Premium control with reinforcement learning

We consider a premium control problem in discrete time, formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimens...

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Veröffentlicht in:	ASTIN Bulletin : The Journal of the IAA Jg. 53; H. 2; S. 233 - 257
Hauptverfasser:	Palmborg, Lina, Lindskog, Filip
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, USA Cambridge University Press 01.05.2023
Schlagworte:	Actuarial science Approximation Control algorithms Deep learning Dividends Dynamical systems Hedging Insurance premiums Markov analysis Markov decision process matematisk statistik Mathematical Statistics Mutual insurance companies Premium control Present value Probability reinforcement learning Stochastic control theory Stochastic models Teaching methods Markov decision process Premium control C60 G22 reinforcement learning
ISSN:	0515-0361, 1783-1350, 1783-1350
Online-Zugang:	Volltext
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