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个人简介
Nan Chen is an associate professor in the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His research interests are quantitative methods in finance and risk management, Monte Carlo simulation, and applied probability. He has published in top journals and referred conference proceedings in the fields of operations research and quantitative finance, such as Operations Research, Mathematics of Operations Research, Mathematical Finance, Finance and Stochastics. The previous research topics included credit spread modeling, stochastic differential game in convertible security pricing, Monte Carlo methods in American option pricing and the related sensitivity analysis, simulation of stochastic differential equations, and exotic option pricing under jump diffusion models. Currently, he is mainly focusing on modeling of systemic contagion and liquidity risk, complex social and financial network, and Monte Carlo method in stochastic control and learning. Part of his research is supported by the scheme of General Research Fund, Hong Kong Research Grant Council.
Prof. Chen received his Ph.D. in operations research from Columbia University in 2006, and M.S. and B.S. in probability and statistics from Peking University, Beijing, China in 2001 and 1998, respectively. He was a second place price recipient of the Best Student Research Paper Award, Financial Services Section, INFORMS, 2006. He served as associate editor for Operations Research Letters from 2007-2008 and chaired (or was a member of) the program committees of several international conferences on quantitative finance and Monte Carlo simulation.
Prof. Chen is also deputy director of Master of Science Program in Financial Engineering at CUHK Shenzhen.
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OPERATIONS RESEARCHno. 1 (2024): 2-19
MATHEMATICAL FINANCEno. 1 (2024): 3-4
Mathematical Finance (2023)
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Stochastic Methods for Modeling and Predicting Complex Dynamical SystemsSynthesis Lectures on Mathematics & Statisticspp.49-65, (2023)
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Stochastic Methods for Modeling and Predicting Complex Dynamical SystemsSynthesis Lectures on Mathematics & Statisticspp.23-37, (2023)
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Stochastic Methods for Modeling and Predicting Complex Dynamical SystemsSynthesis Lectures on Mathematics & Statisticspp.39-48, (2023)
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Stochastic Methods for Modeling and Predicting Complex Dynamical SystemsSynthesis Lectures on Mathematics & Statisticspp.1-21, (2023)
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Social Science Research Network (2022): 102164-102164
arxiv(2020)
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