The Information Content of The Implied Volatility Surface: Can Option Prices Predict Jumps?

62 Pages Posted: 24 Sep 2019 Last revised: 10 Feb 2020

See all articles by Yufeng Han

Yufeng Han

University of North Carolina (UNC) at Charlotte - Finance

Fang Liu

Cornell University

Xiaoxiao Tang

University of Texas at Dallas - School of Management - Department of Finance & Managerial Economics

Date Written: February 9, 2020

Abstract

We ask whether option prices contain information on the likelihood and direction of jumps in the underlying stock prices. Applying the partial least squares (PLS) approach to the entire surface of the implied volatilities (IV), we show that option prices can successfully predict downward jumps in stock prices, but not upward jumps. The PLS estimated downward jump factor can predict stock returns with a spread of 1.53% per month between stocks predicted to have the lowest probability of downward jumps and stocks predicted to have the highest probability of downward jumps. Both put and call option prices, and options of both short and long maturity contribute to the predictability. Furthermore, the predictability of the downward jump is robust to many firm characteristics as well as options related variables. Consistent with the notion that informed investors trade in the options markets to profit from negative information in order to circumvent the short-sale constraint, we find that stronger predictability is associated with tighter short-sale constraints in the equity market, and in periods when the market has poor performance.

Keywords: Options, Implied Volatility, Jumps, PLS, Predictability

JEL Classification: G11, G14

Suggested Citation

Han, Yufeng and Liu, Fang and Tang, Xiaoxiao, The Information Content of The Implied Volatility Surface: Can Option Prices Predict Jumps? (February 9, 2020). Available at SSRN: https://ssrn.com/abstract=3454330 or http://dx.doi.org/10.2139/ssrn.3454330

Yufeng Han (Contact Author)

University of North Carolina (UNC) at Charlotte - Finance ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

Fang Liu

Cornell University ( email )

Ithaca, NY 14853
United States

Xiaoxiao Tang

University of Texas at Dallas - School of Management - Department of Finance & Managerial Economics ( email )

2601 North Floyd Road
P.O. Box 830688
Richardson, TX 75083
United States

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