Academic journal article International Advances in Economic Research

Bank Credit Risk Management and Migration Analysis; Conditioning Transition Matrices on the Stage of the Business Cycle

Academic journal article International Advances in Economic Research

Bank Credit Risk Management and Migration Analysis; Conditioning Transition Matrices on the Stage of the Business Cycle

Article excerpt

JEL El * E020 * E32 *C110.C130*C150

Introduction

Credit risk is defined by the Basel Committee on Banking Supervision (BCBS 2000, par.2, p.1) as the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms." It is usually associated with loans and securities that generate interest income, thus being the primary source of bank revenue. Banks arc increasingly facing credit risk (or counterparty risk) in various financial instruments that are not loans, including inter-bank transactions, trade financing, foreign exchange transactions, financial futures, swaps, bonds, equities, and options.

Under the Basel II guidelines (also incorporated in Basel III), banks are allowed to use their own estimated risk parameters for the purpose of calculating regulatory capital. This is known as the Internal-Ratings-Based (IRB) approach to capital requirements for credit risk. Only banks meeting certain minimum conditions, disclosure requirements, and approval from their national supervisor are allowed to use this approach in estimating capital for various risk exposures (BCBS 2004). The IRB approach relies on a bank's own assessment of its counterparties and exposures in order to calculate capital requirements for credit risk. Thus, it is necessary to devise a method to accurately estimate credit rating migration probabilities. Such a concept relies upon the 'incremental risk charge' (IRC) proposed by regulators (BCBS 2009), requiring banks to calculate a one-year 99.9 % VaR (value at risk) for losses from credit sensitive products in the trading book. Banks are required to consider rating changes as well as defaults. As a result of the instruments subject to the IRC being in the trading book, it is assumed that a bank will have the opportunity to rebalance its portfolio during the course of the year so that default risk can be mitigated (Hull 2012).

Migration analysis that shows changes among the classes of a lender's risk-rating or credit scoring system is a probability-based measurement concept for credit risk. The concept considers upgrades and downgrades in the credit quality of an entire loan portfolio as well as the potential for significant financial stress and loan default (Altman and Saunders 1998). Indeed, recent turmoil in the capital markets has led to a sharp rise in the number of negative rating actions taken by the leading rating agencies, signalling deterioration in the credit quality of firms affected by adverse economic conditions. These credit quality dynamics highlight the importance of credit migration modelling as an integral part of modern credit risk solutions (Tsaig et al. 2011).

A number of relevant past studies have distinguished real economic activity into separate phases or regimes. In their approach, booms are separated from contractions in order to facilitate the observation of the parameters used in their model by examining the weighted impact of each economic phase on these parameters. For instance, this applies to empirical research initially in conditional mean dynamics of interest rates (Hamilton 1988; Cecchetti et al. 1990) or exchange rates (Engel and Hamilton 1990), conditional variance dynamics of stock returns (Hamilton and Susmel 1994), and, more recently, in the specific context of credit rating migration behaviour (Bangia et al. 2002). Belkin et al. (1998) and Kim (1999) have implemented a one-factor model, whereby ratings respond to business cycle shifts. Wei (2003) subsequently extended this model into a multifactor credit migration framework.

The core objective of the paper is to model corporate ratings migration probabilities, taking into account both the IRB bank internal rating data and the state of the economic cycle in which borrowing firms operate. Once the model is specified with its parameters and data have been collected, one is in a position to evaluate its fit; that is, how well it fits the observed data. …

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