Hostname: page-component-7479d7b7d-qlrfm Total loading time: 0 Render date: 2024-07-13T10:34:38.107Z Has data issue: false hasContentIssue false

Multivariate risk processes with interacting intensities

Published online by Cambridge University Press:  01 July 2016

Nicole Bäuerle*
Affiliation:
Universität Karlsruhe (TH)
Rudolf Grübel*
Affiliation:
Leibniz Universität Hannover
*
Postal address: Institut für Stochastik, Universität Karlsruhe (TH), D-76128 Karlsruhe, Germany.
∗∗ Postal address: Institut für Mathematische Stochastik, Leibniz Universität Hannover, Postfach 6009, D-30167 Hannover, Germany. Email address: rgrubel@stochastik.uni-hannover.de
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The classical models in risk theory consider a single type of claim. In the insurance business, however, several business lines with separate claim arrival processes appear naturally, and the individual claim processes may not be independent. We introduce a new class of models for such situations, where the underlying counting process is a multivariate continuous-time Markov chain of pure-birth type and the dependency of the components arises from the fact that the birth rate for a specific claim type may depend on the number of claims in the other component processes. Under certain conditions, we obtain a fluid limit, i.e. a functional law of large numbers for these processes. We also investigate the consequences of such results for questions of interest in insurance applications. Several specific subclasses of the general model are discussed in detail and the Cramér asymptotics of the ruin probabilities are derived in particular cases.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2008 

References

Bäuerle, N. and Grübel, R. (2005). Multivariate counting processes: copulas and beyond. ASTIN Bull. 35, 379408.Google Scholar
Bäuerle, N. and Müller, A. (1998). Modeling and comparing dependencies in multivariate risk portfolios. ASTIN Bull. 28, 5976.Google Scholar
Bäuerle, N., Müller, A. and Blatter, A. (2008). Dependence properties and comparison results for Lévy processes. Math. Meth. Operat. Res. 67, 161186.Google Scholar
Cont, R. and Tankov, P. (2004). Financial Modelling with Jump Processes. Chapman & Hall/CRC, Boca Raton, FL.Google Scholar
Davis, M. H. A. (1993). Markov Models and Optimization (Monogr. Statist. Appl. Prob. 49). Chapman & Hall, London.Google Scholar
Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Vol. II, 2nd edn. John Wiley, New York.Google Scholar
Glynn, P. W. and Whitt, W. (1994). Logarithmic asymptotics for steady-state tail probabilities in a single-server queue. In Studies in Applied Probability (J. Appl. Prob. Spec. Vol. 31A), Applied Probability Trust, Sheffield, pp. 131156.Google Scholar
Johnson, N. L. and Kotz, S. (1977). Urn Models and Their Application. John Wiley, New York.Google Scholar
Kemeny, J. G., Snell, J. L. and Knapp, A. W. (1976). Denumerable Markov Chains, 2nd edn. Springer, New York.CrossRefGoogle Scholar
Kushner, H. J. and Dupuis, P. (2001). Numerical Methods for Stochastic Control Problems in Continuous Time, 2nd edn. Springer, New York.CrossRefGoogle Scholar
Liggett, T. M. (1985). Interacting Particle Systems. Springer, New York.Google Scholar
Müller, A. and Pflug, G. (2001). Asymptotic ruin probabilities for dependent claims. Insurance Math. Econom. 28, 381392.Google Scholar
Müller, A. and Stoyan, D. (2002). Comparison Methods for Stochastic Models and Risks. John Wiley, Chichester.Google Scholar
Pfeifer, D. and Nešlehová, J. (2004). Modeling and generating dependent risk processes for IRM and DFA. ASTIN Bull. 34, 333360.Google Scholar
Rolski, T., Schmidli, H., Schmidt, V. and Teugels, J. (1999). Stochastic Processes for Insurance and Finance. John Wiley, Chichester.Google Scholar
Sawyer, S. A. (1997). Martin boundaries and random walks. In Harmonic Functions on Trees and Buildings (New York, 1995; Contemp. Math. 206), American Mathematical Society, Providence, RI, pp. 1744.Google Scholar
Szekli, R. (1995). Stochastic Ordering and Dependence in Applied Probability (Lecture Notes Statist. 97). Springer, New York.Google Scholar
Whitt, W. (2002). Stochastic-Process Limits. An Introduction to Stochastic-Process Limits and Their Application to Queues. Springer, New York.Google Scholar
Zocher, M. (2003). Multivariate mixed Poisson processes and the dependence of their coordinates. Dresdner Schriften zur Versicherungsmathematik 2, 16.Google Scholar
Zocher, M. (2005). Multivariate mixed Poisson processes. , Technische Universität Dresden.Google Scholar