2020/02/07 by Bielecki, Tomasz R., Chen, Tao, Cialenco, Igor
#49L20 #60J05 #60J20 #62F25 #91A10 #91G10 #91G80 #93C40 #93E35 #FOS: Economics and business #FOS: Mathematics #Mathematical Finance (q-fin.MF) #Optimization and Control (math.OC)
paper · doi:10.48550/arxiv.2002.02604
In this paper we study a class of time-inconsistent terminal Markovian control problems in discrete time subject to model uncertainty. We combine the concept of the sub-game perfect strategies with the adaptive robust stochastic to tackle the theoretical aspects of the considered stochastic control problem. Consequently, as an important application of the theoretical results, by applying a machine learning algorithm we solve numerically the mean-variance portfolio selection problem under the model uncertainty.