J.D. Opdyke

GE Capital

J.D. Opdyke is Head of Operational Risk Modeling at GE Capital where he is leading all operational risk modeling and quantification (capital estimation and stress testing). He also leads enterprise-level model development for Economic Capital estimation, aggregation, and allocation across risk types (credit, market, operational, industrial, and insurance) across GEC. In 2016, the joint development team received GE’s prestigious Saul Dushman Award for “product or service that has created a significant impact on a GEbusiness.” J.D. previously has worked with some of the largest AMA banks globally during his 25 years as a quant, and his peer reviewed publications have earned multiple awards (including being voted Operational Risk Paper of the Year in 2012 and 2015); they span number theory/combinatorics, statistical/econometric modeling of VaR-based capital estimation, statistical finance, applied statistics, computational statistics, and applied econometrics. J.D. has been invited to present his work at numerous risk conferences, where panel discussants on his research have included senior regulators. Finally, his expertise using SAS is unparalleled: J.D. has numerous peer reviewed statistical publications that develop and implement Base SAS algorithms that are orders of magnitude faster than SAS’s built-in procedures (“SAS Procs” – e.g. decreasing runtimes from 22 hours to 81 seconds for globally used statistical procedures). J.D. has earned undergraduate and graduate degrees from Yale and Harvard Universities, respectively, and has completed post-graduate work in robust statistics in the graduate mathematics department at MIT.

Articles by J.D. Opdyke

Original research

Fast, accurate and straightforward extreme quantiles of compound loss distributions

In this paper, the author presents an easy-to-implement, fast and accurate method for approximating extreme quantiles of compound loss distributions (frequency + severity), which are commonly used in insurance and operational risk capital models.

04 Oct 2017

Risk management

Estimating operational risk capital with greater accuracy, precision and robustness

19 Dec 2014

Original research

Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the promise of robust statistics

27 Sep 2012

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