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An ergodic BSDE approach to entropic risk measure and its large time behavior

Wing Fung Chong (), Ying Hu (), Gechun Liang () and Thaleia Zariphopoulou ()
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Wing Fung Chong: Department of Mathematics, King's College London
Ying Hu: IRMAR - Institut de Recherche Mathématique de Rennes - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - AGROCAMPUS OUEST - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - INSA Rennes - Institut National des Sciences Appliquées - Rennes - INSA - Institut National des Sciences Appliquées - UNIV-RENNES - Université de Rennes - ENS Rennes - École normale supérieure - Rennes - UR2 - Université de Rennes 2 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique
Thaleia Zariphopoulou: University of Texas at Austin [Austin]

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Abstract: This paper shows that the long-time behavior of the entropic risk measure (under both forward performance process framework and classical utility framework) converges to a constant, which is independent of the initial state of the stochastic factors in a stochastic factor model. The exponential convergence rate to the long-term limit is also obtained by using ergodic backward stochastic differential equation method. Finally, the paper establishes a connection between the two notions of entropic risk measures and their large time behavior.

Date: 2019
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01361585
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Published in Finance and Stochastics, Springer Verlag (Germany), 2019, 23 (1), pp.239-273. ⟨10.1007/s00780-018-0377-3⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01361585

DOI: 10.1007/s00780-018-0377-3

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