Model Uncertainty in the Cross Section

69 Pages Posted: 14 Sep 2021 Last revised: 2 May 2022

See all articles by Jiantao Huang

Jiantao Huang

The University of Hong Kong - Faculty of Business and Economics

Ran Shi

University of Colorado Boulder - Department of Finance

Date Written: September 12, 2021

Abstract

We develop a transparent Bayesian approach to quantify uncertainty about linear stochastic discount factor models. Under our framework, posterior model probabilities increase with maximum in-sample Sharpe ratios and decrease with model dimensions. The entropy of posterior probabilities represents model uncertainty. We show that model uncertainty displays massive independent variations from popular uncertainty proxies, and episodes of heightened model uncertainty coincide with major market events. These observations hold not only in US markets but also in European and Asian Pacific equity markets. Moreover, positive model uncertainty shocks relate to sharp outflows from US equity mutual funds but significant inflows to government bond funds, with effects persisting for three years. Finally, the survey data suggests that investors tend to be more pessimistic about equity performance during periods of higher model uncertainty.

Keywords: Model Uncertainty, Linear Stochastic Discount Factor, Bayesian Inference

JEL Classification: C11, G11, G12.

Suggested Citation

Huang, Jiantao and Shi, Ran, Model Uncertainty in the Cross Section (September 12, 2021). Available at SSRN: https://ssrn.com/abstract=3922077 or http://dx.doi.org/10.2139/ssrn.3922077

Jiantao Huang (Contact Author)

The University of Hong Kong - Faculty of Business and Economics ( email )

Pokfulam Road
Hong Kong
Hong Kong

Ran Shi

University of Colorado Boulder - Department of Finance ( email )

995 Regent Drive
Boulder, CO 80309
United States

HOME PAGE: http://ranshi.one

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
190
Abstract Views
632
Rank
244,980
PlumX Metrics