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Risk concentration and diversification: Second-order properties

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  • Degen, Matthias
  • Lambrigger, Dominik D.
  • Segers, Johan

Abstract

The quantification of diversification benefits due to risk aggregation plays a prominent role in the (regulatory) capital management of large firms within the financial industry. However, the complexity of today's risk landscape makes a quantifiable reduction of risk concentration a challenging task. In the present paper we discuss some of the issues that may arise. The theory of second-order regular variation and second-order subexponentiality provides the ideal methodological framework to derive second-order approximations for the risk concentration and the diversification benefit.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:insuma:v:46:y:2010:i:3:p:541-546
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    Cited by:

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    2. Bikramjit Das & Marie Kratz, 2017. "Diversification benefits under multivariate second order regular variation," Working Papers hal-01520655, HAL.
    3. 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.
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    5. Mao, Tiantian & Lv, Wenhua & Hu, Taizhong, 2012. "Second-order expansions of the risk concentration based on CTE," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 449-456.
    6. Das, Bikramjit & Kratz, Marie, 2017. "Diversification benefits under multivariate second order regular variation," ESSEC Working Papers WP1706, ESSEC Research Center, ESSEC Business School.
    7. Cui, Hengxin & Tan, Ken Seng & Yang, Fan & Zhou, Chen, 2022. "Asymptotic analysis of portfolio diversification," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 302-325.
    8. Hua, Lei & Joe, Harry, 2011. "Second order regular variation and conditional tail expectation of multiple risks," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 537-546.
    9. Tong, Bin & Wu, Chongfeng & Xu, Weidong, 2012. "Risk concentration of aggregated dependent risks: The second-order properties," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 139-149.
    10. Lv, Wenhua & Pan, Xiaoqing & Hu, Taizhong, 2013. "Asymptotics of the risk concentration based on the tail distortion risk measure," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2703-2710.
    11. Peng, Zuoxiang & Liao, Xin, 2015. "Second-order asymptotics for convolution of distributions with light tails," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 199-208.
    12. Takashi Kato, 2017. "Theoretical Sensitivity Analysis For Quantitative Operational Risk Management," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(05), pages 1-23, August.
    13. 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.
    14. Guillén, Montserrat & Sarabia, José María & Prieto, Faustino, 2013. "Simple risk measure calculations for sums of positive random variables," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 273-280.

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