素，用百度指数衡量中国市场个体投资者关注，将其引入已实现协方差的多元异质自回归类模型，刻画个体投资者关注的变化对资产协方差的非对称影响，同时区分电脑端和移动端百度指数对协方差预测的不同贡献。采用2014年1月2日至2018年12月28日的50ETF成分股高频价格，和以股票简称及“50ETF”为关键词的百度指数，对上述模型进行实证。结果表明，百度指数代理的个体投资者关注蕴含对协方差预测有益的信息，将其引入协方差预测模型显著提升拟合性能和样本外预测能力；投资者关注的变化对资产波动及相关性的影响均存在非对称性；引入电脑端百度指数比引入移动端百度指数对协方差预测性能的提升更为显著。研究结果肯定了引入投资者关注对协方差预测的积极作用，对投资者的资产配置和风险管理有实际指导意义。

关键词: 已实现协方差；MHAR-DRD模型；百度指数；个体投资者关注；非对称影响

Abstract: Economic facts disclose that investors are not completely rational. On the one hand, investors can’t possess all the information in the financial markets due to limited attention, which causes the under-reaction of prices to some information. On the other hand, some information may induce investors’ over attention and over trading, which results in more noises in the price signals. These may cause temporarily mispricing of stocks, which generates volatility and correlation in the market.

Key words: realized covariance; the MHAR-DRD model; Baidu index; individual investor attention; asymmetric influences

中图分类号:

F830.9

瞿慧, 沈微. 引入投资者关注的中国股市协方差预测——基于多元HAR类模型[J]. 中国管理科学, 2022, 30(7): 9-19.

QU Hui, SHEN Wei. Investor Attention and Covariance Forecasting in China’s Stock Markets——A Study Based on the MHAR Type Models[J]. Chinese Journal of Management Science, 2022, 30(7): 9-19.

参考文献

［1］ Andersen T G, Bollerslev T. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts［J］. International Economic Review, 1998, 39(4): 885-905
［2］ Barndorff-Nielsen O E, Hansen P R, Lunde A, et al. Designing realized kernels to measure the ex-post variation of equity prices in the presence of noise［J］. Econometrica, 2008, 76(6): 1481-1536.
［3］ Barndorff-Nielsen O E, Shephard N. Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics［J］. Econometrica, 2004, 72(3): 885-925.
［4］ Barndorff-Nielsen O E, Hansen P R, Lunde A, et al. Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading［J］. Journal of Econometrics, 2011, 162(2): 149-169
［5］ Corsi F. A simple approximate long-memory model of realized volatility［J］. Journal of Financial Econometrics, 2009, 7(2): 174-196.
［6］ Chiriac R, Voev V. Modelling and forecasting multivariate realized volatility［J］. Journal of Applied Econometrics, 2011, 26(6): 922-947.
［7］ Bauer G H, Vorkink K. Forecasting multivariate realized stock market volatility［J］. Journal of Econometrics, 2011, 160(1): 93-101.
［8］ Hautsch N, Kyj L M, Malec P. Do high-frequency data improve high-dimensional portfolio allocations［J］? Journal of Applied Econometrics, 2015, 30(2): 263-290.
［9］ Oh D H, Patton A J. High-dimensional copula-based distributions with mixed frequency data［J］. Journal of Econometrics, 2016, 193(2): 349-366.
［10］ Bollerslev T, Patton A J, Quaedvlieg R. Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions［J］. Journal of Econometrics, 2018, 207(1): 71-91.
［11］ Kahneman D. Attention and effort［M］. Prentice-Hall, Englewood Cliffs, NJ, 1973.
［12］ Da Z, Engelberg J, Gao P J. In search of attention［J］. The Journal of Finance, 2011, 66(5): 1461-1499.
［13］ Andrei D, Hasler M. Investor attention and stock market volatility［J］. Review of Financial Studies, 2015, 28(1): 33-72.
［14］ Aouadi A, Arouri M, Teulon F. Investor attention and stock market activity: Evidence from France［J］. Economic Modelling, 2013, 35(3): 674-681.
［15］ Goddard J, Kita A, Wang Qingwei. Investor attention and FX market volatility［J］. Journal of International Financial Markets Institutions and Money, 2015, 38: 79-96.
［16］苑莹, 樊晓倩. 投资者异常关注与成交量和波动率的交叉相关性及传导方向——基于复杂性理论视角［J］. 系统管理学报, 2019, 28(2): 89-99.Yuan Ying, Fan Xiaoqian. Cross-correlations and conducting direction between abnormal investor attention, trading volume, and volatility based on the perspective of complexity theory［J］. Journal of Systems & Management, 2019, 28(2): 89-99.
［17］张跃军, 李书慧. 投资者关注度对国际原油价格波动的影响研究［J］. 系统工程理论与实践, 2020, 40(10): 2519-2529.Zhang Yuejun, Li Shuhui. The impact of investor attention on international crude oil price volatility［J］. Systems Engineering-Theory & Practice, 2020, 40(10): 2519-2529.
［18］田金方, 杨晓彤, 薛瑞, 等. 不确定性事件、投资者关注与股市异质特征——以COVID-19概念股为例［J］. 财经研究, 2020, 46(11): 19-33.Tian Jinfang, Yang Xiaotong, Xue Rui, et al. Uncertain event, investor attention and heterogeneity of the stock market: A case study on COVID-19［J］. Journal of Finance and Economics, 2020, 46(11): 19-33.
［19］ Dimpfl T, Jank S. Can internet search queries help to predict stock market volatility?［J］. European Financial Management, 2016, 22(2): 171-192.
［20］陈声利, 关涛, 李一军. 基于跳跃、好坏波动率与百度指数的股指期货波动率预测［J］.系统工程理论与实践, 2018, 38(2): 299-316.Chen Shengli, Guan Tao, Li Yijun. Forecasting realized volatility of Chinese stock index futures based on jumps, good-bad volatility and Baidu index［J］. Systems Engineering-Theory & Practice,2018, 38(2): 299-316.
［21］ Zhang Wei, Yan Kai, Shen Dehua. Can the Baidu index predict realized volatility in the Chinese stock market［J］? Financial Innovation, 2021, 7(1): 1-31.
［22］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 Tonghui, 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.
［23］瞿慧, 沈微. 基于LSTHAR模型的投资者关注对股市波动影响研究［J］. 中国管理科学,2020, 28(7): 23-34.Qu Hui, Shen Wei. The impact of investor attention on market volatility based on the LSTHAR model［J］. Chinese Journal of Management Science, 2020, 28(7): 23-34.
［24］ Barber B M, Odean T, Zhu N. Systematic noise［J］. Journal of Financial Markets, 2009, 12(4): 547-569.
［25］ De Long J B, Shleifer A, Summers L H, et al. Noise trader risk in financial markets［J］. Journal of Political Economy, 1990, 98(4):703-738.
［26］ Alfarano S, Lux T. A noise trader model as a generator of apparent financial power laws and long memory［J］. Macroeconomic Dynamics, 2007, 11(S1): 80-101.
［27］ Laurent S, Rombouts J V K, Violante F. On loss functions and ranking forecasting performances of multivariate volatility models［J］. Journal of Econometrics, 2013, 173(1): 1-10.
［28］ Diebold F X, Mariano R S. Comparing predictive accuracy［J］. Journal of Business and Economic Statistics, 1995, 13(3): 253-263.
［29］ Hansen P R, Lunde A, Nason J M. The model confidence set［J］. Econometrica, 2011, 79(2): 453-497.
［30］吴丹, 毕仁敏. 用户移动搜索与桌面搜索行为对比研究［J］. 现代图书情报技术, 2016, 32(2):1-8.Wu Dan, Bi Renmin. Mobile and desktop search behaviors: A comparative study［J］. New Technology of Library and Information Service, 2016, 32(2):1-8.

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