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New risk measures for variance distortion and catastrophic financial risk measures

https://doi.org/10.26794/2587-5671-2021-25-6-165-184

Abstract

In recent years, expectation distortion risk measures have been widely used in financial and insurance applications due to their attractive properties. The author introduced two new classes of financial risk measures “VaR raised to the power of t” and “ES raised to the power of t” in his works and also investigated the issue of the belonging of these risk measures to the class of risk measures of expectation distortion, and described the corresponding distortion functions. The aim of this study is to introduce a new concept of variance distortion risk measures, which opens up a significant area for investigating the properties of these risk measures that may be useful in applications. The paper proposes a method of finding new variance distortion risk measures that can be used to acquire risk measures with special properties. As a result of the study, it was found that the class of risk measures of variance distortion includes risk measures that are in a certain way related to “VaR raised to the power of t” and “ES raised to the power of t” measures. The article describes the composite method for constructing new variance distortion functions and corresponding distortion risk measures. This method is used to build a large set of examples of variance distortion risk measures that can be used in assessing certain financial risks of a catastrophic nature. The author concludes that the study of the variance distortion risk measures introduced in this paper can be used both for the development of theoretical risk management methods and in the practice of business risk management in assessing unlikely risks of high catastrophe.

About the Author

V. B. Minasyan
Russian Presidential Academy of National Economy and Public Administration
Russian Federation

Vigen B. Minasyan — Cand. Sci. (Phys.-Math.), Assoc. Prof., Head of Limitovskii Сorporate Finance, Investment Design and Evaluation Department, Higher School of Finance and Management

Moscow



References

1. Crouhy M., Galai D., Mark R. The essentials of risk management. New York: McGraw-Hill Book Co.; 2006. 414 p. (Russ. ed.: Crouhy M., Galai D., Mark R. Osnovy risk-menedzhmenta. Moscow: Urait; 2006. 414 p.).

2. Hull J. C. Risk management and financial institutions. New York: Pearson Education International; 2007. 576 p.

3. Jorion P. Value at risk: The new benchmark for managing financial risk. New York: McGraw-Hill Education; 2007. 624 p.

4. Wang S. S. A class of distortion operators for pricing financial and insurance risks. The Journal of Risk and Insurance. 2000;67(1):15–36. DOI: 10.2307/253675

5. Szego G., ed. Risk measures for the 21st century. Chichester: John Wiley & Sons, Ltd; 2004. 491 p.

6. Denneberg D. Non-additive measure and integral. Dordrecht: Kluwer Academic Publishers; 1994. 178 p. (Theory and Decision Library B. Vol. 27). DOI: 10.1007/978–94–017–2434–0

7. Wang S., Dhaene J. Comonotonicity, correlation order and premium principles. Insurance: Mathematics and Economics. 1998;22(3):235–242. DOI: 10.1016/S 0167–6687(97)00040–1

8. Artzner P., Delbaen F., Eber J.-M., Heath D. Coherent measures of risk. Mathematical Finance. 1999;9(3):203–228. DOI: 10.1111/1467–9965.00068

9. Denuit M., Dhaene J., Goovaerts M., Kaas R. Actuarial theory for dependent risks: Measures, orders and models. Hoboken, NJ: John Wiley & Sons, Ltd; 2005. 440 p. DOI: 10.1002/0470016450

10. Zhu L., Li H. Tail distortion risk and its asymptotic analysis. Insurance: Mathematics and Economics. 2012;51(1):115–121. DOI: 10.1016/j.insmatheco.2012.03.010

11. Yang F. First- and second-order asymptotics for the tail distortion risk measure of extreme risks. Communications in Statistics — Theory and Methods. 2015;44(3):520–532. DOI: 10.1080/03610926.2012.751116

12. Yin C., Zhu D. New class of distortion risk measures and their tail asymptotics with emphasis on Va R. Journal of Financial Risk Management. 2018;7(1):12–38. DOI: 10.4236/jfrm.2018.71002

13. Belles-Sampera J., Guillén M., Santolino M. Beyond value-at-risk: GlueVaR distortion risk measures. Risk Analysis. 2014;34(1):121–134. DOI: 10.1111/risa.12080

14. Belles-Sampera J., Guillén M., Santolino M. GlueVaR risk measures in capital allocation applications. Insurance: Mathematics and Economics. 2014;58:132–137. DOI: 10.1016/j.insmatheco.2014.06.014

15. Minasyan V. B. New ways to measure catastrophic financial risks: “VaR to the power of t” measures and how to calculate them”. Finansy: teoriya i praktika = Finance: Theory and Practice. 2020;24(3):92–109. DOI: 10.26794/2587–5671–2020–24–3–92–109

16. Minasyan V. B. New risk measures “VaR to the power of t” and “ES to the power of t” and distortion risk measures. Finansy: teoriya i praktika = Finance: Theory and Practice. 2020;24(6):92–107. DOI: 10.26794/2587–5671–2020–24–6–92–107

17. Dhaene J., Kukush A., Linders D., Tang Q. Remarks on quantiles and distortion risk measures. European Actuarial Journal. 2012;2(2):319–328. DOI: 10.1007/s13385–012–0058–0

18. Wang S. S. Premium calculation by transforming the layer premium density. ASTIN Bulletin. 1996;26(1):71–92. DOI: 10.2143/AST.26.1.563234

19. Corless R. M., Gonnet G. H., Hare D. E., Jeffrey D. J., Knuth D. E. On the Lambert W function. Advances in Computational Mathematics. 1996;5:329–359. DOI: 10.1007/BF02124750


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For citations:


Minasyan V.B. New risk measures for variance distortion and catastrophic financial risk measures. Finance: Theory and Practice. 2021;25(6):165-184. https://doi.org/10.26794/2587-5671-2021-25-6-165-184

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ISSN 2587-5671 (Print)
ISSN 2587-7089 (Online)