时变风险偏好与股票风险溢价预测:处置效应的作用

IF 3.4 3区 经济学 Q1 ECONOMICS
Kenan Qiao, Haibin Xie
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引用次数: 0

摘要

本研究探讨了处置效应能否解释时变风险偏好并预测股票风险溢价。为此,我们提出了一个增强的一般自回归条件异方差(GARCH)-中值模型,以揭示未实现收益/亏损、已实现收益和股票风险溢价之间的复杂关系。在我们的模型中,风险规避系数随未实现收益/亏损的市场状态而变化。利用美国股市的数据,我们有力地证明了处置效应推动了风险偏好的时变性:在未实现收益期间,风险规避系数显著为正,而在未实现亏损期间则不显著。这些发现调和了现有文献中关于风险收益权衡的相互矛盾的结果。此外,我们的模型在样本内和样本外都显示出股票风险溢价的可预测性。对于均值方差投资者来说,纳入我们模型的预测可以带来巨大的效用收益。我们的结果表明,处置效应会导致时变风险偏好,从而诱发股票风险溢价的可预测性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-varying risk preference and equity risk premium forecasting: The role of the disposition effect

This study examines whether the disposition effect can explain time-varying risk preference and predict the equity risk premium. To do so, we propose an augmented general autoregressive conditional heteroskedasticity (GARCH)-in-Mean model unraveling the complex relationship between unrealized gains/losses, realized returns, and the equity risk premium. In our model, the risk aversion coefficient varies with the market state of unrealized gains/losses. Using data from the US stock markets, we show strong evidence that the disposition effect drives time-varying risk preference: The risk aversion coefficient is significantly positive during periods of unrealized gains, but insignificant during periods of unrealized losses. These findings reconcile the conflicting results of the risk-return trade-off in existing literature. Moreover, our model shows significant predictability of the equity risk premium, both in-sample and out-of-sample. Incorporating our model's predictions can yield substantial utility gains for a mean-variance investor. Our results indicate that the disposition effect leads to time-varying risk preference and thus induces equity risk premium predictability.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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