Introducing forward looking information to expected credit loss under IFRS 9 : Different time series approaches for Turkish banking system

Credit risk is the most critical type of risk to manage for banks, which are the most important part of the financial system. It is expected that the capital of banks, whose most important activity is to provide loans, will be well structured and will be at a level that will protect the bank against risks. Credit risk is an element that must be managed not only against its realization, but also against the possibility of its occurrence. Credit risk should be managed against both internal factors (such as bank portfolio characteristics, characteristics of bank customers) and external factors (such as economic conjuncture, political and political outlook, natural disasters). When considering the credit risk management against the external factors, finding a relationship between the risk factors such as non-performing loans ratios of the banks and key macroeconomic indicators and modelling them become an issue for the banks. It has become critical to develop time series models by establishing a relationship between credit risk factors and macroeconomic indicators, in order to include forward-looking information in expected credit loss calculations in accordance with the International Financial Reporting Standard (IFRS 9), which entered into force at the beginning of 2018 and both as a stress test. In this study, it has been tried to measure the effectiveness of different time series algorithms comparatively, especially since a certain method is not imposed within the scope of IFRS 9 standard. Different time series models were established between the non-performing loan rates published by the Banks Association of Turkey and all potential macroeconomic indicators officially published, and diagnostic tests were applied to measure the robustness of the models. As algorithms, Stepwise Regression, Autoregressive Distributed Lag Model (ARDL) and Extreme Gradient Boosting Model (XGBoost) were chosen. As a result of this study, Autoregressive Distributed Lag Model showed the best performance. As expected credit loss models are regulative models, they are subject to the examination of many regulatory authorities. It has been demonstrated that ARLD model which also has high explainability and interpretability, is a method that can be used frequently by banks.