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A model-independent measure of aggregate idiosyncratic risk

Author

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  • Bali, Turan G.
  • Cakici, Nusret
  • Levy, Haim

Abstract

This paper introduces a model-independent measure of aggregate idiosyncratic risk, which does not require estimation of market betas or correlations and is based on the concept of gain from portfolio diversification. The statistical results and graphical analyses provide strong evidence that there are significant level and trend differences between the average idiosyncratic volatility measures of Campbell et al. [Campbell, J.Y., Lettau, M., Malkiel, B.G., and Xu, Y., 2001, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, Journal of Finance 56, 1-43.] and the new methodology. Although both approaches indicate a noticeable increase in the firm-level idiosyncratic risk, the volatility measure of CLMX is greater and has a stronger upward trend than the new idiosyncratic volatility measure. For both measures of idiosyncratic risk, the upward trend is found to be stronger for smaller, lower-priced, and younger firms. The analytical and empirical results show that the significant upward trend in the differences of the two idiosyncratic volatility measures is related to the increase in the cross-sectional dispersion of the volatility of individual stocks.

Suggested Citation

  • Bali, Turan G. & Cakici, Nusret & Levy, Haim, 2008. "A model-independent measure of aggregate idiosyncratic risk," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 878-896, December.
    • Handle: RePEc:eee:empfin:v:15:y:2008:i:5:p:878-896
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    References listed on IDEAS

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    Cited by:

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    2. Vozlyublennaia, Nadia & Meshcheryakov, Artem, 2014. "Dynamic correlation structure and security risk," Journal of Economics and Business, Elsevier, vol. 73(C), pages 48-64.
    3. Bekaert, Geert & Hodrick, Robert J. & Zhang, Xiaoyan, 2012. "Aggregate Idiosyncratic Volatility," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(6), pages 1155-1185, December.
    4. Nusret Cakici & Isil Erol & Dogan Tirtiroglu, 2014. "Tracking the Evolution of Idiosyncratic Risk and Cross-Sectional Expected Returns for US REITs," The Journal of Real Estate Finance and Economics, Springer, vol. 48(3), pages 415-440, April.
    5. Shahzad, Farrukh & Fareed, Zeeshan & Wang, Zhenkun & Shah, Syed Ghulam Meran, 2020. "Do idiosyncratic risk, market risk, and total risk matter during different firm life cycle stages?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    6. Nam, Kiseok & Khaksari, Shahriar & Kang, Moonsoo, 2017. "Trend in aggregate idiosyncratic volatility," Review of Financial Economics, Elsevier, vol. 35(C), pages 11-28.
    7. Sina Ehsani & Donald Lien, 2015. "Effects of Passive Intensity on Aggregate Price Dynamics," The Financial Review, Eastern Finance Association, vol. 50(3), pages 363-391, August.
    8. Mehmet Umutlu & Levent Akdeniz & Aslihan Altay-Salih, 2013. "Foreign Equity Trading and Average Stock-return Volatility," The World Economy, Wiley Blackwell, vol. 36(9), pages 1209-1228, September.
    9. Semaan, Elias & Drake, Pamela Peterson, 2016. "TARP and the long-term perception of risk," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 216-235.
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    12. Vozlyublennaia, Nadia, 2013. "Do firm characteristics matter for the dynamics of idiosyncratic risk?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 35-46.

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    More about this item

    Keywords

    G10 G11 C13 Idiosyncratic risk Total risk Average stock risk Stock market volatility Stock returns;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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