Publications
Z. Dai and L. Li (2023). Deep learning for enhanced index tracking. Quantitative Finance 24(5), 569-591. [link]
L. Li, P. Zeng and G. Zhang (2024). Speed and duration of drawdown under general Markov models. Quantitative Finance 24(3), 367-386. [link]
B. Wu and L. Li (2024). Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market. Journal of Economic Dynamics and Control 158, 104787. [link]
G. Zhang and L. Li (2023). A general approach for Parisian stopping times under Markov processes. Finance and Stochastics 27(3), 769-829. [link]
G. Zhang and L. Li (2023). A general approach for lookback option pricing under Markov models. Quantitative Finance 23(9), 1305-1324. [link]
G. Zhang and L. Li (2023). A general method for analysis and valuation of drawdown risk. Journal of Economic Dynamics and Control 152, 104669. [link]
J. Chen and L. Li (2023). Data-driven hedging of stock index options via deep learning. Operations Research Letters 51(4), 408-413. [link]
Q. Lai, X. Gao and L. Li (2023). A data-driven deep learning approach for options market making. Quantitative Finance 23(5), 777-797. [link]
W. Zhang, L. Li and G. Zhang (2023). A two-step framework for arbitrage-free prediction of the implied volatility surface. Quantitative Finance 23(1), 21-34. [link]
C. Meier, L. Li and G. Zhang (2023). Simulation of multidimensional diffusions with sticky boundaries via Markov chain approximation. European Journal of Operational Research 305(3), 1292-1308. [link]
Z. Dai, L. Li and G. Zhang (2022). Evaluation of deep learning algorithms for quadratic hedging. The Journal of Derivatives 30(1), 32-57. [link]
G. Zhang and L. Li (2022). Analysis of Markov chain approximation for diffusion models with non-smooth coefficients. SIAM Journal on Financial Mathematics 13(3),1144-1190. [link]
J. Chen, L. Fan, L. Li and G. Zhang (2022). A multidimensional Hilbert transform approach for barrier option pricing and survival probability calculation. Review of Derivatives Research 25(2), 189-232. [link]
S. Bu, N. Guo and L. Li (2022). Rating fraity, Bayesian updates and portfolio credit risk analysis. Quantitative Finance 22(4), 777-797. [link]
N. Guo and L. Li (2022). Stressed distance to default and default risk. The Journal of Credit Risk 18(3), 29-48. [link]
Y. Ge, L. Li and G. Zhang (2022). A Fourier transform method for solving backward stochastic differential equations. Methodology and Computing in Applied Probability 24(1), 385-412. [link]
X. Zhang, L. Li and G. Zhang (2021). Pricing American drawdown options under Markov models. European Journal of Operational Research 293(3), 1188-1205. [link]
C. Meier , L. Li and G. Zhang (2021). Markov chain approximation of one-dimensional sticky diffusions. Advances in Applied Probability 53(2), 335-369. [link]
Y. Ge and L. Li (2020). A Hilbert transform approach for controlled jump-diffusions with financial applications. International Journal of Financial Engineering 7(4), 1-46. [link]
L. Li and R. Mendoza-Arriaga (2019). Equivalent measure changes for subordinate diffusions. Stochastic Models 35(4), 357-390. [link]
G. Zhang and L. Li (2019). Analysis of Markov chain approximation for option pricing: grid design and convergence behavior. Operations Research 67(2), 407-427. [link]
W. Guo and L. Li (2019). Parametric inference for discretely observed subordinate diffusions. Statistical Inference for Stochastic Processes 22(1), 77-110. [link]
L. Li and G. Zhang (2017). Error analysis of finite difference and Markov chain approximations for option pricing. Mathematical Finance 28(3), 877-919. [link]
J. Li, L. Li and G. Zhang (2017). Pure jump models for pricing and hedging VIX derivatives. Journal of Economic Dynamics and Control 74(1), 28-55. [link]
L. Li and G. Zhang (2016). Option pricing in some non-Lévy jump models. SIAM Journal on Scientific Computing 38(4), B539-B569. [link]
J. Li, L. Li and R. Mendoza-Arriaga (2016). Additive subordination and its applications in finance. Finance and Stochastics 20(3), 589-634. [link]
L. Li, R. Mendoza-Arriaga and D. Mitchell (2016). Analytical representations for the basic affine jump diffusion. Operations Research Letters 44(1), 121-128. [link]
L. Li, R. Mendoza-Arriaga, Z. Mo and D. Mitchell (2016). Modelling electricity prices: a time change approach. Quantitative Finance 16(7), 1089-1109. [link]
L. Li, X. Qu and G. Zhang (2016). An efficient algorithm based on eigenfunction expansions for some optimal timing problems in finance. Journal of Computational and Applied Mathematics 294(1), 225-250. [link]
L. Li and V. Linetsky (2015). Discretely monitored first passage problems and barrier options: an eigenfunction expansion approach. Finance and Stochastics 19(4), 941-977. [link]
L. Li and V. Linetsky (2014). Time-changed Ornstein-Uhlenbeck processes and their applications in commodity derivative models. Mathematical Finance 24(2), 289-330. [link]
L. Li and V. Linetsky (2014). Optimal stopping in infinite horizon: an eigenfunction expansion approach. Statistics and Probability Letters 85(1), 122-128. [link]
L. Li and R. Mendoza-Arriaga (2013). Ornstein-Uhlenbeck processes time-changed with additive subordinators and their applications in commodity derivative models. Operations Research Letters 41(5), 521-525. [link]
L. Li and V. Linetsky (2013). Optimal stopping and early exercise: an eigenfunction expansion approach. Operations Research 61(3), 625-643. [link]
D. Lim, L. Li and V. Linetsky (2012). Evaluating callable and putable bonds: an eigenfunction expansion approach. Journal of Economic Dynamics and Control 36(12), 1888-1908. [link]