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The Volatility-Return Relationship:Insights from Linear and Non-Linear Quantile Regressions

Author

Listed:
  • David E Allen

    (School of Accouting Finance & Economics, Edith Cowan University, Australia)

  • Abhay K Singh

    (School of Accouting Finance & Economics, Edith Cowan University, Australia)

  • Robert J Powell

    (School of Accouting Finance & Economics, Edith Cowan University, Australia)

  • Michael McAleer

    (Erasmus School of Economics, Erasmus University Rotterdam, Institute for Economic Research,Kyoto University, and Department of Quantitative Economics, Complutense University of Madrid)

  • James Taylor

    (Said Business School, University of Oxford, Oxford)

  • Lyn Thomas

    (Southampton Management School, University of Southampton, Southampton)

Abstract

This paper examines the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using a linear and non- linear quantile regression approach. Our goal is to demonstrate that the relationship between the volatility and market return, as quantified by Ordinary Least Square (OLS) regression, is not uniform across the distribution of the volatility-price re- turn pairs using quantile regressions. We examine the bivariate relationships of six volatility-return pairs, namely: CBOE VIX and S&P 500, FTSE 100 Volatility and FTSE 100, NASDAQ 100 Volatility (VXN) and NASDAQ, DAX Volatility (VDAX) and DAX 30, CAC Volatility (VCAC) and CAC 40, and STOXX Volatility (VS- TOXX) and STOXX. The assumption of a normal distribution in the return series is not appropriate when the distribution is skewed, and hence OLS may not capture a complete picture of the relationship. Quantile regression, on the other hand, can be set up with various loss functions, both parametric and non-parametric (linear case) and can be evaluated with skewed marginal-based copulas (for the non-linear case), which is helpful in evaluating the non-normal and non-linear nature of the relationship between price and volatility. In the empirical analysis we compare the results from linear quantile regression (LQR) and copula based non-linear quantile regression known as copula quantile regression (CQR). The discussion of the prop- erties of the volatility series and empirical findings in this paper have significance for portfolio optimization, hedging strategies, trading strategies and risk management, in general.

Suggested Citation

  • David E Allen & Abhay K Singh & Robert J Powell & Michael McAleer & James Taylor & Lyn Thomas, 2012. "The Volatility-Return Relationship:Insights from Linear and Non-Linear Quantile Regressions," KIER Working Papers 831, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:831
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    1. Moshe Buchinsky & Jennifer Hunt, 1999. "Wage Mobility In The United States," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 351-368, August.
    2. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    3. Jun Liu, 2005. "An Equilibrium Model of Rare-Event Premia and Its Implication for Option Smirks," The Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 131-164.
    4. Andrew J. Patton, 2004. "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 130-168.
    5. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    6. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    9. Wu, Guojun, 2001. "The Determinants of Asymmetric Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 14(3), pages 837-859.
    10. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
    11. Eric Bouye & Mark Salmon, 2009. "Dynamic copula quantile regressions and tail area dynamic dependence in Forex markets," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 721-750.
    12. Ihsan Ullah Badshah, 2013. "Quantile Regression Analysis of the Asymmetric Return‐Volatility Relation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(3), pages 235-265, March.
    13. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    14. David E. Allen & Singh Robert Powell, 2011. "Asset Pricing, the Fama—French Factor Model and the Implications of Quantile-Regression Analysis," Palgrave Macmillan Books, in: Greg N. Gregoriou & Razvan Pascalau (ed.), Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures, chapter 7, pages 176-193, Palgrave Macmillan.
    15. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    16. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
    17. Hibbert, Ann Marie & Daigler, Robert T. & Dupoyet, Brice, 2008. "A behavioral explanation for the negative asymmetric return-volatility relation," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2254-2266, October.
    18. Cheekiat Low, 2004. "The Fear and Exuberance from Implied Volatility of S&P 100 Index Options," The Journal of Business, University of Chicago Press, vol. 77(3), pages 527-546, July.
    19. Michelle L. Barnes & Anthony W. Hughes, 2002. "A quantile regression analysis of the cross section of stock market returns," Working Papers 02-2, Federal Reserve Bank of Boston.
    20. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    21. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    22. Dennis, Patrick & Mayhew, Stewart & Stivers, Chris, 2006. "Stock Returns, Implied Volatility Innovations, and the Asymmetric Volatility Phenomenon," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 41(2), pages 381-406, June.
    23. Chan, Louis K. C. & Lakonishok, Josef, 1992. "Robust Measurement of Beta Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 27(2), pages 265-282, June.
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    Cited by:

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    More about this item

    Keywords

    Return Volatility relationship; quantile regression; copula; copula quantile regression; volatility index; tail dependence;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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