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Asymptotic and numerical analysis of the optimal investment strategy for an insurer
Ruin models with investment income
This survey treats the problem of ruin in a risk model when assets earn
investment income. In addition to a general presentation of the problem, topics
covered are a presentation of the relevant integro-differential equations,
exact and numerical solutions, asymptotic results, bounds on the ruin
probability and also the possibility of minimizing the ruin probability by
investment and possibly reinsurance control. The main emphasis is on continuous
time models, but discrete time models are also covered. A fairly extensive list
of references is provided, particularly of papers published after 1998. For
more references to papers published before that, the reader can consult [47].Comment: Published in at http://dx.doi.org/10.1214/08-PS134 the Probability
Surveys (http://www.i-journals.org/ps/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A numerical method for the expected penalty–reward function in a Markov-modulated jump–diffusion process.
A generalization of the Cramér–Lundberg risk model perturbed by a diffusion is proposed. Aggregate claims of an insurer follow a compound Poisson process and premiums are collected at a constant rate with additional random fluctuation. The insurer is allowed to invest the surplus into a risky asset with volatility dependent on the level of the investment, which permits the incorporation of rational investment strategies as proposed by Berk and Green (2004). The return on investment is modulated by a Markov process which generalizes previously studied settings for the evolution of the interest rate in time. The Gerber–Shiu expected penalty–reward function is studied in this context, including ruin probabilities (a first-passage problem) as a special case. The second order integro-differential system of equations that characterizes the function of interest is obtained. As a closed-form solution does not exist, a numerical procedure based on the Chebyshev polynomial approximation through a collocation method is proposed. Finally, some examples illustrating the procedure are presentedExpected penalty–reward function; Markov-modulated process; Jump–diffusion process; Volterra integro-differential system of equations;
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