Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-28T04:24:47.228Z Has data issue: false hasContentIssue false

AN EXTREME-VALUE THEORY APPROXIMATION SCHEME IN REINSURANCE AND INSURANCE-LINKED SECURITIES

Published online by Cambridge University Press:  03 July 2018

Rom Aviv*
Affiliation:
IBI ILS Partners LTD., Shalom Tower, 9 Ahad Ha'Am St, 28th floor, Tel-Aviv 6129101, Israel E-Mail: [email protected]

Abstract

We establish a “top-down” approximation scheme to approximate loss distributions of reinsurance products and Insurance-Linked Securities based on three input parameters, namely the Attachment Probability, Expected Loss and Exhaustion Probability. Our method is rigorously derived by utilizing a classical result from Extreme-Value Theory, the Pickands–Balkema–de Haan theorem. The robustness of the scheme is demonstrated by proving sharp error-bounds for the approximated curves with respect to the supremum and L2 norms. The practical implications of our findings are examined by applying it to Industry Loss Warranties: the method performs very accurately for each transaction. Our approach can be used in a variety of applications such as vendor model blending, portfolio optimization and premium calculation.

Type
Research Article
Copyright
Copyright © Astin Bulletin 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balkema, A.A. and De Haan, L. (1974) Residual life time at great age. The Annals of Probability, 2, 792804.Google Scholar
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) Drawdown measure in portfolio optimization. International Journal of Theoretical and Applied Finance, 8 (1), 1358.Google Scholar
Embrechts, P. and Meister, S. (1997) Pricing insurance derivatives, the case of CAT-futures. Geneva Papers: Etudes et Dossiers, special issue on Insurance and the State of the Art in Cat Bond Pricing, 278, 1929.Google Scholar
Embrechts, P., Klueppelberg, C. and Mikosch, T. (1997) Modelling Extremal Events: For Insurance and Finance. Berlin: Springer-Verlag.Google Scholar
Hainaut, D. (2010) Pricing of a catastrophe bond related to seasonal claims. Actuarial and Financial Mathematics Conference, Interplay between Finance and Insurance, 17–32.Google Scholar
Lane, M.N. (2000) Pricing risk transfer transactions. ASTIN Bulletin, 30 (2), 259293.Google Scholar
McNeil, A.J., Frey, R. and Embrechts, P. (2010) Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton, NJ: Princeton University Press.Google Scholar
Morales, M. (2004) On an approximation for the surplus process using extreme value theory: Applications in ruin theory and reinsurance pricing. North American Actuarial Journal, 8, 4466.Google Scholar
Pickands, J. (1975) Statistical inference using extreme order statistics. Annals of Statistics, 3, 119131.Google Scholar
Rockafellar, R.T. and Uryasev, S. (2000) Optimization of conditional value-at-risk. Journal of Risk, 2, 2142.Google Scholar
Rootzen, H. and Tajvidi, N. (1997) Extreme value statistics and wind storm losses: A case study. Scandinavian Actuarial Journal, 1, 7094.Google Scholar
Vandewalle, B. and Beirlant, J. (2006) On univariate extreme value statistics and the estimation of reinsurance premiums. Insurance: Mathematics and Economics, 38 (3), 441459.Google Scholar
Wang, S.S. (2004) Cat bond pricing using probability transforms. Proceedings of the 1995 Bowles Symposium on Securitization of Risk, Georgia State University Atlanta, Society of Actuaries, Monograph M-FI97-1, 15–26.Google Scholar
Zimbidis, A.A., Frangos, N.E. and Pantelous, A.A. (2007) Modeling earthquake risk via extreme value theory and pricing the respective catastrophe bonds. ASTIN Bulletin, 37 (1), (2007), 163184.Google Scholar