Credit scoring models play a fundamental role in the risk management practice at most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g. application, behavioural, collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level (i.e. consumer lending, but increasingly also small business lending). In this article, we analyze the history and new developments related to credit scoring models. We find that with the new Basel Capital Accord, credit scoring models have been remotivated and given unprecedented significance. Banks, in particular, and most financial institutions worldwide, have either recently developed or modified existing internal credit risk models to conform with the new rules and best practices recently updated in the market. Moreover, we analyze the key steps of the credit scoring model’s lifecycle (i.e. assessment, implementation, validation) highlighting the main requirement imposed by Basel II. We conclude that banks that are going to implement the most advanced approach to calculate their capital requirements under Basel II will need to increase their attention and consideration of credit scoring models in the next future.
Credit risk affects virtually every financial contract. Therefore the measurement, pricing and management of credit risk has received much attention from practitioners, who have a strong interest in accurately pricing and managing this kind of risk, financial economists, who have much to earn from the way such risk is priced in the market, and bank supervisors, who need to design minimum capital requirements that correctly reflect the credit risk of banks’ loan portfolios. Following the recent attempt of the Basel Committee on Banking Supervision to reform the capital adequacy framework by introducing risk-sensitive capital requirements, significant attention has been devoted to the subject of credit risk measurement by the international regulatory, academic and banking communities.
This paper analyses the impact of various assumptions on which most credit risk measurement models are presently based: namely, it analyses the association between aggregate default probabilities and the loss given default on bank loans and corporate bonds, and seeks to empirically explain this critical relationship. Moreover, it simulates the effects of this relationship on credit VaR models, as well as on the procyclicality effects of the new capital requirements proposed in 001 by the Basel Committee. Before we proceed with empirical and simulated results, however, the following section is dedicated to a brief review of the theoretical literature on credit risk modeling of he last three decades.
1. The relationship between default rates and recovery rates in credit risk modeling: a review of the theoretical and empirical literature
This literature review briefly summarises the way credit risk models, which have developed during the last thirty years, treat RR and, more specifically, their relationship with the PD of an obligor. These models can be divided into two main categories: (a) credit pricing models, and (b) portfolio credit value-at-risk (VaR) models. Credit pricing models can in turn be divided into three main approaches: (i) “first generation” structural-form models, (ii) “second generation” structural-form models, and (iii) reduced-form models. These three different approaches, together with their basic assumptions, advantages, drawbacks and empirical performance, are briefly outlined in the following paragraphs. Credit VaR models are then examined. Finally, the more recent studies explicitly modeling and empirically investigating the relationship between PD and RR are briefly analysed.
1.1 First generation structural-form models: the Merton approach
The first category of credit risk models are the ones based on the original framework developed by Merton (1974) using the principles of option pricing (Black and Scholes, 1973). In such a framework, the default process of a company is driven by the value of the company’s assets and the risk of a firm’s default is therefore explicitly linked to the variability in the firm’s asset value. The basic intuition behind the Merton model is relatively simple: default occurs when the value of a firm’s assets (the market value of the firm) is lower than that of its liabilities. The payment to the debtholders at the maturity of the debt is therefore the smaller of two quantities: the face value of the debt or the market value of the firm’s assets. Assuming that the company’s debt is entirely represented by a zero-coupon bond, if the value of the firm at maturity is greater than the face value of the bond, then the bondholder gets back the face value of the bond. However, if the value of the firm is less than the face value of the bond, the equityholders get nothing and the bondholder gets back the market value of the firm.
Unlock the rest of this paper by providing your email address below
You will receive an email with a link to download the paper.
Your request has been registered. Please check your email inbox for a link to the research paper. There is a possibility it may end up in your junk folder so please check there.
Other articles you might enjoy
A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies
Abstract Fifty years ago, in 1967, I completed my PhD dissertation…Read more
Assessing the Credit Worthiness of Italian SMEs and Mini-bond Issuers
Abstract A number of innovations have been introduced in the last five…Read more
Like these pieces? Register for our newsletter to get updates, learn when we publish more, and recieve the latest news from Wiserfunding straight to your inbox.
Thank you for subscribing to our newsletter you should receive an email shortly. Please check you junk folder if you cannot find it.
Join our newsletter and receive quarterly updates about Wiserfunding and our progress.