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Assessing the idiosyncratic risk and stock returns relation in heteroskedasticity corrected predictive models using quantile regression

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  • Nath, Harmindar B.
  • Brooks, Robert D.

Abstract

This paper examines the superiority-claim of the GARCH based measure in resolving the ‘idiosyncratic risk–return puzzle’ using Australian data. The least squares and the quantile regressions of stock-returns on lagged idiosyncratic-volatility estimated from daily data using two measures (including GARCH) fail to support such claim. The quantile regression estimation reveals the risk–return relationship to be quantile dependent; it is parabolic but significant only at the extreme quantiles. The parabolic-form is convex (concave) at the lower (upper) quantiles of the returns' conditional distribution. This changing relationship-form reflects uncertainty in predicting returns. Moreover, the idiosyncratic risk–return puzzle is a model specification problem.

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  • Nath, Harmindar B. & Brooks, Robert D., 2015. "Assessing the idiosyncratic risk and stock returns relation in heteroskedasticity corrected predictive models using quantile regression," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 94-111.
    • Handle: RePEc:eee:reveco:v:38:y:2015:i:c:p:94-111
    DOI: 10.1016/j.iref.2014.12.012
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    Cited by:

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    5. Harmindar B. Nath & Vasilis Sarafidis, 2017. "Does persistence in idiosyncratic risk proxy return-reversals?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 2(8), pages 27-53, October.
    6. Hussain Shahzad, Syed Jawad & Raza, Naveed & Shahbaz, Muhammad & Ali, Azwadi, 2017. "Dependence of stock markets with gold and bonds under bullish and bearish market states," Resources Policy, Elsevier, vol. 52(C), pages 308-319.
    7. Chen, Yi-Ling & Wang, Ming-Chun & Lin, Jun-Biao & Huang, Ming-Chih, 2022. "How financial crises affect the relationship between idiosyncratic volatility and stock returns," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 96-113.
    8. Tariq Aziz & Valeed Ahmad Ansari, 2016. "Idiosyncratic risk and stock returns: a quantile regression approach," Proceedings of Economics and Finance Conferences 3205769, International Institute of Social and Economic Sciences.
    9. Chevapatrakul, Thanaset & Xu, Zhongxiang & Yao, Kai, 2019. "The impact of tail risk on stock market returns: The role of market sentiment," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 289-301.
    10. Mohamed S. Ahmed & John A. Doukas, 2021. "Revisiting disposition effect and momentum: a quantile regression perspective," Review of Quantitative Finance and Accounting, Springer, vol. 56(3), pages 1087-1128, April.

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    More about this item

    Keywords

    Idiosyncratic risk; Quantile regression; GARCH model; Panel data;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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