Deep Solver for Jumps in Backward Stochastic Differential Equations - Lecture
Explore an innovative approach to solving forward backward stochastic differential equations (FBSDEs) with jumps in this 59-minute presentation by Athena Picarelli from the University of Verona. Discover how Picarelli extends the deep solver method to tackle complex financial modeling challenges. Learn about the integration of artificial neural networks (ANNs) to parametrize high-dimensional control processes and the treatment of FBSDEs as model-based reinforcement learning problems. Understand the application of this algorithm to option pricing in both low and high dimensions, and its potential for addressing counterparty credit risk. Gain insights into the handling of finite and infinite jump activity, including approximation techniques for the latter case. The presentation covers an introduction, the main webinar content, and concludes with a Q&A session, offering a comprehensive look at this cutting-edge approach in financial mathematics and engineering.