Learning, slowly unfolding disasters, and asset prices

• 成果类型: Article
• 署名作者: Ghaderi, Mohammad; Kilic, Mete; Seo, Sang Byung
• 署名单位: University of Kansas; University of Southern California; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2021.05.030
• 发表日期: 2022
• 页码: 527-549
• 关键词: Bayesian learning; Economic disasters; Market crises; VIX; Put-protected portfolios

摘要

We develop a model that generates slowly unfolding disasters not only in the macroeconomy but also in financial markets. In our model, investors cannot exactly distinguish whether the economy is experiencing a mild/temporary downturn or is on the verge of a severe/prolonged disaster. Due to imperfect information, disaster periods are not fully identified by investors ex ante . Bayesian learning induces equity prices to gradually react to persistent consumption declines, which plays a critical role in explaining the VIX, variance risk premium, and put-protected portfolio returns. We show that our model can rationalize the market patterns of recent major crises, such as the dot-com bubble burst, Great Recession, and COVID-19 crisis, through investors' belief channel. (c) 2021 Elsevier B.V. All rights reserved.