On the sources of the aggregate risk premium: Risk aversion, bubbles or regime-switching?

IF 1.9 3区 经济学 Q2 ECONOMICS
Tomás E. Caravello , John Driffill , Turalay Kenc , Martin Sola
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引用次数: 0

Abstract

We develop and estimate a consumption-based asset pricing model that uses historical US financial data and assumes recursive utility, allowing for priced regime-switching risk and intrinsic bubbles. We also estimate several restricted versions, including only a subset of these features. Priced regime-switching risk is essential to the equity risk premium, explaining more than fifty per cent of it. Furthermore, a model that does not consider regime switching would overestimate the public's risk aversion, mistakenly assigning the observed risk premium to high-risk aversion instead of priced regime-switching. We also find that intrinsic bubbles are statistically significant, and even though they are not crucial in explaining the risk premium, they substantially improve the model's fit at the end of the sample.

总风险溢价的来源:风险规避、泡沫还是制度转换?
我们开发并估算了一个基于消费的资产定价模型,该模型使用了美国历史金融数据,并假设了递归效用,允许定价制度转换风险和内在泡沫。我们还估算了几个限制性版本,其中只包括这些特征的一个子集。定价制度转换风险对股票风险溢价至关重要,可以解释超过 50%的股票风险溢价。此外,不考虑制度转换的模型会高估公众的风险厌恶程度,从而错误地将观测到的风险溢价归因于高风险厌恶程度,而非定价制度转换。我们还发现,内在泡沫在统计意义上是显著的,尽管它们对解释风险溢价并不重要,但在样本末期,它们大大提高了模型的拟合度。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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