Nicholas Polson


Nicholas G. Polson is a Bayesian Statistician who conducts research on Financial Econometrics, Markov Chain Monte Carlo, Particle Learning and Bayesian inference. Inspired by an interest in probability, Polson has added a number of new algorithms to the fields of Financial Econometrics including the Bayesian analysis of Stochastic Volatility and sequential Particle learning.

Polson's articles have appeared in a number of academic journals, such as the Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, Journal of Royal Statistical Society, Statistical Science, as well as Chance and the Wall Street Journal. His article, "Bayesian Analysis of Stochastic Volatility Models," was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics.

He also is the author of Bayesian Inference, edited with G. Tiao and published by Edward Elgar Publishing. His working papers include "Nonlinear Filtering and Learning Dynamics" (with Lars Hansen and Tom Sargent).

He is currently an associate editor for the Journal of the American Statistical Association.

Polson earned a master's degree with First Class Honours from Worcester College at Oxford University in 1984. He earned a PhD from the University of Nottingham in 1988. He joined Chicago Booth in 1991 after teaching at Carnegie Mellon University and Nottingham University.

Outside of academics, Polson enjoys horse racing, biking and swimming.



With M. Johannes, "MCMC Methods for Financial Econometrics," Handbook of Financial Econometrics (2010).

With C. Carvalho et al, "Particle Learning and Smoothing," Statistical Science (2010).

With B. Eraker and M. Johannes, "The Impact of Jumps in Volatility in Returns," Journal of Finance (2003).

With E. Jacquier and P. Rossi, "Bayesian Analysis of Stochastic Volatility Models," Journal of Business and Economic Statistics (1994, 2002).

"Convergence of Markov Chain Monte Carlo Algorithms," 5th Valencia Meeting on Bayesian Statistics.

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