Research Interests

I have two primary research interests, both of which use deep learning for computation.

1. Computational methods in mathematical finance, particularly term structure modeling and pricing interest rate derivatives using continuous-time techniques and deep learning.
2. Computational macro-finance with a focus on general equilibrium policy analysis, where I study questions at the intersection of household finance, macroeconomics, and asset pricing.

Working Papers

• Deep Learning the Term Structure for Derivatives Pricing [available upon request]
• Real and Asset Pricing Effects of Employer Retirement Matching
• Student Debt (Forgiveness) in General Equilibrium
  • Presented at Society for the Advancement of Economic Theory (Santiago, Chile, 2024)
  • Presented at Society for Economic Measurement (Calgary, AB, 2022)

Open-Source Research Tool: Finance-Informed Neural Networks for Overlapping Generations Models

I developed a flexible computational framework for solving stochastic overlapping generations models using deep learning. The code implements policy iteration with neural networks that directly incorporate economic constraints (Euler equations, feasibility conditions) into the training process. This grid-free approach leverages GPU acceleration to efficiently solve high-dimensional complex dynamic stochastic general equilibrium models.

Designed with modularity in mind, the modular nature of the program allows researchers to easily adapt the methodology to their own models by customizing model parameters, constraints, and equilibrium conditions.

GitHub: Finance-Informed Neural Networks for OLG Models