Jump Diffusion

Jump diffusion models describe stochastic processes incorporating both continuous and discontinuous changes, offering a more realistic representation of phenomena exhibiting sudden jumps alongside gradual fluctuations. Current research focuses on developing efficient algorithms, such as deep learning methods and reinforcement learning approaches (including Q-learning variations), to solve complex problems involving these models, particularly in high-dimensional settings. These advancements are improving the accuracy and speed of applications in diverse fields, including financial modeling (e.g., option pricing and portfolio optimization) and generative modeling of multi-dimensional data. The resulting improvements in model accuracy and computational efficiency are driving significant progress in various scientific and practical domains.