| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| E-Books | Biblioteca da FCTUNL Online | Não Ficção | HG8781.SPR FCT 95708 (Browse shelf) | 1 | Available |
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| HG8781.SPR FCT 82290 Non-life insurance pricing with generalized linear models | HG8781.SPR FCT 82440 Market-consistent actuarial valuation | HG8781.SPR FCT 82461 Introduction to insurance mathematics | HG8781.SPR FCT 95708 Stochastic control in insurance | HG8781.SPR FCT 97403 Handbook on loss reserving | HG8781.SPR FCT 98300 An introduction to optimal control of FBSDE with incomplete information | HG8781.TSE FCT 99967 Nonlife actuarial models |
Colocação: Online
Stochastic control is one of the methods being used to find optimal decision-making strategies in fields such as operations research and mathematical finance. In recent years, stochastic control techniques have been applied to non-life insurance problems, and in life insurance the theory has been further developed. This book provides a systematic treatment of optimal control methods applied to problems from insurance and investment, complete with detailed proofs. The theory is discussed and illustrated by way of examples, using concrete simple optimisation problems that occur in the actuarial sciences. The problems come from non-life insurance as well as life and pension insurance and also cover the famous Merton problem from mathematical finance. Wherever possible, the proofs are probabilistic but in some cases well-established analytical methods are used. The book is directed towards graduate students and researchers in actuarial science and mathematical finance who want to learn stochastic control within an insurance setting, but it will also appeal to applied probabilists interested in the insurance applications and to practitioners who want to learn more about how the method works. Readers should be familiar with basic probability theory and have a working knowledge of Brownian motion, Markov processes, martingales and stochastic calculus. Some knowledge of measure theory will also be useful for following the proofs.
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