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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
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 34 (2010)
    Issue (Month): 4 (April)
    Pages: 871-881

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    Handle: RePEc:eee:jbfina:v:34:y:2010:i:4:p:871-881

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Volatility Stock options Information content Implied volatility Model-free volatility expectations ARCH models;

    References

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    Cited by:
    1. 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.
    2. 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.
    3. Weinbaum, David, 2010. "Preference heterogeneity and asset prices: An exact solution," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2238-2246, September.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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