Explaining the Idiosyncratic Volatility Puzzle With a Bayesian-Updating Model

69 Pages Posted: 7 May 2020

See all articles by Xuhui (Nick) Pan

Xuhui (Nick) Pan

University of Oklahoma

Bharat Raj Parajuli

University of Utah - David Eccles School of Business

Petra Sinagl

University of Iowa - Department of Finance

Date Written: April 13, 2020

Abstract

We find that the idiosyncratic volatility (IVOL) puzzle exists only among firms that under-perform their benchmark or release negative earnings surprises. We explain the findings using a Bayesian updating model in which agents observe noisy signals about future cash flows. In this setting, high IVOL increases the likelihood of observing earnings surprises. When the noisy surprise is negative, returns exhibit a negative momentum, and the IVOL puzzle emerges. When controlling for relative performance and signal precision, the IVOL puzzle disappears. Our explanation alone can account for up to 75% of the IVOL puzzle, which is more than all other existing theories combined.

Keywords: Idiosyncratic Volatility Puzzle, Bayesian Updating, Signal Precision

JEL Classification: G12, G14

Suggested Citation

Pan, Xuhui (Nick) and Parajuli, Bharat Raj and Sinagl, Petra, Explaining the Idiosyncratic Volatility Puzzle With a Bayesian-Updating Model (April 13, 2020). Available at SSRN: https://ssrn.com/abstract=3574790 or http://dx.doi.org/10.2139/ssrn.3574790

Xuhui (Nick) Pan

University of Oklahoma ( email )

307 W Brooks
Norman, OK 73019
United States

Bharat Raj Parajuli

University of Utah - David Eccles School of Business ( email )

1645 E Campus Center Dr
Salt Lake City, UT 84112-9303
United States

Petra Sinagl (Contact Author)

University of Iowa - Department of Finance ( email )

Iowa City, IA 52242-1000
United States

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