 Markov decision processes under model uncertaintyAriel Neufeld, Julian Sester and Mario Šikić Mathematical Finance, 2023, vol. 33, issue 3, 618-665 Abstract: We introduce a general framework for Markov decision problems under model uncertainty in a discrete‐time infinite horizon setting. By providing a dynamic programming principle, we obtain a local‐to‐global paradigm, namely solving a local, that is, a one time‐step robust optimization problem leads to an optimizer of the global (i.e., infinite time‐steps) robust stochastic optimal control problem, as well as to a corresponding worst‐case measure. Moreover, we apply this framework to portfolio optimization involving data of the S&P500$S\&P\nobreakspace 500$. We present two different types of ambiguity sets; one is fully data‐driven given by a Wasserstein‐ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:mathfi:v:33:y:2023:i:3:p:618-665 Ordering information: This journal article can be ordered from Access Statistics for this article Mathematical Finance is currently edited by Jerome Detemple More articles in Mathematical Finance from Wiley Blackwell |
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