经济政策不确定性、投资者情绪与股价同步性——基于TVP-VAR模型的时变参数

doi:10.12066/j.issn.1007-2861.2080

摘要/Abstract

摘要：

基于时变参数向量自回归(time-varying parameter-vector auto regression, TVP-VAR)模型, 考察了经济政策不确定性、投资者情绪与股价同步性之间的时变关联性. 模型估计结果表明, 经济政策不确定性对股价同步性主要表现为中短期的正向影响, 且波动比较明显, 长期影响则相对较弱; 投资者情绪对股价同步性表现为负向影响, 且短期影响最为明显, 长期影响则较弱. 时点脉冲函数结果显示, 在不同时间点上, 股价同步性对经济政策不确定性的冲击具有正向响应, 对投资者情绪的冲击具有负向响应, 且不同时间点的响应程度和响应时间均存在差异. 这些结论为进一步完善政策调控体系, 规范和引导投资者行为, 促进市场理性化提供了思路.

关键词: 经济政策不确定性, 投资者情绪, 股价同步性, 时变参数向量自回归(time-varying parameter-vector auto regression, TVP-VAR)

Abstract:

Based on the time-varying parameter-vector auto regression (TVP-VAR) model, a time-varying correlation between economic policy uncertainty, investor's sentiment and stock price synchronicity has been examined. The results show that the economic policy uncertainty has the positive effect on stock price synchronicity with obvious volatility in the short and medium term, and the long-term effect is relatively weak and has the stable performance. The investor's sentiment has a negative impact on the stock price synchronicity, and the short-term impact is the most obvious and the long-term impact is weak. The results of the time-point impulse function show that the stock price synchronicity has the positive response to the impact of the economic policy uncertainty and the negative response to the impact of the investor's sentiment at different time points. And there are differences in terms of response degree and response time at different time points. The conclusion sheds new light on how to further improve the policy control system, regulate and guide investors' behavior, and promote market rationalization.

Key words: economic policy uncertainty, investor's sentiment, stock price synchronicity, time-varying parameter-vector auto regression (TVP-VAR)

中图分类号:

F832.51

任永平, 李伟. 经济政策不确定性、投资者情绪与股价同步性——基于TVP-VAR模型的时变参数[J]. 上海大学学报(自然科学版), 2020, 26(5): 769-781.

REN Yongping, LI Wei. Economic policy uncertainty, investor's sentiment and stock price synchronicity: a time-varying analysis based on TVP-VAR model[J]. Journal of Shanghai University（Natural Science Edition）, 2020, 26(5): 769-781.

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