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Measurement of Expected Credit Loss | 12 Months Ended
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Dec. 31, 2020
Measurement of Expected Credit Loss [Abstract] |
Disclosure of Measurement Expected Credit Loss | 11. Measurement of Expected Credit Loss The expected credit loss of a financial instrument must reflect an unbiased estimate, the time value of money and a forward looking perspective (including the economic forecast). Therefore the recognition and measurement of ECL is highly complex and involves the use of significant analysis and estimation including formulation and incorporation of forward-looking economic conditions into ECL. Risk Parameters Adjusted by Macroeconomic Scenarios ECL must include forward-looking macroeconomic information. The Group uses the credit risk parameters PD, LGD and EAD in order to calculate the ECL for the credit portfolios. The Group´s methodological approach in order to incorporate the forward looking information aims to determine the relation between macroeconomic variables and risk parameters following three main steps:
| • |  | Step 1: Analysis and transformation of time series data.
| • |  | Step 2: For each dependent variable find conditional forecasting models that are economically consistent.
| • |  | Step 3: Select the best conditional forecasting model from the set of candidates defined in Step 2, based on their out of sample forecasting performance.
How economic scenarios are reflected in calculation of ECL Based on economic theory and analysis, the macroeconomic variables most directly relevant for explaining and forecasting the selected risk parameters are:
| • |  | The net income of families, corporates or public administrations.
| • |  | The payment amounts on the principal and interest on the outstanding loans.
The Group approximates these variables by using a proxy indicator from the set included of the macroeconomic scenarios provided by the economic research department. The Group BBVA selected the Real GDP Growth as the principal indicator, among other indicators such as unemployment rate, BADLAR rate, private consumption or country risk, because it captures the influence of all potentially relevant macro-financial scenario on internal PD, even though other variables could be used. Multiple scenario approach under IFRS 9 IFRS 9 requires calculating an unbiased probability weighted measurement of ECL by evaluating a range of possible outcomes, including forecasts of future economic conditions. The BBVA Research team produces forecasts of the macroeconomic variables under the baseline scenario, which are used in the rest of the related processes of the Group, such as budgeting, the internal capital adequacy assessment process (ICAAP) and risk appetite framework, stress testing, etc. Additionally, the BBVA Research team produces alternative scenarios to the baseline scenario so as to meet the requirements under the IFRS 9 standard. Alternative macroeconomic scenarios For each of the macro-financial variables (GDP or interest rate or exchange rate), BBVA Research produces three scenarios. Each of these scenarios corresponds to the expected value of a different area of the probabilistic distribution of the possible projections of the economic variables. The approach of the Group consists of using the scenario that is the most likely scenario, which is the baseline scenario, consistent with the rest of internal processes (ICAAP, Budgeting) and then applying upside and downside scenarios by taking into account the weighted average of the ECL determined by each of the scenarios. It is important to note that in general, it is expected that the effect of the overlay is to increase the ECL. It is possible to obtain an overlay that does not have that effect, whenever the relationship between macro scenarios and losses is linear. However, the overlay is not expected to reduce the ECL. COVID-19 Impact During the pandemic-related lockdown, the BCRA and the government issued several communications and decrees, pursuant to which customers within the portfolio of non-card financings benefitted from the deferral, without interest, of installments not paid in April 2020 until the final loan maturity. The table below summarizes the loan portfolio affected by the aforementioned measures and the related impact on contractual cash flows:
|  | Affected portfolio |  |  | Loss from changes in contractual cash flows (a) |
UVA-indexed mortgage loans |  |  | 16,568,485 |  |  |  | (451,177 | )
UVA-indexed pledge loans |  |  | 338,749 |  |  |  | (7,118 | )
(a) | Recognized in Net Interest Income.
Concerning credit cards, outstanding balances as of April 2020 and September 2020 were required to be automatically rescheduled with interest in nine equal and consecutive installments, with a three-month grace period. The due date deferral did not result in stage improvements in any case. The ECL measurement model parameters were not affected. The update of macroeconomic scenarios and non-linearity adjustment did not represent relevant impacts on the level of ECL. Credit quality ratios did not show deterioration as a result of the aid measures promoted by the national authorities. Given the pandemic and quarantine situation, no relevant impacts were recorded on ECL directly related to COVID 19.

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