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Working Paper
Updated Primer on the Forward-Looking Analysis of Risk Events (FLARE) Model: A Top-Down Stress Test Model
This is an updated technical note describes the Forward-Looking Analysis of Risk Events (FLARE) model, which is a top-down model that helps assess how well the banking system is positioned to weather exogenous macroeconomic shocks. FLARE estimates banking system capital under varying macroeconomic scenarios, time horizons, and other systemic shocks.
Working Paper
Forecasting credit card portfolio losses in the Great Recession: a study in model risk
Credit card portfolios represent a significant component of the balance sheets of the largest US banks. The charge?off rate in this asset class increased drastically during the Great Recession. The recent economic downturn offers a unique opportunity to analyze the performance of credit risk models applied to credit card portfolios under conditions of economic stress. Specifically, we evaluate three potential sources of model risk: model specification, sample selection, and stress scenario selection. Our analysis indicates that model specifications that incorporate interactions between policy ...
Discussion Paper
Are Stress Tests Still Informative?
Since the height of the financial crisis, each year the Federal Reserve has disclosed the results of its stress tests, and stress testing has become ?business as usual? in the U.S. banking industry. In this post, we assess whether market participants find supervisory stress test disclosures informative. After half a decade, do the disclosures still contain information that the market finds valuable?
Working Paper
Credit risk modeling in segmented portfolios: an application to credit cards
The Great Recession offers a unique opportunity to analyze the performance of credit risk models under conditions of economic stress. We focus on the performance of models of credit risk applied to risk-segmented credit card portfolios. Specifically, we focus on models of default and loss and analyze three important sources of model risk: model selection, model specification, and sample selection. Forecast errors can be significant along any of these three model-risk dimensions. Simple linear regression models are not generally outperformed by more complex or stylized models. The impact of ...