Predicting the Equity Premium with Implied Volatility Spreads Charles Cao † , Timothy Simin † , and Han Xiao ‡ † Department of Finance, Smeal College of Business, Penn State University ‡ Department of Economics, Penn State University March 23, 2018 1 / 32
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Predicting the Equity Premium withImplied Volatility Spreads
Charles Cao†, Timothy Simin†, and Han Xiao‡
† Department of Finance, Smeal College of Business, Penn State University‡ Department of Economics, Penn State University
March 23, 2018
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Motivation and Research Questions
Stock return predictabilty is an important question in asset pricing
literature (uncondtional and conditional)
Conventional predictiors are based on backward-looking information
I Dividend yield, P/E, Book-to-market ratio, term spread, etc
Question
I What is the predictive ability of forward-looking information of options?
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Motivation and Research Questions
Can the call-put option implied volatility spread (CPIVS) predict the
aggregate market risk premium?
Can we improve the performance of conditional factor models by
incorporating CPIVS?
Why does CPIVS have predictive power?
Does CPIVS predict non-equity variables?
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Motivation and Research Questions
Many reasons to investigate predictive ability of CPIVS
Theory
I Chowdhry and Nanda (1991), Easley, O’Hara, and Srinivas (1998):
Informed traders chose option market first
I An, Ang, Bali, and Cakici (2014): Noisy rational expectations model of
informed trading in both markets ⇒ option volatilities can predict
stock returns
Empirical work
I Option market information: price, volume and volatility
I Information Content of Option Implied Volatility Spread
I Nonlinear risks
I Cross sectional predictability
I Time-series prediction
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Literature Review
Information Content of Option Implied Volatility Spread
I Doran, Fodor, and Jiang (2013), Christoffersen, Jacobs, and Chang
(2013), Cao, Gempeshaw, and Simin (2018)
Nonlinear risks
I Bollerslev and Todorov (2011), Kelly and Jiang (2014)
Cross sectional evidence
I Bali and Hovakimian (2009), Cremers and Weinbaum (2010), Xing,
Zhang and Zhao (2010) and An, Ang, Bali and Cakici (2014)
Time-series prediction
I Atilgan, Bali and Demirtas (2015), Cao, Gempeshaw, and Simin (2018)
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Motivation and Research Questions
We consider a quarterly horizon:
Options are 3-month contracts
Longer horizon prediction: market timing, transaction costs, and
bid-ask spread
Lower autocorrelation: less spurious regression bias
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Main results
CPIVS predicts
I Quarterly aggregate market returns in-sample and out-of-sample
F In-sample R2: 14.7%!
F Out-of-sample R2: 8.5% (29% during recessions!)
I Long-run in-sample prediction up to three years
CPIVS improves the conditional factor models
I 50% less pricing errors
Prediction power comes from
I Forward-looking information orthogonal to other predictors
I Net innovation between call option and put option implied volatility