Garland Durham

919 total citations
18 papers, 571 citations indexed

About

Garland Durham is a scholar working on Finance, Economics and Econometrics and Artificial Intelligence. According to data from OpenAlex, Garland Durham has authored 18 papers receiving a total of 571 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Finance, 7 papers in Economics and Econometrics and 4 papers in Artificial Intelligence. Recurrent topics in Garland Durham's work include Financial Risk and Volatility Modeling (11 papers), Stochastic processes and financial applications (9 papers) and Financial Markets and Investment Strategies (4 papers). Garland Durham is often cited by papers focused on Financial Risk and Volatility Modeling (11 papers), Stochastic processes and financial applications (9 papers) and Financial Markets and Investment Strategies (4 papers). Garland Durham collaborates with scholars based in United States, Australia and Netherlands. Garland Durham's co-authors include A. Ronald Gallant, John Geweke, Yang‐Ho Park, Pulak Ghosh, Susan Porter‐Hudak and Fallaw Sowell and has published in prestigious journals such as Journal of Financial Economics, Journal of Econometrics and Journal of Business and Economic Statistics.

In The Last Decade

Garland Durham

18 papers receiving 543 citations

Peers

Countries citing papers authored by Garland Durham

Since Specialization

Citations

This map shows the geographic impact of Garland Durham's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Garland Durham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Garland Durham more than expected).

Fields of papers citing papers by Garland Durham

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Garland Durham. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Garland Durham. The network helps show where Garland Durham may publish in the future.

Co-authorship network of co-authors of Garland Durham

This figure shows the co-authorship network connecting the top 25 collaborators of Garland Durham. A scholar is included among the top collaborators of Garland Durham based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Garland Durham. Garland Durham is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

#	Work	Indexed citations
1	Bayesian Inference for ARFIMA Models 2019 ·Journal of Time Series Analysis ·Garland Durham, John Geweke, Susan Porter‐Hudak, Fallaw Sowell	9
2	Sequentially adaptive Bayesian learning algorithms for inference and optimization 2018 ·Journal of Econometrics ·John Geweke, Garland Durham	6
3	A comment on Christoffersen, Jacobs, and Ornthanalai (2012), “Dynamic jump intensities and risk premiums: Evidence from S&P 500 returns and options” 2014 ·Journal of Financial Economics ·Garland Durham, John Geweke, Pulak Ghosh	8
4	\Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options" 2014 ·Garland Durham, John Geweke, Pulak Ghosh	3
5	Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments 2013 ·SSRN Electronic Journal ·Garland Durham, John Geweke	26
6	Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments 2013 ·arXiv (Cornell University) ·Garland Durham, John Geweke	6
7	Improving Asset Price Prediction When All Models are False 2013 ·Journal of Financial Econometrics ·Garland Durham, John Geweke	13
8	Bayesian Inference for Logistic Regression Models using Sequential Posterior Simulation 2013 ·SSRN Electronic Journal ·John Geweke, Garland Durham, (unknown)	2
9	Risk-neutral Modeling with Affine and Nonaffine Models 2013 ·Journal of Financial Econometrics ·Garland Durham	19
10	Beyond Stochastic Volatility and Jumps in Returns and Volatility 2012 ·DigitalCommons - CalPoly (California State Polytechnic University) ·Garland Durham, Yang‐Ho Park	1
11	Beyond Stochastic Volatility and Jumps in Returns and Volatility 2012 ·Journal of Business and Economic Statistics ·Garland Durham, Yang‐Ho Park	15
12	Massively Parallel Sequential Monte Carlo for Bayesian Inference 2011 ·SSRN Electronic Journal ·John Geweke, Garland Durham	26
13	Risk-Neutral Modelling With Affine and Non-Affine Models 2008 ·SSRN Electronic Journal ·Garland Durham	3
14	SV mixture models with application to S&P 500 index returns 2007 ·Journal of Financial Economics ·Garland Durham	57
15	Sv Mixture Models with Application to S&P 500 Index Returns 2006 ·SSRN Electronic Journal ·Garland Durham	1
16	Monte Carlo methods for estimating, smoothing, and filtering one- and two-factor stochastic volatility models 2005 ·Journal of Econometrics ·Garland Durham	64
17	Likelihood-based specification analysis of continuous-time models of the short-term interest rate 2003 ·Journal of Financial Economics ·Garland Durham	67
18	Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes 2002 ·Journal of Business and Economic Statistics ·Garland Durham, A. Ronald Gallant	245

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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