Disaster learning and aggregate investment

• 成果类型: Article
• 署名作者: Niu, Yingjie; Yang, Jinqiang; Zou, Zhentao
• 署名单位: Shanghai University of Finance & Economics; Shanghai Institute of International Finance & Economics; Shanghai University of Finance & Economics; Wuhan University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2024.105872
• 发表日期: 2024
• 关键词: Learning; Rare disaster; Jump size; Growth volatility; Equity risk premium

摘要

We extend a production-based asset pricing model by introducing learning about disaster risk. The information is not perfect, and Bayesian learning is adopted to update beliefs about the likelihood of rare disasters. We show that disaster learning reconciles key stylized facts about macroeconomic quantities and financial markets. For macroeconomic quantities, during the crisis, the decline in aggregate investments is much worse than that of output, whereas the decline in aggregate consumption is moderate relative to that of output. Additionally, the model endogenously features lower consumption volatility and higher investment volatility than that of output. For financial markets, belief updating over a rare disaster produces a higher equity premium, lower risk-free rate, and more volatile stock returns. Finally, we show that jump intensity uncertainty accounts for a substantial fraction of the total welfare cost of rare disasters.