5.1 Scope
| 5.1.1 | Macroeconomic models (“macro models”) are primarily employed by UAE institutions for the estimation of Expected Credit Loss (“ECL”) and for internal and regulatory stress testing purpose. The objective of this section is to provide guidance and set the Central Bank’s expectation applicable to all macroeconomic models used by institutions. The practices described in this section are in compliance with current accounting principles. | |
| 5.1.2 | In this context, macro models are defined as statistical constructions linking macro variables (“independent variables”) to an observed risk or business metrics, typically PD, Credit Index, LGD, cost of funds, or revenues (“dependent variables”). Several types of macro models exist. A common approach relies on time series regression techniques, which is the main focus of this section on macro models. Other approaches include (i) for retail clients, vintage-level logistic regression models using directly macroeconomic drivers as inputs and (ii) for corporate clients, structural models using macro variables as inputs. | |
| 5.1.3 | Irrespective of the methodology employed, institutions should use judgement and critical thinking, where statistical techniques are coupled with causality analysis. Institutions should justify and balance (i) statistical performance, (ii) business intuition, (iii) economic meaning, and (iv) implementation constraints. Statistical methods and procedures will only provide part of the solution. Therefore, rigorous modelling techniques should be coupled with sound economic and business judgement in order to build and choose the most appropriate models. The key modelling choices and the thought process for model selection should be rigorously documented and presented to the Model Oversight Committee. | |
| 5.1.4 | The modelling decision process should be driven by explorations, investigations, and comparisons between several possible methods. Note that time series regression models have been proven to yield the most intuitive results over other techniques. | |
| 5.1.5 | When developing macro models, institutions should follow a clear sequential approach with a waterfall of steps. Depending on the outcome, some steps may need to be repeated. Each step should be documented and subsequently independently validated. In particular, for time series regression models, the process should include, at a minimum, the steps presented in the table below. | |
Table 8: Sequential steps for the development of macro models | ||
| # | Step |
| 1 | Data collection |
| 2 | Analysis of the dependent variables |
| 3 | Analysis of the macro variables |
| 4 | Variable transformations |
| 5 | Correlation analysis |
| 6 | Model construction |
| 7 | Statistical tests |
| 8 | Model selection |
| 9 | Monitoring and validation |
| 10 | Scenario forecasting |