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Optimal insurance strategies : a hybrid deep learning Markov chain approximation approach

Recurso electrónico / Electronic resource
MARC record
Tag12Value
LDR  00000cab a2200000 4500
001  MAP20200019084
003  MAP
005  20200604155948.0
008  200604e20200501bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20200013389‎$a‎Cheng, Xiang
24510‎$a‎Optimal insurance strategies‎$b‎: a hybrid deep learning Markov chain approximation approach‎$c‎Xiang Cheng, Zhuo Jin, Hailiang Yang
520  ‎$a‎This paper studies deep learning approaches to find optimal reinsurance and dividend strategies for insurance companies. Due to the randomness of the financial ruin time to terminate the control processes, a Markov chain approximation-based iterative deep learning algorithm is developed to study this type of infinite-horizon optimal control problems. The optimal controls are approximated as deep neural networks in both cases of regular and singular types of dividend strategies. The framework of Markov chain approximation plays a key role in building the iterative equations and initialization of the algorithm. We implement our method to classic dividend and reinsurance problems and compare the learning results with existing analytical solutions. The feasibility of our method for complicated problems has been demonstrated by applying to an optimal dividend, reinsurance and investment problem under a high-dimensional diffusive model with jumps and regime switching.
650 4‎$0‎MAPA20080576783‎$a‎Modelo de Markov
650 4‎$0‎MAPA20080590567‎$a‎Empresas de seguros
650 4‎$0‎MAPA20080589875‎$a‎Control estocástico
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080552367‎$a‎Reaseguro
650 4‎$0‎MAPA20080553128‎$a‎Algoritmos
7001 ‎$0‎MAPA20140000197‎$a‎Jin, Zhuo
7001 ‎$0‎MAPA20080653507‎$a‎Yang, Hailiang
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎01/05/2020 Volumen 50 Número 2 - mayo 2020 , p. 449-477