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Uncertainty and the volatility forecasting power of option‐implied volatility

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  • Byounghyun Jeon
  • Sung Won Seo
  • Jun Sik Kim

Abstract

This study investigates the impact of uncertainty on the volatility forecasting power of option‐implied volatility. Option‐implied volatility is a powerful predictor of future volatility, particularly during periods of high uncertainty. This is consistent with option‐implied volatility being largely determined by volatility‐informed traders (rather than directional traders) when uncertainty is high. New volatility forecasting models that incorporate such interaction outperform benchmark models, both in‐ and out‐of‐sample. The new models also better predict future volatility during the 2008 global financial crisis, for which benchmark models perform poorly. The results are robust to alternative choices of benchmark models, loss functions, and estimation windows.

Suggested Citation

  • Byounghyun Jeon & Sung Won Seo & Jun Sik Kim, 2020. "Uncertainty and the volatility forecasting power of option‐implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1109-1126, July.
  • Handle: RePEc:wly:jfutmk:v:40:y:2020:i:7:p:1109-1126
    DOI: 10.1002/fut.22116
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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Segal, Gill & Shaliastovich, Ivan & Yaron, Amir, 2015. "Good and bad uncertainty: Macroeconomic and financial market implications," Journal of Financial Economics, Elsevier, vol. 117(2), pages 369-397.
    3. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
    4. Yoram Halevy & Vincent Feltkamp, 2005. "A Bayesian Approach to Uncertainty Aversion," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(2), pages 449-466.
    5. Andrea Buraschi & Fabio Trojani & Andrea Vedolin, 2014. "When Uncertainty Blows in the Orchard: Comovement and Equilibrium Volatility Risk Premia," Journal of Finance, American Finance Association, vol. 69(1), pages 101-137, February.
    6. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
    7. Segal, Uzi, 1987. "The Ellsberg Paradox and Risk Aversion: An Anticipated Utility Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(1), pages 175-202, February.
    8. Beber, Alessandro & Breedon, Francis & Buraschi, Andrea, 2010. "Differences in beliefs and currency risk premiums," Journal of Financial Economics, Elsevier, vol. 98(3), pages 415-438, December.
    9. Nabil Al-Najjar & Jonathan Weinstein, 2015. "A Bayesian model of Knightian uncertainty," Theory and Decision, Springer, vol. 78(1), pages 1-22, January.
    10. Bollerslev, Tim & Gibson, Michael & Zhou, Hao, 2011. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Journal of Econometrics, Elsevier, vol. 160(1), pages 235-245, January.
    11. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    12. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    13. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    14. repec:hal:journl:peer-00741630 is not listed on IDEAS
    15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    16. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    17. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    18. Huang, Darien & Schlag, Christian & Shaliastovich, Ivan & Thimme, Julian, 2019. "Volatility-of-Volatility Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(6), pages 2423-2452, December.
    19. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    20. Junjie Hu & Wolfgang Karl Hardle & Weiyu Kuo, 2019. "Risk of Bitcoin Market: Volatility, Jumps, and Forecasts," Papers 1912.05228, arXiv.org, revised Dec 2021.
    21. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    22. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    23. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    24. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    25. Busch, Thomas & Christensen, Bent Jesper & Nielsen, Morten Ørregaard, 2011. "The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets," Journal of Econometrics, Elsevier, vol. 160(1), pages 48-57, January.
    26. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67.
    27. repec:bla:jfinan:v:59:y:2004:i:3:p:1235-1258 is not listed on IDEAS
    28. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    29. JoongHo Han & Da‐Hea Kim & Suk‐Joon Byun, 2017. "Informed Trading in the Options Market and Stock Return Predictability," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(11), pages 1053-1093, November.
    30. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    31. Sophie X. Ni & Jun Pan & Allen M. Poteshman, 2008. "Volatility Information Trading in the Option Market," Journal of Finance, American Finance Association, vol. 63(3), pages 1059-1091, June.
    32. Borochin, Paul & Zhao, Yanhui, 2019. "Belief heterogeneity in the option markets and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 107(C), pages 1-1.
    33. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    34. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    35. Krause, Timothy A., 2019. "Hedge fund returns and uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 597-601.
    36. Park, Yang-Ho, 2015. "Volatility-of-volatility and tail risk hedging returns," Journal of Financial Markets, Elsevier, vol. 26(C), pages 38-63.
    37. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    38. Andrea Buraschi & Alexei Jiltsov, 2006. "Model Uncertainty and Option Markets with Heterogeneous Beliefs," Journal of Finance, American Finance Association, vol. 61(6), pages 2841-2897, December.
    39. Baltussen, Guido & van Bekkum, Sjoerd & van der Grient, Bart, 2018. "Unknown Unknowns: Uncertainty About Risk and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(4), pages 1615-1651, August.
    40. Fei Sun & Yijun Hu, 2018. "Quasiconvex risk measures with markets volatility," Papers 1806.08701, arXiv.org, revised Jun 2019.
    41. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    42. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    43. Andrew Dubinsky & Michael Johannes & Andreas Kaeck & Norman J Seeger, 2019. "Option Pricing of Earnings Announcement Risks," The Review of Financial Studies, Society for Financial Studies, vol. 32(2), pages 646-687.
    44. Andreou, Panayiotis C. & Kagkadis, Anastasios & Philip, Dennis & Tuneshev, Ruslan, 2018. "Differences in options investors’ expectations and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 315-336.
    45. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
    46. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    47. Fei Sun & Yijun Hu, 2018. "Systemic risk measures with markets volatility," Papers 1812.06185, arXiv.org, revised Jun 2019.
    48. Brenner, Menachem & Izhakian, Yehuda, 2018. "Asset pricing and ambiguity: Empirical evidence⁎," Journal of Financial Economics, Elsevier, vol. 130(3), pages 503-531.
    49. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
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