Jiayu Zheng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Jiayu Zheng has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Jie Xiong
Department of Mathematics and SUSTech International Center for Mathematics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
5 shared publications - Lihu Xu
Department of Mathematics, Faculty of Science and Technology, University of Macau, Macao, China
2 shared publications - Peng Chen
School of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2 shared publications - Hancheng Guo
Department of Mathematics, Faculty of Science and Technology, University of Macau, Macau, China
1 shared publication Jie Xiong
Department of Mathematics and SUSTech International Center for Mathematics, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
1 shared publicationXiaowen Zhou
Department of Mathematics and Statistics, Concordia University, Canada
1 shared publication
Latest Publications
- 19 March 2026
- Published by Springer Nature in Potential Analysis
- Vol. 64 (4) , 56
Quantitative Diffusion Approximation for the Neutral r-Alleles Wright-Fisher Model with Mutations
- 19 March 2026
- Published by Springer Nature in Potential Analysis
- Vol. 64 (4) , 56
Quantitative Diffusion Approximation for the Neutral r-Alleles Wright-Fisher Model with Mutations
- Open Access
Quantitative diffusion approximation for the Neutral $r$-Alleles Wright-Fisher Model with Mutations
We apply a Lindeberg principle under the Markov process setting to approximate the Wright-Fisher model with neutral $r$-alleles using a diffusion process, deriving an error rate based on a function class distance involving fourth-order bounded differentiable functions. This error rate consists of a linear combination of the maximum mutation rate and the reciprocal of the population size. Our result improves the error bound in the seminal work [PNAS,1977], where only the special case $r=2$ was studied.Open AccessQuantitative diffusion approximation for the Neutral $r$-Alleles Wright-Fisher Model with Mutations
We apply a Lindeberg principle under the Markov process setting to approximate the Wright-Fisher model with neutral $r$-alleles using a diffusion process, deriving an error rate based on a function class distance involving fourth-order bounded differentiable functions. This error rate consists of a linear combination of the maximum mutation rate and the reciprocal of the population size. Our result improves the error bound in the seminal work [PNAS,1977], where only the special case $r=2$ was studied. - 1 March 2024
- Published by Springer Nature in Journal of Optimization Theory and Applications
- Vol. 201 (1) , 352-377
Stochastic Maximum Principle for Generalized Mean-Field Delay Control Problem
- 1 March 2024
- Published by Springer Nature in Journal of Optimization Theory and Applications
- Vol. 201 (1) , 352-377
Stochastic Maximum Principle for Generalized Mean-Field Delay Control Problem
- 8 April 2021
- Published by Taylor & Francis in Quantitative Finance
- Vol. 21 (9) , 1461-1473
Mean–variance portfolio selection under partial information with drift uncertainty
In this paper, we study the mean–variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.
- 8 April 2021
- Published by Taylor & Francis in Quantitative Finance
- Vol. 21 (9) , 1461-1473
Mean–variance portfolio selection under partial information with drift uncertainty
In this paper, we study the mean–variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.
- 13 January 2019
- Published by Taylor & Francis in Stochastics
- Vol. 91 (4) , 592-612
Unique strong solutions of Lévy processes driven stochastic differential equations with discontinuous coefficients
We study the strong solutions for a class of one-dimensional stochastic differential equations driven by a Brownian motion and a pure jump Lévy process. Under fairly general conditions on the coefficients, we prove the pathwise uniqueness by showing the weak uniqueness and applying a local time technique.
- 13 January 2019
- Published by Taylor & Francis in Stochastics
- Vol. 91 (4) , 592-612
Unique strong solutions of Lévy processes driven stochastic differential equations with discontinuous coefficients
We study the strong solutions for a class of one-dimensional stochastic differential equations driven by a Brownian motion and a pure jump Lévy process. Under fairly general conditions on the coefficients, we prove the pathwise uniqueness by showing the weak uniqueness and applying a local time technique.
- 1 November 2017
- Published by Elsevier in Statistics & Probability Letters
- Vol. 130, 100-104
Pathwise uniqueness for stochastic differential equations driven by pure jump processes
- 1 November 2017
- Published by Elsevier in Statistics & Probability Letters
- Vol. 130, 100-104
Pathwise uniqueness for stochastic differential equations driven by pure jump processes