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Analytical Bounds for two Value-at-Risk Functionals

Published online by Cambridge University Press:  29 August 2014

Werner Hürlimann*
Affiliation:
Value and Risk Management, Winterthur Life and Pensions, Postfach 300 – CH-8401 Winterthur – Switzerland, Tel. +41-52-2615861, E-mail: [email protected]
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Abstract

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Based on the notions of value-at-risk and conditional value-at-risk, we consider two functionals, abbreviated VaR and CVaR, which represent the economic risk capital required to operate a risky business over some time period when only a small probability of loss is tolerated. These functionals are consistent with the risk preferences of profit-seeking (and risk averse) decision makers and preserve the stochastic dominance order (and the stop-loss order). This result is used to bound the VaR and CVaR functionals by determining their maximal values over the set of all loss and profit functions with fixed first few moments. The evaluation of CVaR for the aggregate loss of portfolios is also discussed. The results of VaR and CVaR calculations are illustrated and compared at some typical situations of general interest.

Type
Articles
Copyright
Copyright © International Actuarial Association 2002

References

Acerbi, C. (2001) Risk aversion and coherent risk measures: a spectral representation theorem. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar
Acerbi, C. and Tasche, D. (2001a) On the coherence of expected shortfall. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar
Acerbi, C. and Tasche, D. (2001b). Expected shortfall: a natural coherent alternative to value-at-risk. Working paper http://www.gloriamundi.org/var/wps.html.CrossRefGoogle Scholar
Artzner, P. (1999) Application of coherent risk measures to capital requirements in insurance. North American Actuarial Journal 3(2), 1125.CrossRefGoogle Scholar
Artzner, P., Delbaen, E., Eber, J.-M. and Heath, D. (1997) Thinking coherently. RISK 10(11), 6871.Google Scholar
Artzner, P., Delbaen, E., Eber, J.-M. and Heath, D. (1999) Coherent measures of risk. Mathematical Finance 9(3), 203–28.CrossRefGoogle Scholar
Bertsimas, D., Lauprete, G.J. and Samarov, A. (2000) Shortfall as a risk measure: properties, optimization and applications. Working paper, Sloan School of Management, MIT.Google Scholar
Cossette, H., Denuit, M., Dhaene, J. and Marceau, E. (2001) Stochastic approximations of present value functions. Bulletin of the Swiss Association of Actuaries, 1528.Google Scholar
Delbaen, F. (2000) Coherent risk measures. Blätter der Deutschen Gesellschaft für Versicherungsmathematik XXIV(4), 733–39.Google Scholar
Denuit, M., Genest, C. and Marceau, E. (1999) Stochastic bounds on sums of dependent risks. Insurance: Mathematics and Economics 25, 85104.Google Scholar
De Vylder, F. (1996) Advanced Risk Theory. A Self-Contained Introduction. Editions de l'Université de Bruxelles, Collection Actuariat.Google Scholar
Dhaene, J., Wang, S., Young, V. and Goovaerts, M.J. (2000) Comonotonicity and maximal stop-loss premiums. Bulletin of the Swiss Association of Actuaries, 99113.Google Scholar
Durrleman, V., Nikeghbali, A. and Roncalli, T. (2000) How to get bounds for distribution convolutions? A simulation study and an application to risk management. Working paper, available from http://gro.creditlyonnais.fr/content/rd/wp.html.CrossRefGoogle Scholar
Embrechts, P. (1995) A survival kit to quantile estimation. UBS Quant Workshop, Zürich.Google Scholar
Embrechts, P., Höing, A. and Juri, A. (2001) Using copulae to bound the value-at-risk for functions of dependents risks. Preprint ETHZ, available from http://www.math.ethz.ch/~embrechts.Google Scholar
Frank, M.J., Nelsen, R.B., Schweizer, B. (1987) Best-possible bounds on the distribution of a sum – a problem of Kolmogorov. Probability Theory and Related Fields 74, 199211.CrossRefGoogle Scholar
Guiard, V. (1980) Robustheit I. Probleme der angewandten Statistik, Heft 4. FZ für Tierproduktion Dummerstorf-Rostock.Google Scholar
Heilmann, W.-R. (1987) A premium calculation principle for large risks. In: Operations Research Proceedings 1986, 342–51.Google Scholar
Hürlimann, W. (1994) Splitting risk and premium calculation. Bulletin of the Swiss Association of Actuaries, 229–49.Google Scholar
Hürlimann, W. (1996) Improved analytical bounds for some risk quantities. ASTIN Bulletin 26(2), 185–99.CrossRefGoogle Scholar
Hürlimann, W. (1997a) Best bounds for expected financial payoffs (I) algorithmic evaluation. Journal of Computational and Applied Mathematics 82, 199212.CrossRefGoogle Scholar
Hürlimann, W. (1997b) Best bounds for expected financial payoffs (II) applications. Journal of Computational and Applied Mathematics 82, 213227.CrossRefGoogle Scholar
Hürlimann, W. (1998a) On distribution-free safe layer-additive pricing. Insurance: Mathematics and Economics 22, 277–85.Google Scholar
Hürlimann, W. (1998b) Extremal Moment Methods and Stochastic Orders – Application in Actuarial Science. Research monograph (available from the author).Google Scholar
Hürlimann, W. (1999) Bounds for actuarial present values under the fractional independence age assumption (with discussions). North American Actuarial Journal 3(3), 7084.CrossRefGoogle Scholar
Hürlimann, W. (2000) Higher-degree stop-loss orders and right-tail risk. Manuscript (available from the author).Google Scholar
Hürlimann, W. (2001a) Distribution-free comparison of pricing principles. Insurance: Mathematics and Economics 28, 351360.Google Scholar
Hürlimann, W. (2001b) Analytical evaluation of economic risk capital for portfolios of gamma risks. ASTIN Bulletin 31, 107122.CrossRefGoogle Scholar
Hürlimann, W. (2001c) Financial data analysis with two symmetric distributions. First Prize in the Gunnar Benktander ASTIN Award Competition 2000. ASTIN Bulletin 31, 187211.CrossRefGoogle Scholar
Hürlimann, W. (2001d) Analytical evaluation of economic risk capital and diversification using linear Spearman copulas. Working paper, available from http://www.gloriamundi.org/var/wps.html.Google Scholar
Hürlimann, W. (2001e) Conditional value-at-risk bounds for compound Poisson risks and a normal approximation. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar
Jansen, K., Haezendonck, J. and Goovaerts, M.J. (1986) Analytical upper bounds on stop-loss premiums in case of known moments up to the fourth order. Insurance: Mathematics and Economics 5, 315334.Google Scholar
Kaas, R., Heerwaarden, van A.E. and Goovaerts, M.J. (1994) Ordering of Actuarial Risks. CAIRE Education Series 1, Brussels.Google Scholar
Karlin, S. and Studden, W.J. (1966) Tchebycheff systems: with applications in Analysis and Statistics. Interscience Publishers, J. Wiley. New York.Google Scholar
Kusuoka, S. (2001) On law invariant coherent risk measures. In: Kusuoka, S. (Ed.). Advances in Mathematical Economics, 3, 8395. Springer.CrossRefGoogle Scholar
Li, D.X. (1999) Value-at-risk based on the volatility, skewness and kurtosis. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar
Luciano, E. and Marena, M. (2001) Value at Risk bounds for portfolios of non-normal returns. Working paper http://www.gloriamundi.org/var/wps.html.CrossRefGoogle Scholar
Makarov, G.D. (1981) Estimates for the distribution function of a sum of two random variables when the marginal distributions are fixed. Theory of Probability and its Applications 26, 803806.CrossRefGoogle Scholar
Mammana, C. (1954) Sul Problema Algebraico dei Momenti. Scuola Norma Sup. Pisa 8, 133140.Google Scholar
Pearson, K. (1916) Mathematical contributions to the theory of evolution XIX; second suppl. to a memoir on skew variation. Phil. Trans. Royal Soc. London, Ser. A 216, 432.Google Scholar
Pflug, G. (2000) Some remarks on the value-at-risk and the conditional value-at-risk. In: Urasev, S. (Ed.). Probabilistic Constrained Optimization: Methodology and Applications. Kluwer Academic Publishers.CrossRefGoogle Scholar
Rockafellar, R.T. and Uryasev, S. (2000) Optimization of conditional value-at-risk. The Journal of Risk 2(3), 2141 (also available from http://www.ise.ufl.edu/uryasev).CrossRefGoogle Scholar
Rockafellar, R.T. and Uryasev, S. (2001) Conditional value-at-risk for general loss distributions Appears in Journal of Banking and Finance.Google Scholar
Rüschendorf, L. (1982) Random variables with maximum sums. Advances in Applied Probability 14, 623–32.CrossRefGoogle Scholar
Shaked, M. and Shanthikumar, J.G. (1994) Stochastic orders and their applications. Academic Press, New York.Google Scholar
Stoyan, D. (1977) Qualitative Eigenschaften und Abschatzungen Stochastischer Modelle. Akademie-Verlag, Berlin. (English version (1983). Comparison Methods for Queues and Other Stochastic Models. J. Wiley, New York).CrossRefGoogle Scholar
Testuri, C.E. and Uryasev, S. (2000) On relation between expected regret and conditional value-at-risk. Working paper http://www.ise.ufl.edu/uryasev.CrossRefGoogle Scholar
Wang, S. (1998) An actuarial index of the right-tail index. North American Actuarial Journal 2(2), 88101.CrossRefGoogle Scholar
Wilkins, J.E. (1944) A note on skewness and kurtosis. Annals of Mathematical Statistics 15, 333–35.CrossRefGoogle Scholar
Wirch, J.L. (1999) Raising value at risk. North American Actuarial Journal 3(2), 106115.CrossRefGoogle Scholar
Wirch, J.L. and Hardy, M.R. (1999) A synthesis of risk measures for capital adequacy. Insurance: Mathematics and Economics 25, 337–47.Google Scholar
Yamai, Y. and Yoshiba, T. (2001a) Comparative analyses of expected shortfall and value-at-risk: their estimation error, decomposition, and optimization. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar
Yamai, Y. and Yoshiba, T. (2001b) On the validity of value-at-risk: comparative analyses with expected shortfall. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar
Yoshiba, T. and Yamai, Y. (2001) Comparative analyses of expected shortfall and value-at-risk (2): expected utility maximization and tail risk. Working paper http://www.gloriamundi.org/var/wps.html.Google Scholar