arXiv (Cornell University)
An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
June 2024 • Mingtao Xia, Xiangting Li, Qijing Shen, Tom Chou
We analyze the Wasserstein distance ($W$-distance) between two probability distributions associated with two multidimensional jump-diffusion processes. Specifically, we analyze a temporally decoupled squared $W_2$-distance, which provides both upper and lower bounds associated with the discrepancies in the drift, diffusion, and jump amplitude functions between the two jump-diffusion processes. Then, we propose a temporally decoupled squared $W_2$-distance method for efficiently reconstructing unknown jump-diffusio…