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Stochastic Control in Insurance [electronic resource] /by Hanspeter Schmidli.

by Schmidli, Hanspeter [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Probability and Its Applications: Publisher: London : Springer London, 2008.Description: online resource.ISBN: 9781848000032.Subject(s): Mathematics | Mathematical optimization | Distribution (Probability theory) | Banks and banking | Mathematics | Probability Theory and Stochastic Processes | Calculus of Variations and Optimal Control; Optimization | Optimization | Finance /Banking | Control Engineering | Mathematics, generalDDC classification: 519.2 Online resources: Click here to access online
Contents:
Stochastic Control in Discrete Time -- Stochastic Control in Continuous Time -- Problems in Life Insurance -- Asymptotics of Controlled Risk Processes -- Appendices -- Stochastic Processes and Martingales -- Markov Processes and Generators -- Change of Measure Techniques -- Risk Theory -- The Black-Scholes Model -- Life Insurance -- References -- Index -- List of Principal Notation.
In: Springer eBooksSummary: 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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Stochastic Control in Discrete Time -- Stochastic Control in Continuous Time -- Problems in Life Insurance -- Asymptotics of Controlled Risk Processes -- Appendices -- Stochastic Processes and Martingales -- Markov Processes and Generators -- Change of Measure Techniques -- Risk Theory -- The Black-Scholes Model -- Life Insurance -- References -- Index -- List of Principal Notation.

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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