Director, Economic Research
Brian is a director in the specialized modeling group at Moody’s Analytics, where he develops new products for forecasting and stress testing purposes and leads external model validation projects. He has a PhD and MA in economics from the University of Michigan.
In this webinar, Mark Zandi and the Moody's Analytics team discuss the impact of the wealth effect on economic expansion and quantify econometric estimates based on data from Visa and Equifax.
In this webinar, we demonstrate how forecasts based on industry data can be used to generate an objective benchmark of a bank’s performance under baseline and stressed scenarios. We demonstrate results though case study of regional banks, peer groups, and larger CCAR-sized institutions.
In this presentation, we demonstrate how forecasts based on industry data can be used to generate an objective benchmark of a bank's performance under baseline and stressed scenarios. We demonstrate results though case study of regional banks, peer groups, and larger CCAR-sized institutions.
Regulators are placing increased emphasis on the rigor by which banks model their income and balance sheet projections.
In this article we demonstrate how to combine our forecasts of bank financial statements with internal data to produce forecasts that better reflect the macroeconomic environment posited under the various Comprehensive Capital Analysis and Review scenarios.
In this presentation, Dr. Brian Poi, Director, Economic Research, demonstrates how forecasts based on industry data can be used to generate an objective benchmark for internally generated forecasts.
Multicollinearity, the phenomenon in which the regressors of a model are correlated with each other, apparently causes a lot of confusion among practitioners and users of stress testing models. This article seeks to dispel this confusion.
To capture a bank’s real capacity to withstand an adverse economic scenario, the best approach is to start with forecasts produced only for accuracy and then apply a conservative overlay as regulation requires. Such forecasts capture mitigating forces such as flight to safety.
Banks face the difficult task of building hundreds of forecasting models that disentangle macroeconomic effects from bank-specific decisions. We propose an approach based on consistently reported industry data that simplifies the modeler’s task and at the same time increases forecast accuracy.