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Idiosyncratic risk and stock returns: a quantile regression approach

Author

Listed:
  • Tariq Aziz

    (Department of Business Administration, Aligarh Muslim University)

  • Valeed Ahmad Ansari

    (Department of Business Administration, Aligarh Muslim University)

Abstract

The relation between idiosyncratic risk and stock returns is currently a topic of debate in the academic literature. So far the evidence regarding the relation is mixed. This study aims to investigate the cross-sectional relation between idiosyncratic risk and stock returns in the Indian stock market employing quantile regressions. Using quantile regressions, this study demonstrates that idiosyncratic volatility and stock returns relation is quantile dependent. The relation between idiosyncratic volatility and stock returns is parabolic. The high idiosyncratic risk is associated with high (low) excess returns at the upper (lower) quantile of the conditional distribution. This partially explains the inconclusive evidence on the idiosyncratic volatility and the stock returns relation in the literature.

Suggested Citation

  • 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.
  • Handle: RePEc:sek:iefpro:3205769
    as

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    File URL: https://iises.net/proceedings/5th-economics-finance-conference-miami/table-of-content/detail?cid=32&iid=002&rid=5769
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    References listed on IDEAS

    as
    1. Aziz, Tariq & Ansari, Valeed Ahmad, 2014. "Size and value premiums in the Indian stock market," MPRA Paper 60451, University Library of Munich, Germany.
    2. 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.
    3. Michael E. Drew & Mirela Malin & Tony Naughton & Madhu Veeraraghavan, 2006. "Idiosyncratic volatility and security returns: evidence from Germany and United Kingdom," Studies in Economics and Finance, Emerald Group Publishing, vol. 23(2), pages 80-93, June.
    4. Merton, Robert C, 1987. "A Simple Model of Capital Market Equilibrium with Incomplete Information," Journal of Finance, American Finance Association, vol. 42(3), pages 483-510, July.
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    6. 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.
    7. Das, Sudipta, 2015. "Empirical evidence of conditional asset pricing in the Indian stock market," Economic Systems, Elsevier, vol. 39(2), pages 225-239.
    8. 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.
    9. Levy, Haim, 1978. "Equilibrium in an Imperfect Market: A Constraint on the Number of Securities in the Portfolio," American Economic Review, American Economic Association, vol. 68(4), pages 643-658, September.
    10. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    idiosyncratic volatility; quantile regression; asset pricing; emerging markets; India;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • 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

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