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Garland Durham
Garland Durham
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Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes
GB Durham, AR Gallant
Journal of Business & Economic Statistics 20 (3), 297-338, 2002
5762002
Likelihood-based specification analysis of continuous-time models of the short-term interest rate
GB Durham
Journal of Financial Economics 70 (3), 463-487, 2003
1562003
Monte Carlo methods for estimating, smoothing, and filtering one-and two-factor stochastic volatility models
GB Durham
Journal of Econometrics 133 (1), 273-305, 2006
1142006
Adaptive sequential posterior simulators for massively parallel computing environments
G Durham, J Geweke
Bayesian model comparison, 1-44, 2014
113*2014
SV mixture models with application to S&P 500 index returns
GB Durham
Journal of Financial Economics 85 (3), 822-856, 2007
902007
Risk-neutral modeling with affine and nonaffine models
GB Durham
Journal of Financial Econometrics 11 (4), 650-681, 2013
272013
Beyond stochastic volatility and jumps in returns and volatility
G Durham, YH Park
Journal of Business & Economic Statistics 31 (1), 107-121, 2013
232013
Improving asset price prediction when all models are false
G Durham, J Geweke
Journal of Financial Econometrics 12 (2), 278-306, 2013
222013
Numerical techniques for simulated maximum likelihood estimation of stochastic differential equations
GB Durham, AR Gallant
Journal of Business and Economic Statistics 20 (3), 297-316, 2002
172002
Bayesian inference for ARFIMA models
G Durham, J Geweke, S Porter‐Hudak, F Sowell
Journal of Time Series Analysis 40 (4), 388-410, 2019
152019
Sequentially adaptive Bayesian learning algorithms for inference and optimization
J Geweke, G Durham
Journal of econometrics 210 (1), 4-25, 2019
152019
A comment on Christoffersen, Jacobs, and Ornthanalai (2012),“Dynamic jump intensities and risk premiums: Evidence from S&P 500 returns and options”
G Durham, J Geweke, P Ghosh
Journal of Financial Economics 115 (1), 210-214, 2015
132015
Bayesian inference for logistic regression models using sequential posterior simulation
J Geweke, G Durham, H Xu
SSRN, 2013
42013
Sequentially Adaptive Bayesian Learning Algorithms for Inference and Optimization
G Durham, J Geweke
12015
Bayesian inference for logistic regression models using sequential posterior simulation
J Geweke, G Durham, H Xu
Current Trends in Bayesian Methodology with Applications, 289-312, 2015
12015
Comment: Mikhail Chernov
M Chernov
Journal of Business & Economic Statistics 21 (4), 485, 2003
1*2003
Rényi Divergence and Monte Carlo Integration
J Geweke, G Durham
Advances in Info-Metrics: Information and Information Processing across …, 2020
2020
Statistical Methods for Stochastic Differential Equations
GB Durham
Journal of the American Statistical Association 109 (505), 453-454, 2014
2014
[Iterative and Recursive Estimation in Structural Nonadaptive Models]: Comment
G Durham, J Geweke
Journal of Business & Economic Statistics 21 (4), 490-492, 2003
2003
Comment [7](multiple letters)
H Zhou, GB Durham, AR Gallant
Journal of Business and Economic Statistics 20 (3), 332-335+ 338, 2002
2002
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