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Multivariate Counting Processes: Copulas and Beyond

Published online by Cambridge University Press:  17 April 2015

Nicole Bäuerle
Affiliation:
Institut für Mathematische Stochastik, Universität Hannover, Postfach 60 09, D-30060 Hannover, E-mail: [email protected], [email protected]
Rudolf Grübel
Affiliation:
Institut für Mathematische Stochastik, Universität Hannover, Postfach 60 09, D-30060 Hannover, E-mail: [email protected], [email protected]
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Abstract

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Multivariate stochastic processes with Poisson marginals are of interest in insurance and finance; they can be used to model the joint behaviour of several claim arrival processes, for example. We discuss various methods for the construction of such models, with particular emphasis on the use of copulas. An important class of multivariate counting processes with Poisson marginals arises if the events of a background Poisson process with constant intensity are moved forward in time by a random amount and possibly deleted; here we think of the events of the background process as triggering later claims in different categories. We discuss structural aspects of these models, their dependence properties together with stochastic order aspects, and also some related computational issues. Various actuarial applications are indicated.

Type
Articles
Copyright
Copyright © ASTIN Bulletin 2005

References

Christofides, T.C. and Vaggelatou, E.A. (2004) Connection between supermodular ordering and positive/negative association. Journal of Multivariate Analysis, 88, 138151.CrossRefGoogle Scholar
Cont, R. and Tankov, P. (2004) Financial Modelling with Jump Processes. Chapman and Hall, Boca Raton.Google Scholar
Cox, D.R. and Isham, V. (1980) Point Processes. Chapman and Hall, London.Google Scholar
Daley, D.J. and Vere-Jones, D. (1988) An Introduction to the Theory of Point Processes. Springer, New York.Google Scholar
Esary, J.D., Proschan, F. and Walkup, D.W. (1967) Association of random variables with applications. Ann. Math. Stat., 38, 14661474.CrossRefGoogle Scholar
Joe, H. (1997) Multivariate Models and Dependence Concepts. Chapman and Hall, London.Google Scholar
Johnson, N.L., Kotz, S. and Balakrishnan, N. (1997) Discrete Multivariate Distributions. Wiley, New York.Google Scholar
Lindskog, F. and Mcneil, A.J. (2003) Common Poisson shock models: applications to insurance and credit risk modelling. ASTIN Bulletin, 33, 209238.CrossRefGoogle Scholar
McNeil, A.J., Frey, R. and Embrechts, P. (2004) Quantitative Risk Management: Concepts, Techniques and Tools. Book in preparation.Google Scholar
Müller, A. and Stoyan, D. (2002) Comparison Methods for Stochastic Models and Risks. Wiley, New York.Google Scholar
Nelsen, R.B. (1999) An Introduction to Copulas. Lecture Notes in Statistics 139. Springer, New York.CrossRefGoogle Scholar
Pfeifer, D. and Neslehova, J. (2004) Modeling and generating dependent risk processes for IRM and DFA. ASTIN Bulletin, 34, 333360.CrossRefGoogle Scholar
Resnick, S.I. (1987) Extreme Values, Regular Variation and Point Processes. Springer, New York.CrossRefGoogle Scholar