Deep Learning for Reflected Backwards Stochastic Differential Equations

Miller, Frederick

Student Work

Deep Learning for Reflected Backwards Stochastic Differential Equations

Public Deposited

In this work, we in investigate the theory and numerics of reflected backwards stochastic differential equations (RBSDEs). We review important concepts from stochastic calculus, as well as key theoretical properties of (R)BSDEs. We provide an overview of feedforward neural networks and their applications to functional approximation for numerical implementations. We also discuss the key application of RBSDEs to the field of mathematical finance, in particular indifference pricing of put options. Lastly, we present preliminary theoretical and numerical results of Risk Indifference pricing of American from both the Buyer’s and Seller’s perspectives.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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