代表性代理模型中风险溢价的来源研究

Tyler Beason, David Schreindorfer
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引用次数: 8

摘要

我们使用期权和返回数据将无条件风险溢价分解为返回状态空间的不同部分。在数据中,整个股票溢价归因于月回报率低于-11.3%,但极左尾部的回报率影响很小。相比之下,基于习惯、长期风险和罕见灾难的主流资产定价模型几乎完全将溢价归因于高于-11.3%的回报,或极左尾部。我们发现尾部风险数量较大的模型扩展不能解释数据,而尾部风险价格较高的模型可以解释数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Sources of Risk Premia in Representative Agent Models
We use options and return data to decompose unconditional risk premia into different parts of the return state space. In the data, the entire equity premium is attributable to monthly returns below -11.3%, but returns in the extreme left tail matter very little. In contrast, leading asset pricing models based on habits, long-run risks, and rare disasters attribute the premium almost exclusively to returns above -11.3%, or to the extreme left tail. We find that model extensions with a larger quantity of tail risk cannot account for the data, while models with a higher price of tail risk can.
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