The Analytics of Risk Model Validation

George A. Christodoulakis, Stephen Satchell

• Publisher: Academic Press
• Year: 2007
• ISBN:
  9780080553887
• Topic: Economics
• Subtopic: Banks & Banking
• Index: Economics

Risk model validation is an emerging and important area of research, and has arisen because of Basel I and II. These regulatory initiatives require trading institutions and lending institutions to compute their reserve capital in a highly analytic way, based on the use of internal risk models. It is part of the regulatory structure that these risk models be validated both internally and externally, and there is a great shortage of information as to best practise. Editors Christodoulakis and Satchell collect papers that are beginning to appear by regulators, consultants, and academics, to provide the first collection that focuses on the quantitative side of model validation. The book covers the three main areas of risk: Credit Risk and Market and Operational Risk.*Risk model validation is a requirement of Basel I and II *The first collection of papers in this new and developing area of research *International authors cover model validation in credit, market, and operational risk

1

Determinants of small business default*

Sumit Agarwal^†; Souphala Chomsisengphet^‡; Chunlin Liu^¶
† Federal Reserve Bank of Chicago, Chicago, IL
^‡ Office of the Comptroller of the Currency, Washington, DC
^¶ College of Business Administration, University of Nevada, Reno, NV

Abstract

In this paper, we empirically validate the importance of owner and business credit risk characteristics in determining default behaviour of more than 31 000 small business loans by type and size. Our results indicate that both owner- and firm-specific characteristics are important predictors of overall small business default. However, owner characteristics are more important determinants of small business loans but not small business lines. We also differentiate between small and large business accounts. The results suggest that owner scores are better predictors of small firm default behaviours, whereas firm scores are better predictors of large firm default behaviour.

1 Introduction

In this chapter, we develop a small business default model to empirically validate the importance of owner and the business credit bureau scores while controlling for time to default, loan contract structure as well as macroeconomic and industry risk characteristics. In addition, several unique features associated with the dataset enable us to validate the importance of the owner and business credit bureau scores in predicting the small business default behaviour of (i) spot market loans versus credit lines and (ii) small businesses below $100 000 versus between $100 000 and $250 000.

Financial institutions regularly validate credit bureau scores for several reasons. First, bureau scores are generally built on static data, i.e. they do not account for the time to delinquency or default.¹ Second, bureau scores are built on national populations. However, in many instances, the target populations for the bureau scores are region-specific. This can cause deviation in the expected and actual performance of the scores. For example, customers of a certain region might be more sensitive to business cycles and so the scores in that region might behave quite differently during a recession. Third, the bureau scores may not differentiate between loan type (spot loans versus lines of credit) and loan size (below $100 K and above $100 K), i.e. they are designed as one-size-fits-all.

However, it is well documented that there are significant differences between bank spot loans (loans) and lines of credit (lines). For example, Strahan (1999) notes that firms utilize lines of credit to meet short-term liquidity needs, whereas spot loans primarily finance long-term investments. Agarwal et al. (2006) find that default performance of home equity loans and lines differ significantly. Hence, we assess whether there are any differences in the performance of small business loans and lines, and if so, what factors drive these differences?

Similarly, Berger et al. (2005) argue that credit availability, price and risk for small businesses with loan amounts below and above $100 K differ in many respects. Specifically, they suggest that scored le...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright page
• About the editors
• About the contributors
• Preface
• 1: Determinants of small business default
• 2: Validation of stress testing models
• 3: The validity of credit risk model validation methods
• 4: A moments-based procedure for evaluating risk forecasting models
• 5: Measuring concentration risk in credit portfolios
• 6: A Simple method for regulators to cross-check operational risk loss models for banks
• 7: Of the credibility of mapping and benchmarking credit risk estimates for internal rating systems
• 8: Analytic models of the ROC Curve: Applications to credit rating model validation
• 9: The validation of equity portfolio risk models
• 10: Dynamic risk analysis and risk model evaluation
• 11: Validation of internal rating systems and PD estimates
• Index