Option-Implied Factors for Predicting Stock and Aggregate Returns
Summary
The document surveys research using information from option prices and implied volatility to forecast returns. It groups examples into several themes: predictors derived from implied volatility, expected-return estimates recovered from option prices, and variance or tail-risk premiums used to forecast aggregate stock returns. It also points to work on option-based predictors beyond those categories. The central takeaway is that option surfaces may contain signals relevant to both cross-sectional and aggregate returns, and that option information can also be studied for predicting returns on options or other asset classes.
The evidence presented is a bibliography of named papers and publication venues, rather than summaries of their methods or findings. It does not compare predictors, report effect sizes, or establish that any signal is profitable after costs. The suggested references are therefore a starting point for research, not a complete account of implementation or robustness. Readers would need to consult the papers to learn how each factor is defined, which assets and horizons it covers, and whether its predictive results persist out of sample.
Key ideas
- Option implied-volatility changes and spreads are among the proposed predictors of future returns.
- Some research extracts expected stock returns or lower bounds on them from option prices.
- Variance risk premiums and tail-risk premiums have been studied as predictors of aggregate stock returns.
- Option-based signals can also be investigated for forecasting option returns and returns in other asset classes.
- The document provides paper references rather than comparative evidence about signal strength or trading profitability.
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# Options related factors forecasting cross section of returns # Options related factors forecasting cross section of returns I came across this research paper that shows that skewness derived from options surfaces can help explain the cross section of returns. https://pubsonline.informs.org/doi/10.1287/mnsc.2015.2379 Are there any other similar papers that have other option surface related factors that can explain cross section returns? For example, change in IV or put call ratio etc? ## Answer by Kevin (score 5) https://quant.stackexchange.com/a/78746 There is plenty of research! Below I list but a few examples of options being used to predict future stock returns (either in aggregate or in the cross-section). Of course, you can also use current option prices to predict future option returns (or other asset classes). #### Implied Volatility One set of papers considers variable derived from option implied volatilities. Examplary predictors include changes or spreads in implied volatility. You can apply these predictors also to the cross-section of bond returns. - Bali and Hovakimian (2009, MS) - Cremers and Weinbaum (2010, JFQA) - An et al. (2014, JF) - Goncalves-Pinto (2020, MS) - Cao et al. (2023, MS) - Campbell et al. (2023, JFQA) #### Expected Returns A recent literature recovers (lower bounds for) expected stock returns from option prices. This works for aggregate indices and for single stocks. Very cool papers in my opinion! - Martin (2017, QJE) - Martin and Wagner (2019, JF) - Schneider and Trojani (2019, JF) - Kadan and Tang (2020, RFS) - Chabi-Yo and Loudis (2020, JFE) - Chabi-Yo et al. (2023, MS) #### Aggregate Returns Another literature identifies variance risk premiums or tail risk premiums as predictors for aggregate stock returns. - Bollerslev et al. (2009, RFS) - Bollerslev et al. (2015, JFE) - Andersen et al. (2015, JFE) - Pyun (2019, JFE) #### And much more And, of course, there is so much more work! - Pan and Poteshman (2006, RFS) - Conrad et al. (2013, JF) - Kapadia and Zekhnini (2019, JFE) - Ni et al. (2021, RFS) - Chordia et al. (2021, JFQA) - Weinbaum et al. (2023, MS)
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