Weighted-Probability ECL

Inspired by wonderful discussion from this blog post about “IFRS 9 Scenario Weights for ECL: A Simple Rule” Article, I would like to extend the discussion to my experience and understanding.

Under IFRS 9, Expected Credit Loss (ECL) calculations require the use of scenario weights to reflect the probability of different economic outcomes. These weights range from simple to complex depending on the sophistication of the modeling approach.

Simple: 3-scenario approach

In the context of IFRS 9, Expected Credit Loss (ECL) is the probability-weighted estimate of credit losses over the expected life of a financial instrument. It is typically calculated using three scenarios:

Base Scenario: This represents the most likely economic outcome and is assigned the highest weight, usually around 50%.

Optimistic Scenario: This scenario considers a better-than-expected economic performance and receives a lower weight, typically around 25%.

Pessimistic Scenario: This scenario accounts for a worse-than-expected economic situation and is also assigned a lower weight, typically around 25%.

Each scenario incorporates relevant macroeconomic overlay information to reflect the varying economic conditions. The weights assigned to each scenario directly impact the final calculated ECL. The base scenario, being the most probable, has the most significant influence on the final ECL value.

It’s important to note that the specific probabilities assigned to each scenario can vary depending on the entity’s specific circumstances and risk assessment. The example probabilities of 50%, 25%, and 25% are just common starting points.

Complex: Monte Carlo Simulation for multiple scenarios approach

What if we sought a more flexible approach than relying on three static probability weights and a limited number of macroeconomic scenarios? The answer lies in Monte Carlo Simulation (MCS). With MCS, we define an objective function that encapsulates our prediction of the ECL distribution. Here’s a summary of the MCS process for calculating weighted ECL:<br>

Step 1: Objective Function Definition: Create a function that calculates the difference (error) between predicted probabilities and given probabilities. This function typically returns the sum of squared errors.

Step 2: Random Parameter Generation & Optimization: MCS generates numerous sets of random initial parameters for the chosen probability distribution. It then iteratively optimizes these parameters to minimize the objective function’s value.

Step 3: Optimal Parameter Identification: The parameters resulting in the lowest error (smallest objective function value) are identified. These parameters represent the best fit for the probability distribution used in step 1.

Step 4: ECL Calculation: Using the optimized parameters, the Expected Credit Loss (ECL) is calculated for each simulated scenario. These ECL values are then weighted and aggregated to produce the final weighted ECL estimate.

Advantages of Applying Monte Carlo Simulation to ECL Calculation:

• Real-World Reflection: MCS effectively simulates the inherent uncertainty and variability of real-world risk scenarios, offering a more accurate representation of potential outcomes.

• Modeling Flexibility: The choice of probability distribution in the objective function can be tailored based on expert judgment and specific risk beliefs, allowing for greater adaptability in the modeling process.

• Improved Accuracy: Increasing the number of simulations enhances the accuracy of the final ECL estimate by better representing the true underlying distribution.

By incorporating MCS, risk modellers can move beyond static assumptions and embrace a more dynamic and nuanced approach to ECL calculation, ultimately leading to more informed decision-making and a deeper understanding of potential credit risks.

You can contact me for sample Python code that I have done.