A0431
Title: A principal component regression method to incorporate macroeconomic forecasts in modelling expected credit loss
Authors: Gerbrand Breed - North-West University (South Africa)
Helgard Raubenheimer - North-West University (South Africa) [presenting]
Tanja Verster - North-West University (South Africa)
Abstract: During the financial crisis, the International Accounting Standard Board (IASB) and Financial Accounting Standard Board (FASB) joined their efforts to redesign accounting standards for an improved and simplified expected credit loss (ECL) framework and released the International Financial Reporting Standard (IFRS) 9 in 2014. The quantification of ECL is often broken down into its three components, namely the probability of default (PD), loss given default (LGD) and exposure at default (EAD). The IFRS9 standard requires that the PD model accommodates the influence of the current and the forecasted macroeconomic conditions on default rates. This enables a determination of forward-looking estimates on impairments. A methodology is proposed based on principal component regression (PCR) to adjust IFRS 9 PD term structures for macroeconomic forecasts. We propose that a credit risk index (CRI) is derived from historic defaults to approximate the default behaviour of the portfolio. PCR is used to model the CRI with the macroeconomic variables as the set of explanatory variables. A novice all-subset variable selection is proposed incorporating business decisions. We demonstrate the method's advantages on a real-world banking data set and compare it to several other technics.
