ارتباط دادن پویایی های اقتصادی جهانی به مدل همبستگی ریسک اعتباری خاص-آفریقایی جنوبی

In order to address practical questions in credit portfolio management it is necessary 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. This paper builds on the methodology proposed by Pesaran, Schuermann, and Weiner [Pesaran, M.H., Schuermann, T., and Weiner, S.M., (2004), Modeling regional interdependencies using a global error correcting macroeconometric model, Journal of Business and Economic Statistics, 22, 2, 129–169.] and supplemented by Pesaran, Schuermann, Treutler and Weiner [Pesaran, M.H., Schuermann, T., Treutler, B., and Weiner, S.M., (2006), Macroeconomic dynamics and credit risk: a global perspective, Journal of Money, Credit, and Banking, Volume 38, Number 5, August 2006, 1211–1261.] which has made a significant advance in credit risk modelling in that it avoids the use of proprietary balance sheet and distance-to-default data, focusing on credit ratings which are more freely available. In this paper a country-specific macroeconometric risk-driver engine which is compatible with and could feed into the GVAR model and framework of PSW (2004) is constructed, using vector error-correcting (VECM) techniques. This allows 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 to the GVAR model. The set of domestic factors is extended 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, the results indicate that it is possible to construct a South African component for the GVAR model that can easily be integrated into the global component. From a practical application perspective the framework and model is particularly appealing since it can be used as a theoretically consistent correlation model within a South African-specific credit portfolio management tool.

Since the early 1990s intense competition for market share has motivated banks across the globe to allow credit portfolios to become less diversified (across all dimensions – country, industry, sector and size) and accept lesser quality assets on their books without being adequately compensated for the higher risk. 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 and credit and capital management practices. From a credit portfolio perspective it is essential that portfolio managers understand the dynamics and interaction of two key elements of their exposures, namely, systematic and idiosyncratic risk. Systematic risk refers to the co-movement and risk associated with the relationship between exposures and the general economic environment while idiosyncratic risk refers to exposure-specific risk factors such as leverage or cash flow ratios. In order to perform meaningful credit portfolio management it is necessary to be able the to link 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. In general, this relationship is referred to as credit correlation. Conceptually one would expect that the correlation of individual exposures with the business cycle would imply that in an economic downturn portfolio credit risk is increased by the simultaneous increase in risk of exposures which are sensitive to the same macroeconomic variables. A better understanding of these correlations would not only allow better capital budgeting over the business cycle but would also allow portfolio managers to execute and more effectively exploit market opportunities. The methodology proposed by Pesaran, Schuermann and Weiner (2004) (PSW) and Pesaran, Schuermann, Treutler and Weiner (2006) (PSTW) has made a significant advance in credit risk modelling by linking an adjusted structural default model to a structural global econometric model (their global vector autoregressive (GVAR) model), from which conditional credit risk analysis and portfolio management can be done. In general the methodology can be described as comprising two parts: the first is a macroeconomic simulation engine (normally refer to as the “correlation model” in the credit portfolio literature) while the second part is a set of firm-specific default models which translates the macroeconomic conditions into credit risk outcomes. This paper investigates the possibility of constructing a country-specific macroeconometric risk-driver engine which is compatible with the GVAR model and framework. 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, because such a model can easily be linked to the GVAR model. The paper is structured as follows. The first and second sections discuss the basic problems faced by bank credit portfolio managers across the globe, then highlight the methodology and framework proposed by PSW (2004) and PSTW (2006) to develop a consistent econometric framework and model to estimate the dynamics of global credit markets, which is shown in Section 3. Section 4 provides an in-depth discussion on the data construction process, estimation results, dynamic properties and forecasting ability of the proposed South African-specific vector error-correcting model (VECM). The paper concludes in Section 5 by arguing that the proposed model could be used as a stand-alone correlation model in a South African-specific credit portfolio model or could be linked to the GVAR model as part of a global credit portfolio management tool.

Based on the methodology proposed by PSW (2004) and PSTW (2006) this paper proposes a South African-specific credit-market correlation model which can be linked to the current GVAR model proposed by PSW (2004). The model is based on a VECM system which includes credit-market related domestic and global economic variables. Although PSW (2004) only impose statistically exact identifying restrictions on their individual country VECM model, here it is proposed that a set of theoretically consistent over-identifying restrictions be placed on the VECM system in order to identify coefficient estimates that conform to theoretical expectations. Although it is not the aim of the model to provide forecast results for global factors, but rather to provide South African-specific elements to the GVAR model, in- and out-of-sample forecasts show that the stochastic simulations are in line with actual variable realisation and expectations. As such, it is argued that the correlation model could be employed as a stand-alone model within a South African-specific credit portfolio management tool.