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The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks

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  • Taylor, Stephen J.
  • Yadav, Pradeep K.
  • Zhang, Yuanyuan

Abstract

We measure the volatility information content of stock options for individual firms using option prices for 149 US firms and the S&P 100 index. We use ARCH and regression models to compare volatility forecasts defined by historical stock returns, at-the-money implied volatilities and model-free volatility expectations for every firm. For 1-day-ahead estimation, a historical ARCH model outperforms both of the volatility estimates extracted from option prices for 36% of the firms, but the option forecasts are nearly always more informative for those firms that have the more actively traded options. When the prediction horizon extends until the expiry date of the options, the option forecasts are more informative than the historical volatility for 85% of the firms. However, at-the-money implied volatilities generally outperform the model-free volatility expectations.

Suggested Citation

  • Taylor, Stephen J. & Yadav, Pradeep K. & Zhang, Yuanyuan, 2010. "The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 871-881, April.
  • Handle: RePEc:eee:jbfina:v:34:y:2010:i:4:p:871-881
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    References listed on IDEAS

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    Cited by:

    1. Weinbaum, David, 2010. "Preference heterogeneity and asset prices: An exact solution," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2238-2246, September.
    2. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4943-4957.
    3. Chen, Carl R. & Diltz, J. David & Huang, Ying & Lung, Peter P., 2011. "Stock and option market divergence in the presence of noisy information," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 2001-2020, August.
    4. Chabi-Yo, Fousseni, 2011. "Explaining the idiosyncratic volatility puzzle using Stochastic Discount Factors," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1971-1983, August.
    5. Sharma, Susan Sunila & Narayan, Paresh Kumar & Zheng, Xinwei, 2014. "An analysis of firm and market volatility," Economic Systems, Elsevier, vol. 38(2), pages 205-220.
    6. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    7. repec:eee:jbfina:v:81:y:2017:i:c:p:136-149 is not listed on IDEAS
    8. Ormos, Mihály & Timotity, Dusan, 2016. "Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring," Economic Systems, Elsevier, vol. 40(3), pages 345-354.
    9. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Effects of the US stock market return and volatility on the VKOSPI," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 9, pages 1-34.
    10. repec:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-015-2079-y is not listed on IDEAS
    11. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Modeling and predicting the market volatility index: The case of VKOSPI," Economics Discussion Papers 2015-7, Kiel Institute for the World Economy (IfW).
    12. Shackleton, Mark B. & Taylor, Stephen J. & Yu, Peng, 2010. "A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2678-2693, November.
    13. Prokopczuk, Marcel & Wese Simen, Chardin, 2014. "The importance of the volatility risk premium for volatility forecasting," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 303-320.
    14. Ricardo Crisostomo & Lorena Couso, 2018. "Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes," Papers 1801.08007, arXiv.org, revised May 2018.
    15. Choi, Jaewon & Richardson, Matthew, 2016. "The volatility of a firm's assets and the leverage effect," Journal of Financial Economics, Elsevier, vol. 121(2), pages 254-277.
    16. Alok Kumar Mishra & Siba Prasad Panda, 2016. "Looking into the relationship between implied and realized volatility: a study on S&P CNX Nifty index option," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(1), pages 67-96, April.
    17. Jiang, George J. & Tian, Yisong S., 2010. "Misreaction or misspecification? A re-examination of volatility anomalies," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2358-2369, October.
    18. Kearney, Fearghal & Murphy, Finbarr & Cummins, Mark, 2015. "An analysis of implied volatility jump dynamics: Novel functional data representation in crude oil markets," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 199-216.
    19. repec:eee:intfor:v:33:y:2017:i:4:p:848-863 is not listed on IDEAS
    20. Apostolos Kourtis & Raphael N. Markellos & Lazaros Symeonidis, 2016. "An International Comparison of Implied, Realized, and GARCH Volatility Forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1164-1193, December.
    21. Gonzalez-Perez, Maria T., 2015. "Model-free volatility indexes in the financial literature: A review," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 141-159.
    22. repec:eee:riibaf:v:41:y:2017:i:c:p:445-460 is not listed on IDEAS
    23. Guo, Hui & Savickas, Robert, 2010. "Relation between time-series and cross-sectional effects of idiosyncratic variance on stock returns," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1637-1649, July.

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