5.2.1
| In order to proceed with macroeconomic modelling, institutions should collect several types of time series. This data collection process should follow the requirements articulated in the MMS.
|
5.2.2
| At a minimum, these time series should be built with monthly or quarterly time steps over an overall period of five (5) years covering, at least, one economic cycle. Institutions should aim to build longer data series. The following data should be collected.
|
5.2.3
| Macro variables: Institutions should obtain macro variables from one or several external reliable sources.
|
| | (i)
| The scope of variables should be broad and capture appropriately the evolution of the economic environment. They will typically include national accounts (overall and non-oil, nominal and real), oil production, real estate sector variables, CPI, crude oil price and stock price indexes. |
| | (ii)
| Institutions should collect macro data pertaining to all jurisdictions where they have material exposures (at least greater than ten percent (10%) of the total lending book, excluding governments and financial institutions). |
| | (iii)
| Institutions should document the nature of the collected variables, covering at a minimum, for each variable, a clear definition, its unit, currency, source, frequency, and extraction date. |
| | (iv)
| Institutions should ensure that all variables employed for modelling will also be available for forecasting.
|
5.2.4
| Historical default rates: Macro-PD models (or macro-to-credit index models) stand at the end of a chain of models. They are employed to make adjustments to the output of TTC PD models, themselves linked to rating models. Therefore the default data used for macro-PD modelling should reflect the institution’s own experience. If external default data points are used, justification should be provided. Finally, institutions are encouraged to also include restructuring and/or rescheduling events in their data to better capture the relationship between obligor creditworthiness and the economic environment.
|
5.2.5
| Historical recovery rates: Macro-LGD models are designed to adjust the output of TTC LGD models. Consequently, the recovery data employed for macro-LGD modelling should reflect the institution’s own experience. If external recovery data points are used, justification should be provided.
|
5.2.6
| Macro data series are mostly available with quarterly or even annual data points and rarely with monthly time intervals. Consequently, interpolation techniques may need to be developed. Institution should include interpolation methodology as part of the data transformation step. Such interpolation should be documented and included in the validation process.
|