Guillaume Broux-Quemerais,Sarah Kaakai, Anis Matoussi,Wissal Sabbagh  

作　　者：Guillaume Broux-Quemerais Sarah Kaakarl Anis Matoussi Wissal Sabbagh 

机构地区：[1]Laboratoire Manceau de Mathematiques&FR CNRS No 2962,Institut du Risque et de I'Assurance,LeMansUniversity,France [2]Centre de Mathématiques Appliquees(CMAP),CNRS,Ecole polytechnique,Institut Polytechnique de Paris,91120 Palaiseau,France

出　　处：《Probability, Uncertainty and Quantitative Risk》2024年第2期149-180,共32页概率、不确定性与定量风险（英文）

基　　金：The authors research is part of the ANR project DREAMeS(ANR-21-CE46-0002)and benefited from the support of respectively the "Chair Risques Emergents en Assurance"and"Chair Impact de la Transition Climatique en Assurance"under the aegis of Fondation du Risque,a joint initiative by Risk and Insurance Institute of Le Mans,and MMA-Covea and Groupama respectively.

摘　　要：In this paper,we present a probabilistic numerical method for a class of forward utilities in a stochastic factor model.For this purpose,we use the representation of forward utilities using the ergodic Backward Stochastic Differential Equations(eBSDEs)introduced by Liang and Zariphopoulou in[27].We establish a connection between the solution of the ergodic BSDE and the solution of an associated BSDE with random terminal time T,defined as the hitting time of the positive recurrent stochastic factor.The viewpoint based on BSDEs with random horizon yields a new characterization of the ergodic cost^which is a part of the solution of the eBSDEs.In particular,for a certain class of eBSDEs with quadratic generator,the Cole-Hopf transformation leads to a semi-explicit representation of the solution as well as a new expression of the ergodic cost>.The latter can be estimated with Monte Carlo methods.We also propose two new deep learning numerical schemes for eBSDEs.Finally,we present numerical results for different examples of eBSDEs and forward utilities together with the associated investment strategies.

关 键 词：Deep leaming scheme Forward utilities Ergodic BSDEs Markovian solution Deep learning algorithm 

分 类 号：O17[理学—数学]