Dr. Durham’s research is focused in the area of financial econometrics, with applications to risk management and bond and option pricing. His most recent work involves the development of methods and software for efficient sequential simulation of Bayesian posteriors. Similar methods also turn out to be useful in optimization, which is a key element of classical model estimation, and Durham is actively pursuing research in this area as well. Dr. Durham is interested in computational issues, especially the development of tools making effective use of massively parallel hardware (e.g., graphics cards) that has become increasing available and powerful in recent years.
Dr. Durham’s research has been published in leading journals, including the Journal of Financial Economics, Journal of Econometrics, and Journal of Business and Economic Statistics.
Dr. Durham’s teaching interests include corporate finance, fixed income, options and futures, and data analytics.
Dr. Durham joined the Cal Poly Finance faculty in 2014. He received his PhD from the University of North Carolina. Dr. Durham has also done extensive private sector work involving modeling, forecasting and data analytics while senior economist at Quantos Analytics, LLC.
• Econometrics, Financial Econometrics, Bayesian Econometrics, Sequential Monte Carlo, Asset Pricing, Option Pricing
• Ph.D. in Economics, University of North Carolina, May 2001.
• Dissertation: Likelihood-based estimation techniques for continuous-time diffusion processes and applications to finance.
Adviser: L. Ronald Gallant
• Undergraduate Level: corporate Finance, business stats
• Graduate Level: financial econometrics, applied econometrics, time series econometrics