Credit risk grades are the primary input in the determination of the term structure of PD for exposures. Grupo Aval collects performance and default information about its credit risk exposures analyzed by jurisdiction or region, by type of product
and borrower, and by credit risk grade. For some portfolios, information purchased from external credit reference agencies may also be used.
Grupo Aval employs statistical models to analyze the data collected and generate estimates of the remaining lifetime PD of exposures and how these are expected to change as a result of the passage of time.
This analysis includes the identification and calibration of the relation between changes in default rates and changes in key macro-economic factors, as well as an in-depth analysis of the impact of certain other factors on the risk of default. For exposures to specific industries and/or regions, the analysis may extend to relevant commodity and/ or real estate prices.
For stage 1 the PD estimates the probability that the credit will default in the next 12 months, while the PD in stage 2 is the result of the probabilities for the remaining life of the credit. The probability in Stage 3 is defined as 100%.
Grupo Aval’s approach to incorporating forward-looking information into this assessment is discussed below.
Forward-Looking Information
Grupo Aval incorporates forward-looking information into both its assessment of whether the credit risk of an instrument has increased significantly since initial recognition and its measurement of ECLs. Grupo Aval formulates a ‘base case’
view of the future direction of relevant economic variables and a representative range of other possible forecast scenarios based on forecasts provided by economic experts and considering a forecast of multiple variables. This process involves developing two or more additional economic scenarios and considering the relative probabilities of each outcome.
The base case represents a most-likely outcome. It is aligned with information used by Grupo Aval for other purposes, such as strategic planning and budgeting. The other scenarios for Colombia represent more optimistic and more pessimistic outcomes. For the countries in Central America the other scenarios represent possible outcomes which are less probable than the “base case”.
Grupo Aval has identified and documented key drivers of credit risk and credit losses for each portfolio of financial instruments and, using an analysis of historical data, has estimated relationships between macro-economic variables and credit risk and credit losses.
The economic scenarios used as of December 31, 2018 include the following key indicators (among others) for Colombia for the years ending 31 December 2018 and 2019.
2018 2019
Scenario A Scenario B Scenario C Scenario A Scenario B Scenario C
Inflation 3.02 % 3.26 % 3.54 % 3.27 % 3.68 % 4.14 %
Interest rates 4.25 % 4.25 % 4.25 % 3.75 % 5.00 % 5.25 %
GDP growth 2.35 % 2.66 % 2.84 % 2.79 % 3.21 % 4.24 %
House prices (1.45)% 1.70 % 4.93 % (3.47)% 2.81 % 7.37 %
Unemployment rate 10.13 % 9.73 % 9.28 % 10.67 % 9.55 % 8.48 %
The economic scenarios used at 31 December 2018 included the following variations of key indicators (among others) for Guatemala.
2019
Scenario A Scenario B Scenario C
Inflation 3.23 % 5.16 % 6.67 %
Interest rates 0.30 % (0.15)% 0.47 %
GDP growth 2.76 % 3.54 % 2.33 %
Exchange rate 1.12 % 0.32 % 1.43 %
The economic scenarios used at 31 December 2018 included the following variations of key indicators (among others) for Honduras.
2019
Scenario A Scenario B Scenario C
Inflation 4.69 % 4.22 % 5.02 %
Interest rates 0.30 % (1.49)% 0.20 %
GDP growth 3.46 % 4.41 % 3.17 %
Exchange rate 4.15 % 3.92 % 4.35 %
The economic scenarios used at 31 December 2018 included the following variations of key indicators (among others) for El Salvador.
2019
Scenario A Scenario B Scenario C
Inflation 1.40 % 5.94 % 1.40 %
Interest rates 0.11 % 0.21 % 0.24 %
GDP growth 2.50 % 4.64 % 1.34 %
The economic scenarios used at 31 December 2018 included the following variations of key indicators (among others) for Nicaragua.
2019
Scenario A Scenario B Scenario C
Inflation 2.68 % 6.62 % 9.08 %
Interest rates 0.63 % 1.69 % 1.04 %
GDP growth 1.32 % (3.64)% (6.78)%
Exchange rate 5.68 % 7.00 % 6.26 %
The economic scenarios used at 31 December 2018 included the following variations of key indicators (among others) for Costa Rica.
2019
Scenario A Scenario B Scenario C
Inflation 2.20 % 3.62 % 11.41 %
Interest rates 0.20 % 7.72 % 9.63 %
GDP growth 3.30 % 2.11 % (0.95)%
Exchange rate 1.71 % 6.43 % 18.35 %
The economic scenarios used at 31 December 2018 included the following variations of key indicators (among others) probability of default and applying experienced credit judgment. Grupo Aval uses these grades with the purpose identifying significant increases in credit risk. Credit risk grades are defined using qualitative and quantitative factors that are indicative of the risk of default. These factors may vary depending on the nature of the exposure and the type of borrower.
Each exposure is allocated to a credit risk grade at initial recognition based on available information about the borrower.
Exposures are subject to ongoing monitoring, which may result in an exposure being moved to a different credit risk grade.
The monitoring typically involves use of the following data.
Loan Portfolio
LGD is a measure of the potential loss in the event of a default. To estimate LGD, Grupo Aval uses information of the collateral security / guarantee which covers each individual credit, when available. In any case, Grupo Aval uses historical and forward-looking information (the same information described above in II. PD – Probability of Default - Forward-Looking Information) to estimate the expected potential recovery in case of a default. The LGD is estimated in groups by type of credit, collateral security / guarantee or maturity.
IV. EAD – Exposure at Default
EAD represents the amount owed from a counterparty at the time of a possible default. For stage 2 Grupo Aval incorporates in the analysis of the exposure at default the probability of payments and increase in exposure during the lifetime of the credit.
These probabilities are estimated using the historical information collected by the financial subsidiaries and are grouped by type of product. The probabilities are constantly reviewed in order to accurately estimate them and calibrate them.