Compute EL and RWA from loan data
Problem Statement: Portfolio Risk Calculation
You are tasked with computing key risk metrics for a loan portfolio based on provided loan-level data. This involves calculating the Portfolio Expected Loss (EL) and Risk-Weighted Assets (RWA).
Input Data
You will receive an anonymized dataset containing individual loan exposures. Each loan record will include at least the following fields:
- PD (Probability of Default): The one-year probability of default, expressed as a decimal (e.g., 0.01 for 1%).
- LGD (Loss Given Default): The expected loss given a default event, under downturn conditions, expressed as a decimal between 0 and 1.
- EAD (Exposure at Default): The total exposure at the time of default, in currency units. For off-balance-sheet items, EAD must incorporate Credit Conversion Factors (CCFs), or be derived from committed/undrawn amounts and CCFs.
Deliverables
Your solution must compute and present:
- Portfolio Expected Loss (EL)
- Risk-Weighted Assets (RWA)
Additionally, you must clearly articulate the following aspects of your approach:
- Assumptions: Detail any assumptions made regarding the regulatory framework (e.g., Basel II/III), asset class characteristics, loan maturity, and parameter floors.
- Formulas Used: Explicitly state the mathematical formulas applied for calculating EL and RWA.
- Aggregation Approach: Describe how individual loan-level calculations are aggregated to derive portfolio-level metrics.
- Off-Balance-Sheet Exposures: Explain the specific treatment and methodology for incorporating off-balance-sheet items into the calculations.
- Sensitivity and Validation Checks: Outline the types of sensitivity analyses and validation checks that would be performed to ensure the robustness and accuracy of the computed metrics.
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Core dilemma
Balancing the precision and regulatory compliance of complex financial risk models with the computational scalability and auditability required for large, dynamic loan portfolios.
3 tradeoffs and 3 stress tests locked
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Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Compute EL and RWA from loan data
Problem Statement: Portfolio Risk Calculation
You are tasked with computing key risk metrics for a loan portfolio based on provided loan-level data. This involves calculating the Portfolio Expected Loss (EL) and Risk-Weighted Assets (RWA).
Input Data
You will receive an anonymized dataset containing individual loan exposures. Each loan record will include at least the following fields:
- PD (Probability of Default): The one-year probability of default, expressed as a decimal (e.g., 0.01 for 1%).
- LGD (Loss Given Default): The expected loss given a default event, under downturn conditions, expressed as a decimal between 0 and 1.
- EAD (Exposure at Default): The total exposure at the time of default, in currency units. For off-balance-sheet items, EAD must incorporate Credit Conversion Factors (CCFs), or be derived from committed/undrawn amounts and CCFs.
Deliverables
Your solution must compute and present:
- Portfolio Expected Loss (EL)
- Risk-Weighted Assets (RWA)
Additionally, you must clearly articulate the following aspects of your approach:
- Assumptions: Detail any assumptions made regarding the regulatory framework (e.g., Basel II/III), asset class characteristics, loan maturity, and parameter floors.
- Formulas Used: Explicitly state the mathematical formulas applied for calculating EL and RWA.
- Aggregation Approach: Describe how individual loan-level calculations are aggregated to derive portfolio-level metrics.
- Off-Balance-Sheet Exposures: Explain the specific treatment and methodology for incorporating off-balance-sheet items into the calculations.
- Sensitivity and Validation Checks: Outline the types of sensitivity analyses and validation checks that would be performed to ensure the robustness and accuracy of the computed metrics.