PhD Seminar • Scientific Computing • Efficient Pricing and Hedging of High Dimensional American Options using Deep Recurrent Networks

Please note: This PhD seminar will take place in DC 3102.

Andrew Na, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Justin Wan

Recently, deep neural networks have been utilized to price and hedge high dimensional American options. However, the deep neural network needs to be initialized and trained over N time steps resulting in a framework that requires N deep neural networks to price and hedge high-dimensional options. This results in a long training time and large memory requirements to store the weights of each network.

We propose to use two deep recurrent networks to mitigate this problem. We show that by using recurrent networks we can reduce the time and the memory required to price and hedge high dimensional American options.