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On ruin probabilities in risk models with interest rate

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Abstract

An explicit formula for the finite-time ruin probability in a discrete-time collective ruin model with constant interest rate is found under the assumption that claims follow a generalised hyperexponential distribution. The formula can be used for finding approximations for finite-time ruin probabilities in the case when claim sizes follow a heavy-tailed distribution e.g. Pareto. We also provide theoretical bounds for the accuracy of approximations of the finite-time ruin probabilities in terms of a distance between the distribution of claims and its approximation. Results of numerical comparisons with asymptotic formulas and simulations are presented.

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Acknowledgements

The authors thank Dr E. Shinjikashvili for assistance provided during an early stage of this project that was supported by ARC Discovery grant. The third author acknowledges the financial support of Far Eastern Branch of Russian Academy of Science 09-1-P2-07, 09-1-OMN-07.

The authors also thank Mrs N. Carter, the Honorary Associate of the Department of Statistics, Macquarie University for helpful comments that improved the paper.

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Correspondence to Nino Kordzakhia .

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Appendix

Appendix

Proof of Proposition 2

The proof is based on the following lemma which was proved in [7].

Lemma. Let the Markov chain V n be a solution of the

(16)

Then

(17)

1. Proof of (14)

Set

then

For n=1 we have

We use the induction method, let us assume that

Next,

Thus, the statement 1 is proved.

2. Proof of (15)

Set

where random variables ω i , i=1, 2 follow the uniform distribution on [0, 1],

Then by induction we have

Note that in view of results in [8], we obtained

where infimum is taken over all joint distributions that have the same marginal distributions as that of V n and \( V_n^* \) . This completes the proof of statement 2.

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Kordzakhia, N., Novikov, A., Tsitsiashvili, G. (2012). On ruin probabilities in risk models with interest rate. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Milano. https://doi.org/10.1007/978-88-470-2342-0_29

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