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Portfolio Optimization for Extreme Risks with Maximum Diversification: An Empirical Analysis

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  • Navya Jayesh Mehta

    (Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Fan Yang

    (Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

Heavy tailedness and interconnectedness widely exist in stock returns and large insurance claims, which contributes to huge losses for financial institutions. Diversification ratio (DR) measures the degree of diversification using the Value-at-Risk, which is known to capture extreme risks better than variance. The portfolio optimization strategy based on DR maximizes the effect of diversification for extreme risks. In this paper, we empirically examine the DR strategy by using more than 350 S&P 500 stocks under the assumption that the stock losses are modeled with a flexible multivariate heavy-tailed model. This assumption is verified empirically. The performance of DR strategy is compared with four benchmark strategies: equally weighted portfolio, minimum-variance portfolio, extreme risk index portfolio, and most diversified portfolio. The performance of comparison includes annualized portfolio return, modified Sharpe ratio, maximum drawdown, portfolio concentration, portfolio turnover, and the degree of diversification. DR outperforms other strategies. In particular, DR shows the highest return and maintains the highest level of diversification during the global financial crisis of 2007–2009.

Suggested Citation

  • Navya Jayesh Mehta & Fan Yang, 2022. "Portfolio Optimization for Extreme Risks with Maximum Diversification: An Empirical Analysis," Risks, MDPI, vol. 10(5), pages 1-26, May.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:5:p:101-:d:813252
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    References listed on IDEAS

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    1. Richardson, Matthew & Smith, Tom, 1993. "A Test for Multivariate Normality in Stock Returns," The Journal of Business, University of Chicago Press, vol. 66(2), pages 295-321, April.
    2. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2012. "Assessing the systemic risk of a heterogeneous portfolio of banks during the recent financial crisis," Journal of Financial Stability, Elsevier, vol. 8(3), pages 193-205.
    3. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2009. "A framework for assessing the systemic risk of major financial institutions," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2036-2049, November.
    4. Degen, Matthias & Lambrigger, Dominik D. & Segers, Johan, 2010. "Risk concentration and diversification: Second-order properties," LIDAM Reprints ISBA 2010011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Loretan, Mico & Phillips, Peter C. B., 1994. "Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets," Journal of Empirical Finance, Elsevier, vol. 1(2), pages 211-248, January.
    6. Orlando Rivera-Escobar & John Willmer Escobar & Diego Fernando Manotas, 2022. "Measurement of Systemic Risk in the Colombian Banking Sector," Risks, MDPI, vol. 10(1), pages 1-27, January.
    7. Degen, Matthias & Lambrigger, Dominik D. & Segers, Johan, 2010. "Risk concentration and diversification: Second-order properties," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 541-546, June.
    8. Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
    9. Mao, Tiantian & Yang, Fan, 2015. "Risk concentration based on Expectiles for extreme risks under FGM copula," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 429-439.
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    1. Argimiro Arratia & Henryk Gzyl & Silvia Mayoral, 2024. "How to Keep Your Portfolio Close in Risk and Diversification to a Desired Benchmark," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1489-1505, September.

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