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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

    The volatility information content of stock options for individual firms is measured using option prices for 149 U.S. firms and the S&P 100 index. ARCH and regression models are used to compare volatility forecasts defined by historical stock returns, at-the-money implied volatilities and model-free volatility expectations for every firm. For one-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, the model-free volatility expectations are generally outperformed by the at-the-money implied volatilities. --

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    Bibliographic Info

    Paper provided by University of Cologne, Centre for Financial Research (CFR) in its series CFR Working Papers with number 09-07.

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    Date of creation: 2009
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    Handle: RePEc:zbw:cfrwps:0907

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    Related research

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

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    1. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    2. Lamoureux, Christopher G & Lastrapes, William D, 1993. "Forecasting Stock-Return Variance: Toward an Understanding of Stochastic Implied Volatilities," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 6(2), pages 293-326.
    3. Duffee, Gregory R., 1995. "Stock returns and volatility A firm-level analysis," Journal of Financial Economics, Elsevier, Elsevier, vol. 37(3), pages 399-420, March.
    4. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 50(2), pages 125-150, November.
    5. Cheung, Yin-Wong & Ng, Lilian K, 1992. " Stock Price Dynamics and Firm Size: An Empirical Investigation," Journal of Finance, American Finance Association, American Finance Association, vol. 47(5), pages 1985-97, December.
    6. 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, Elsevier, vol. 105(1), pages 5-26, November.
    7. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
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    Cited by:
    1. Cathy Chen & I-Doun Kuo, 2014. "Investor sentiment and interest rate volatility smile: evidence from Eurodollar options markets," Review of Quantitative Finance and Accounting, Springer, Springer, vol. 43(2), pages 367-391, August.
    2. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2010. "Predictable dynamics in implied volatility surfaces from OTC currency options," Journal of Banking & Finance, Elsevier, Elsevier, vol. 34(6), pages 1175-1188, June.

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