Joseph L. Breeden
Dr. Breeden has been designing and deploying risk management systems for loan portfolios since 1996. He founded Prescient Models in 2011, which focuses on portfolio and loan-level forecasting solutions for pricing, account management, CCAR, and CECL. He co-founded Deep Future Analytics in 2012 to serve credit unions and community banks. He is also the owner of auctionforecast.com, which predicts the values of fine wines using a proprietary database with over 2 million auction prices.
He is member of the board of directors of Upgrade, a San Francisco-based FinTech,, an Associate Editor for the Journal of Credit Risk and the Journal of Risk Model Validation and for the Journal of Credit Risk., and president of the Model Risk Managers’ International Association (mrmia.org).
Dr. Breeden has created models through the 1995 Mexican Peso Crisis, the 1997 Asian Economic Crisis, the 2001 Global Recession, the 2003 Hong Kong SARS Recession, and the 2007-2009 US Mortgage Crisis and Global Financial Crisis. These crises have provided Dr. Breeden with a rare perspective on crisis management and the analytics needs of executives for strategic decision-making.
Dr. Breeden earned a Ph.D. in physics, and has published over 50 academic articles, 7 patents, and 4 books. His new books, Living with CECL: Mortgage Modeling Alternatives and Living with CECL: The Modeling Dictionary were published in 2018.
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Articles by Joseph L. Breeden
Instabilities in Cox proportional hazards models in credit risk
The authors explore possible instabilities in applying Cox PH models and conduct numerical studies to demonstrate the same linear specification error from APC models an occur in Cox PH estimation.
Quantifying model selection risk in macroeconomic sensitivity models
The authors compare forecasts and uncertainties of three possibilities in model selection: the model selected as best, the best ensemble and the model not selected.
A survey of machine learning in credit risk
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.
Current expected credit loss procyclicality: it depends on the model
This work looks at a wide range of models to test the degree to which CECL is procyclical for different types of model.
Measuring economic cycles in data
This paper determines if enough data is available for forecasting or stress testing, a better measure of data length is required.