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This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI's surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a risky-asset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.
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참고문헌 (21)
참고문헌 신청
E. Anderson / 1988 / Robustness detection and the price of risk, Working Paper
E. Anderson / 2003 / A quartet of semi-groups for model specication, robustness, prices of risk, and model detection / JEEA 1:68~123
Lihua Bai / 2007 / Utility maximization with partial information: Hamilton-Jacobi-Bellman equation approach / Frontiers of Mathematics in China 2(4):527~537
Lihua Bai / 2008 / Optimal proportional reinsurance and investment with multiple risky assets and no-shorting constraint / Insurance: Mathematics and Economics 42(3):968~975
Nicole Bäuerle / 2005 / Benchmark and mean-variance problems for insurers / Mathematical Methods of Operations Research 62(1):159~165
E. Anderson / 2003 / A quartet of semi-groups for model specication, robustness, prices of risk, and model detection / JEEA 1:68~123
Lihua Bai / 2007 / Utility maximization with partial information: Hamilton-Jacobi-Bellman equation approach / Frontiers of Mathematics in China 2(4):527~537
Lihua Bai / 2008 / Optimal proportional reinsurance and investment with multiple risky assets and no-shorting constraint / Insurance: Mathematics and Economics 42(3):968~975
Nicole Bäuerle / 2005 / Benchmark and mean-variance problems for insurers / Mathematical Methods of Operations Research 62(1):159~165
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