基于LSTHAR模型的投资者关注对股市波动影响研究

doi:10.16381/j.cnki.issn1003-207x.2020.07.003

摘要/Abstract

摘要： 有限关注理论认为投资者关注有限，无法掌握市场上所有信息，这会使股票出现暂时的错误定价，引起市场波动，因此投资者关注可能包含预测波动的有益信息。鉴于百度指数能较好代理中国投资者的主动性关注，本文提出将其作为逻辑平滑转移结构的转移变量，引入已实现波动的异质自回归类模型，以刻画投资者关注的变化对未来市场波动的非线性影响。基于华夏上证50ETF高频价格数据的实证表明：新模型相比于异质自回归类基础模型，有显著更优的拟合效果和显著更强的预测性能，即投资者关注的非线性引入对波动率预测有显著贡献。本文还发现，相比于引入移动端百度指数和总体百度指数，引入电脑端百度指数对模型预测性能的改进明显更大，表明电脑端百度指数代表的投资者关注对市场波动有更大的影响。研究结论对投资者风险管理和投资决策有实际指导意义。

关键词: HAR模型, 逻辑平滑转移, 百度指数, 投资者关注, MCS检验

Abstract: Limited attention theory points out that investors have limited attention and can't possess all the information in the financial market. This will cause temporarily mispricing of stocks which generates volatility in the market, thus investor attention may include valuable information about future volatility.
Since the Baidu index can well measure active investor attention in China, the logistic smooth transition structure is incorporated in the heterogeneous autoregressive (HAR) models of realized volatility, with the Baidu index being the transition variable, which can characterize the nonlinear influence of investor attention variation on future volatility. Three HAR models are considered as the benchmark model, that is, the basic HAR-RV model, the HAR-RV-J model which includes jumps and the HAR-RV-CJ model with separates the contribution of continuous volatility and jumps.
50 ETF high-frequency data and the Baidu index from January 2, 2014 to November 30, 2017 are used as empirical data. For the out-of-sample forecast comparison, we not only compare the average losses, but also perform the Diebold-Mariano test and the model confidence set test to evaluate the statistical significance of the models' forecasting performance difference. Empirical results show that, the new models are significantly superior to the original heterogeneous autoregressive models both in-sample and out-of-sample, indicating that the nonlinear introduction of investor attention has significant contribution to volatility forecasting. In addition, compared to introducing the total index and the mobile index, introducing the PC oriented index contributes to volatility forecasting more significantly, showing that the investor attention represented by the PC oriented index impacts the market volatility more significantly.
The volatility forecasting capability is effectively improved by the nonlinear introduction of investor attention, and the appropriate choice of investor attention proxy is illuminated. Meanwhile, it provides practical guides for investor risk management and investment decision-making.

Key words: HAR models, logistic smooth transition, the Baidu index, investor attention, MCS test

中图分类号:

F830.9

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.

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