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On the infinite-horizon probability of (non)ruin for integer-valued claims

Published online by Cambridge University Press:  14 July 2016

Zvetan G. Ignatov*
Affiliation:
Sofia University ‘St Kliment Ohridski’
Vladimir K. Kaishev*
Affiliation:
City University, London
*
Postal address: Faculty of Economics and Business Administration, Sofia University ‘St Kliment Ohridski’, 125 Tsarigradsko Shosse Boulevard, bl. 3, Sofia 1113, Bulgaria.
∗∗Postal address: Faculty of Actuarial Science and Statistics, Cass Business School, City University, 106 Bunhill Row, London EC1Y 8TZ, UK. Email address: [email protected]
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Abstract

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We consider a compound Poisson process whose jumps are modelled as a sequence of positive, integer-valued, dependent random variables, W1,W2,…, viewed as insurance claim amounts. The number of points up to time t of the stationary Poisson process which models the claim arrivals is assumed to be independent of W1,W2,…. The premium income to the insurance company is represented by a nondecreasing, nonnegative, real-valued function h(t) on [0,∞) such that limt→∞h(t) = ∞. The function h(t) is interpreted as an upper boundary. The probability that the trajectory of such a compound Poisson process will not cross the upper boundary in infinite time is known as the infinite-horizon nonruin probability. Our main result in this paper is an explicit expression for the probability of infinite-horizon nonruin, assuming that certain conditions on the premium-income function, h(t), and the joint distribution of the claim amount random variables, W1,W2,…, hold. We have also considered the classical ruin probability model, in which W1,W2,… are assumed to be independent, identically distributed random variables and we let h(t)=u + ct. For this model we give a formula for the nonruin probability which is a special case of our main result. This formula is shown to coincide with the infinite-horizon nonruin probability formulae of Picard and Lefèvre (2001), Gerber (1988), (1989), and Shiu (1987), (1989).

Type
Research Papers
Copyright
© Applied Probability Trust 2006 

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