Deep Learning in Finance – Lecture 2
Speaker: Prof. Ovidiu Calin, Eastern Michigan University
Host: S Baskar
Title: Machine Learning Methods for Financial Model Calibration
Time, day and date: 5:00:00 PM – 6:00:00 PM, Thursday, August 06
Venue: Ramanujan Hall
Abstract: This presentation introduced machine learning methods for the calibration of financial models, focusing on the recovery of unknown model parameters from observed market data. We began by formulating model calibration as an inverse problem in which parameters are estimated so that theoretical option prices closely match market prices. After reviewing the mathematical optimization framework, we showed how machine learning can replace repeated numerical optimization by directly learning the inverse mapping from market prices to model parameters, enabling fast real-time calibration. Samuelson's geometric Brownian motion model
was then presented as a fundamental example, together with its stochastic differential equation, probabilistic properties, and explicit maximum likelihood estimators for drift and volatility. To account for changing market conditions, we introduced sliding-window estimation, which produces time-dependent parameter estimates capable of identifying volatility clustering, market regimes, and trend changes. Finally, the limitations of constant-volatility models motivated the introduction of the Heston stochastic volatility model, whose additional parameters provide a more realistic description of financial markets. Throughout the presentation, interactive implementations demonstrated how modern machine learning techniques can efficiently estimate model parameters, visualize their evolution over time, and support practical applications in option pricing, hedging, and quantitative risk management.