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Nonlinearly weighted convex risk measure and its application

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  • Chen, Zhiping
  • Yang, Li
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    Abstract

    We propose a new class of risk measures which satisfy convexity and monotonicity, two well-accepted axioms a reasonable and realistic risk measure should satisfy. Through a nonlinear weight function, the new measure can flexibly reflect the investor's degree of risk aversion, and can control the fat-tail phenomenon of the loss distribution. A realistic portfolio selection model with typical market frictions taken into account is established based on the new measure. Real data from the Chinese stock markets and American stock markets are used for empirical comparison of the new risk measure with the expected shortfall risk measure. The in-sample and out-of-sample empirical results show that the new risk measure and the corresponding portfolio selection model can not only reflect the investor's risk-averse attitude and the impact of different trading constraints, but can find robust optimal portfolios, which are superior to the corresponding optimal portfolios obtained under the expected shortfall risk measure.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 35 (2011)
    Issue (Month): 7 (July)
    Pages: 1777-1793

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    Handle: RePEc:eee:jbfina:v:35:y:2011:i:7:p:1777-1793

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Expected shortfall Market frictions Portfolio optimization Performance ratio;

    References

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    1. Pérignon, Christophe & Smith, Daniel R., 2010. "Diversification and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 55-66, January.
    2. Silver, M S, 1985. "On the Measurement of Changes in Aggregate Concentration, Market Concentration and Diversification," Journal of Industrial Economics, Wiley Blackwell, vol. 33(3), pages 349-52, March.
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    4. Chung, Dennis & Hrazdil, Karel, 2010. "Liquidity and market efficiency: A large sample study," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2346-2357, October.
    5. Carlo Acerbi & Dirk Tasche, 2001. "On the coherence of Expected Shortfall," Papers cond-mat/0104295, arXiv.org, revised May 2002.
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    7. Hwang, Soosung & Satchell, Steve E., 2010. "How loss averse are investors in financial markets?," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2425-2438, October.
    8. Acerbi, Carlo, 2002. "Spectral measures of risk: A coherent representation of subjective risk aversion," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1505-1518, July.
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    10. Fischer, T., 2003. "Risk capital allocation by coherent risk measures based on one-sided moments," Insurance: Mathematics and Economics, Elsevier, vol. 32(1), pages 135-146, February.
    11. Inui, Koji & Kijima, Masaaki, 2005. "On the significance of expected shortfall as a coherent risk measure," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 853-864, April.
    12. Farinelli, Simone & Ferreira, Manuel & Rossello, Damiano & Thoeny, Markus & Tibiletti, Luisa, 2008. "Beyond Sharpe ratio: Optimal asset allocation using different performance ratios," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2057-2063, October.
    13. Ding, Bill & Shawky, Hany A. & Tian, Jianbo, 2009. "Liquidity shocks, size and the relative performance of hedge fund strategies," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 883-891, May.
    14. Hans Föllmer & Alexander Schied, 2002. "Convex measures of risk and trading constraints," Finance and Stochastics, Springer, vol. 6(4), pages 429-447.
    15. Georg Pflug & Nancy Wozabal, 2010. "Asymptotic distribution of law-invariant risk functionals," Finance and Stochastics, Springer, vol. 14(3), pages 397-418, September.
    16. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 362-377, February.
    17. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:
    1. Chen, Zhi-ping & Li, Gang & Guo, Ju-e, 2013. "Optimal investment policy in the time consistent mean–variance formulation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 145-156.

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