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.
Regulators are placing increased emphasis on the rigor by which banks model their income and balance sheet projections.
In this webinar, 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.
Risk modelers at banks often feel pressure to produce conservative, as opposed to strictly accurate, forecasts of a bank’s resilience in times of stress. Regulators typically frown on capital plans that have even the barest whiff of optimism.
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.