Prof. LI, Lingfei 李 凌 飛 教授
Associate Professor
BS (Peking University)
MS, PhD (Northwestern University)
Research Interests :
* Financial Engineering
* Mathematical Finance
* Computational Finance
Office: Room 608, William M.W. Mong Engineering Building
Tel: (852) 3943-8329
Email: lfli@se.cuhk.edu.hk
Biography
Professor Lingfei Li received his B.S. in Applied Mathematics from Peking University, China in 2007, and his M.S. and Ph.D. in Industrial Engineering and Management Sciences from Northwestern University, USA in 2008 and 2012. He joined the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong in June 2012. His research interests include financial engineering, mathematical finance and computational finance. He worked as a quant in the commodity strategies group at Morgan Stanley in the summer of 2009.
Selected Publications
G. Zhang and L. Li, “Analysis of Markov chain approximation for option pricing and hedging: grid design and convergence behavior”, Operations Research 67(2):407-427, 2019.
L. Li and G. Zhang, “Error analysis of finite difference and Markov chain approximations for option pricing”, Mathematical Finance, 28(3), 877-919, 2018.
J. Li, L. Li and G. Zhang, “Pure jump models for pricing and hedging VIX derivatives”, Journal of Economic Dynamics and Control 74(1), 28-55, 2017.
L. Li and G. Zhang, “Option pricing in some non-Levy jump models”, SIAM Journal on Scientific Computing, 38(4), B539-B569, 2016.
J. Li, L. Li and R. Mendoza-Arriaga, “Additive subordination and its applications in finance”, Finance and Stochastics 20(3), 589-634, 2016.
L. Li and V. Linetsky, “Discretely monitored first passage problems and barrier options: an eigenfunction expansion approach”, Finance and Stochatics 19(4), 941-977, 2015.
L. Li and V. Linetsky, “Time-changed Ornstein-Uhlenbeck processes and their applications in commodity derivative models”, Mathematical Finance 24(2), 289-330, 2014.
L. Li and V. Linetsky, “Optimal stopping and early exercise: an eigenfunction expansion approach”, Operations Research 61(3), 625-643, 2013.