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Predicting Extreme Returns and Portfolio Management Implications

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  • Krieger, Kevin
  • Fodor, Andy
  • Mauck, Nathan
  • Stevenson, Greg

Abstract

We consider which readily observable characteristics of individual stocks (e.g., option implied volatility, accounting data, analyst data) may be used to forecast subsequent extreme price movements. We are the first to explicitly consider the predictive influence of option implied volatility in such a framework, which we unsurprisingly find to be an important indicator of future extreme price movements. However, after controlling for implied volatility levels, other factors, particularly firm age and size, still have additional predictive power of extreme future returns. Furthermore, excluding predicted extreme return stocks leads to a portfolio that has lower risk (standard deviation of returns) without sacrificing performance.

Suggested Citation

  • Krieger, Kevin & Fodor, Andy & Mauck, Nathan & Stevenson, Greg, 2012. "Predicting Extreme Returns and Portfolio Management Implications," MPRA Paper 39845, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:39845
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    1. Ang, Andrew & Hodrick, Robert J. & Xing, Yuhang & Zhang, Xiaoyan, 2009. "High idiosyncratic volatility and low returns: International and further U.S. evidence," Journal of Financial Economics, Elsevier, vol. 91(1), pages 1-23, January.
    2. Wing Hong Chan & Ranjini Jha & Madhu Kalimipalli, 2009. "The Economic Value Of Using Realized Volatility In Forecasting Future Implied Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 231-259, September.
    3. Ofek, Eli & Richardson, Matthew & Whitelaw, Robert F., 2004. "Limited arbitrage and short sales restrictions: evidence from the options markets," Journal of Financial Economics, Elsevier, vol. 74(2), pages 305-342, November.
    4. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(4), pages 905-939.
    5. Wei Huang & Qianqiu Liu & S. Ghon Rhee & Liang Zhang, 2010. "Return Reversals, Idiosyncratic Risk, and Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 147-168, January.
    6. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-386.
    7. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    8. Xiaoquan Jiang & Bong-Soo Lee, 2006. "The Dynamic Relation Between Returns and Idiosyncratic Volatility," Financial Management, Financial Management Association, vol. 35(2), Summer.
    9. Peterson, David R. & Smedema, Adam R., 2011. "The return impact of realized and expected idiosyncratic volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2547-2558, October.
    10. Kevin Krieger & David Peterson, 2009. "Predicting stock splits with the help of firm-specific experiences," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 33(4), pages 410-421, October.
    11. Fu, Fangjian, 2009. "Idiosyncratic risk and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 91(1), pages 24-37, January.
    12. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    13. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    14. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    15. Xiaoquan Jiang & Bong‐Soo Lee, 2006. "The Dynamic Relation Between Returns and Idiosyncratic Volatility," Financial Management, Financial Management Association International, vol. 35(2), pages 43-65, June.
    16. Lev, B & Thiagarajan, Sr, 1993. "Fundamental Information Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 31(2), pages 190-215.
    17. Xing, Yuhang & Zhang, Xiaoyan & Zhao, Rui, 2010. "What Does the Individual Option Volatility Smirk Tell Us About Future Equity Returns?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(3), pages 641-662, June.
    18. Cremers, Martijn & Weinbaum, David, 2010. "Deviations from Put-Call Parity and Stock Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 335-367, April.
    19. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    20. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, vol. 32(4), pages 1151-1168, September.
    21. 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.
    22. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    23. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    24. Choong Tze Chua & Jeremy Goh & Zhe Zhang, 2010. "Expected Volatility, Unexpected Volatility, And The Cross‐Section Of Stock Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 103-123, June.
    25. Brockman, Paul & Turtle, H. J., 2003. "A barrier option framework for corporate security valuation," Journal of Financial Economics, Elsevier, vol. 67(3), pages 511-529, March.
    26. repec:bla:jfinan:v:53:y:1998:i:3:p:1131-1147 is not listed on IDEAS
    27. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    28. Black, Fischer, 1972. "Capital Market Equilibrium with Restricted Borrowing," The Journal of Business, University of Chicago Press, vol. 45(3), pages 444-455, July.
    29. Blitz, D.C. & van Vliet, P., 2007. "The Volatility Effect: Lower Risk without Lower Return," ERIM Report Series Research in Management ERS-2007-044-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    Cited by:

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    2. Feng Sun & Cheng Liu & Xiaoguang Zhou, 2017. "Analysis of industry risk premium with MVS three dimensions vector factor model," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1374814-137, January.
    3. Echaust, Krzysztof, 2021. "Asymmetric tail dependence between stock market returns and implied volatility," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).

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    More about this item

    Keywords

    Implied volatility; portfolio management;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G00 - Financial Economics - - General - - - General

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