| Abstract |
In this talk, I will present the recent work on the deep learning algorithms for solving the nonlinear backward stochastic partial differential equations (BSPDEs). In particular we focus on continuous-time optimal investment (utility maximization) under the rough volatility models which are non-Markovian. The optimal value is expressed by a nonlinear BSPDE. The deep learning algorithms with iteration policy are proposed to solve the nonlinear BSPDE and analyzed in regards to the convergence.
(This is joint work with Haofei Wu and Harry Zheng.) |
| About the speaker |
Jingtang Ma is now a professor of Southwestern University of Finance and Economics and currently serves as the dean of School of Mathematics. His research interests include computational mathematics and mathematical finance. He has published papers in SIAM Journal on Control and Optimization, European Journal of Operational Research, Insurance: Mathematics and Economics, Journal of Computational Physics etc.
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