no code implementations • 19 Dec 2023 • Min Dai, Yuchao Dong, Yanwei Jia, Xun Yu Zhou
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primitives are unknown.
no code implementations • 14 Apr 2023 • Yunhong Li, Zuo Quan Xu, Xun Yu Zhou
We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market.
no code implementations • 15 Dec 2022 • Kaizheng Wang, Xiao Xu, Xun Yu Zhou
We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression.
no code implementations • 14 Dec 2022 • Lin Chen, Xun Yu Zhou
We study a continuous-time Markowitz mean-variance portfolio selection model in which a naive agent, unaware of the underlying time-inconsistency, continuously reoptimizes over time.
no code implementations • 3 Oct 2022 • Xuefeng Gao, Xun Yu Zhou
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-horizon episodic setting.
no code implementations • 17 Aug 2022 • Xia Han, Ruodu Wang, Xun Yu Zhou
We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer.
no code implementations • 2 Jul 2022 • Yanwei Jia, Xun Yu Zhou
We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang et al. (2020).
no code implementations • 23 May 2022 • Xuefeng Gao, Xun Yu Zhou
We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting.
no code implementations • 22 Nov 2021 • Yanwei Jia, Xun Yu Zhou
This effectively turns PG into a policy evaluation (PE) problem, enabling us to apply the martingale approach recently developed by Jia and Zhou (2021) for PE to solve our PG problem.
no code implementations • 15 Aug 2021 • Yanwei Jia, Xun Yu Zhou
From this perspective, we find that the mean--square TD error approximates the quadratic variation of the martingale and thus is not a suitable objective for PE.
no code implementations • 26 Mar 2021 • Wenpin Tang, Xiao Xu, Xun Yu Zhou
Finally, we conduct an empirical analysis to verify the performance of the algorithm.
no code implementations • 5 Feb 2021 • Sang Hu, Jan Obloj, Xun Yu Zhou
We develop an approach to solve Barberis (2012)'s casino gambling model in which a gambler whose preferences are specified by the cumulative prospect theory (CPT) must decide when to stop gambling by a prescribed deadline.
no code implementations • 3 Feb 2021 • Wenpin Tang, Xun Yu Zhou
in cumulative step size), and provide an explicit rate as a function of the model parameters.
no code implementations • 15 Nov 2020 • Xuefeng Gao, Zuo Quan Xu, Xun Yu Zhou
We study the temperature control problem for Langevin diffusions in the context of non-convex optimization.
no code implementations • 17 Aug 2020 • Yichun Chi, Xun Yu Zhou, Sheng Chao Zhuang
We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle.
no code implementations • 2 Jun 2020 • Ying Hu, Hanqing Jin, Xun Yu Zhou
We study portfolio selection in a complete continuous-time market where the preference is dictated by the rank-dependent utility.
1 code implementation • 25 Apr 2019 • Haoran Wang, Xun Yu Zhou
We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL).