School of Business
25 Feb 2021
Virtual Zoom lecture

Deep Learning in Pricing, Hedging and Risk Management

Financial Engineering Seminar Series

Financial data on a dark screen.

This talk will explore the application of deep learning in solving many pricing and risk modeling problems for common instruments which are posed as Forward Backward Stochastic Differential Equations. The SDEs could be time-continuous or time-discrete, and in general posed as stochastic control problems with final values, representing the final value of instrument, payoff or cash flow. 

The framework, referred to as DeepBSDE, employs deep neural networks to learn the appropriate controls required to achieve the final value or the payoff. The above DeepBSDE formalism has several advantages compared to traditional approaches for pricing and hedging. This talk will present a general overview of FBSDEs in the context of DeepBSDEs, and demonstrate their applicability in pricing simple instruments, as well as more complex barrier options along with its risk management and hedging PnLs.


Headshot of Bernhard HientzschDr. Bernhard Hientzsch is a quantitative manager working in model risk R&D at Wells Fargo. He leads a group that concentrates on capital markets pricing and risk modeling and has managed, lead and worked on a wide range of projects within that area. Previously, he was a postdoctorate researcher at New York University and self-employed. He received his Ph.D. in applied mathematics from the Courant Institute of Mathematical Sciences at NYU.


Headshot of Narayan GanesanDr. Narayan Ganesan is a quantitative analyst working in model risk R&D at Wells Fargo in the area of capital markets pricing and risk modeling. Among other things, he has been working on pricing and hedging models for equity, FX and interest rate derivatives; modeling counterparty credit risk; margin requirements; and volatility calibration. Prior to joining Wells Fargo he was a vice president at Morgan Stanley. He also was an assistant professor at Stevens, where his research specialized in high performance and heterogeneous computing platforms. He received his Ph.D in Electrical and Systems Engineering at Washington University in St. Louis. He is a recipient of industry awards and the New Jersey Inventors Hall of Fame award. 

About this series

The Financial Engineering Seminar Series is a recurring event featuring thought leaders from industry and academia, who bring their experiences to a variety of important topics in this discipline. 

M.S. Financial Engineering Curriculum Overview School of Business

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