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Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

Author

Listed:
  • David E. Allen

    (Edith Cowan University, Australia)

  • Abhay K. Singh

    (Edith Cowan University, Australia)

  • Robert J. Powell

    (Edith Cowan University, Australia)

  • Michael McAleer

    (Erasmus University Rotterdam, Complutense University of Madrid, Spain, and Kyoto University, Japan)

  • James Taylor

    (University of Oxford, Oxford)

  • Lyn Thomas

    (University of Southampton, Southampton)

Abstract

The purpose of this paper is to examine the asymmetric relationship betweenprice and implied volatility and the associated extreme quantile dependence usinglinear and non linear quantile regression approach. Our goal in this paper is todemonstrate that the relationship between the volatility and market return as quantifiedby Ordinary Least Square (OLS) regression is not uniform across the distributionof the volatility-price return pairs using quantile regressions. We examine thebivariate relationship of six volatility-return pairs, viz. 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 andSTOXX Volatility (VSTOXX) and STOXX. The assumption of a normal distributionin the return series is not appropriate when the distribution is skewed and henceOLS does not capture the complete picture of the relationship. Quantile regressionon the other hand can be set up with various loss functions, both parametric andnon-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 andnon-linear nature of the relationship between price and volatility. In the empiricalanalysis we compare the results from linear quantile regression (LQR) and copulabased non linear quantile regression known as copula quantile regression (CQR).The discussion of the properties of the volatility series and empirical findings inthis paper have significance for portfolio optimization, hedging strategies, tradingstrategies and risk management in general.

Suggested Citation

  • David E. Allen & Abhay K. Singh & Robert J. Powell & Michael McAleer & James Taylor & Lyn Thomas, 2013. "Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression," Tinbergen Institute Discussion Papers 13-020/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20130020
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    File URL: https://papers.tinbergen.nl/13020.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Avdulaj Krenar & Barunik Jozef, 2017. "A semiparametric nonlinear quantile regression model for financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 81-97, February.
    3. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stober, 2016. "Regime switching vine copula models for global equity and volatility indices," Papers 1604.05598, arXiv.org.
    4. Bekiros, Stelios & Jlassi, Mouna & Lucey, Brian & Naoui, Kamel & Uddin, Gazi Salah, 2017. "Herding behavior, market sentiment and volatility: Will the bubble resume?," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 107-131.
    5. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stöber, 2017. "Regime Switching Vine Copula Models for Global Equity and Volatility Indices," Econometrics, MDPI, vol. 5(1), pages 1-38, January.

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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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