Linking global economic dynamics to a South African specific credit portfolio

Abstract

Driven by intense competition for market share banks across the globe have increasingly allowed credit portfolios to become less diversified (across all dimensions - country, industry, sector and size) and were willing to accept lesser quality assets on their books. As a result, even well capitalised banks could come under severe solvency pressure when global economic conditions turn. The banking industry have realised the need for more sophisticated loan origination, credit and capital management practices. To this end, the reforms introduced by the Bank of International Settlement through the New Basel Accord (Basel II) aim to include exposure specific credit risk characteristics within the regulatory capital requirement framework. The new regulatory capital framework still does not allow diversification and concentration risk to be fully recognised within the credit portfolio because it does not account for systematic and idiosyncratic risk in a multifactor framework. The core principle for addressing practical questions in credit portfolio management is enclosed in the ability to link the cyclical or systematic components of firm credit risk with the firm’s own idiosyncratic credit risk as well as the systematic credit risk component of every other exposure in the portfolio. Simple structural credit portfolio management approaches have opted to represent the general economy or systematic risk by a single risk factor. The systematic component of all exposures, the process generating asset values and therefore the default thresholds are homogeneous across all firms. Indeed, this Asymptotic Single Risk Factor (ASRF) model has been the foundation for Basel II. While the ASRF framework is appealing due to its analytical closed-form properties for regulatory and generally universal application in large portfolios, the single risk factor characteristic is also its major drawback. Essentially it does not allow for enough flexibility in answering real life questions. Commercially available credit portfolio models make an effort to address this by introducing more systematic factors in the asset value generating process but from a practitioner’s point of view, these models are often a “black-box” allowing little economic meaning or inference to be attributed to systematic factors. The methodology proposed by Pesaran, Schuermann, and Weiner (2004) and supplemented by Pesaran, Schuermann, Treutler and Weiner (2006) has made a significant advance in credit risk modelling in that it avoids the use of proprietary balance sheet and distance to default data, focussing on credit ratings which are more freely available. Linking an adjusted structural default model to a structural global econometric model (GVAR) credit risk analysis and portfolio management can be done through the use of a conditional loss distribution estimation and simulation process. The GVAR model used in Pesaran et al. (2004) comprises a total of 25 countries which is grouped into 11 regions and accounts for 80 per cent of world production. In the case of South Africa the GVAR model lacks applicability since it does not include an African component. In this paper we construct a country specific macro-econometric risk driver engine which is compatible with and could feed into the GVAR model and framework of PSW (2004) using vector error correcting (VECM) techniques. This will allow conditional loss estimation of a South African specific credit portfolio but also opens the door for credit portfolio modelling on a global scale as such a model can easily be linked into the GVAR model. We extend the set of domestic factors beyond those used in PSW (2004) in such a way that the risk driver model is applicable for both retail and corporate credit risk. As such, the model can be applied to a total bank balance sheet, incorporating the correlation and diversification between both retail and corporate credit exposures. Assuming statistical over-identification restrictions, our results indicate that it is possible to construct a South African component for the GVAR model and that such a component could easily be integrated into a global content. From a practical application perspective the framework and model is particularly appealing since it could be used as a theoretically consistent correlation model within a South African specific credit portfolio management tool.