Stress Testing Counterparty Credit Risk Exposures
Credit → Counterparty Analysis
| 2025-11-13 23:29:27
| 2025-11-13 23:29:27
Introduction to Stress Testing Counterparty Credit Risk Exposures
Foundations and Importance of Stress Testing for Counterparty Credit Risk
Overview
- Stress testing is crucial to assess resilience of exposures under extreme market conditions.
- It helps identify directional sensitivities and concentration risks in counterparty portfolios.
- We will explore methodologies, key drivers, and results interpretation in the following slides.
- Key insights include multifactor stress frameworks and the role of wrong-way risk.
Key Concepts and Drivers in Stress Testing CCR
Understanding the Mechanics and Risk Factors in CCR Stress Testing
Main Points
- Stress tests consider exposures to principal market risk factors: interest rates, FX, equities, credit spreads, commodities.
- Multifactor scenarios simulate severe macroeconomic or liquidity shocks plus market impacts of large position liquidations.
- Wrong-way risk, the correlation of counterparty default likelihood and exposure, complicates risk capture.
- Scenario analysis complements traditional risk metrics like VaR, improving capital allocation and risk management.
Graphical Analysis – Stress Testing CCR Exposures: Multifactor Scenario Impact
Visualizing Exposure Changes Under Multifactor Stress Scenarios
Context and Interpretation
- This layered chart presents hypothetical changes in maximum and minimum counterparty exposures alongside credit spread impacts over three months under stress.
- Shows potential widening of exposure ranges and concurrent credit spread increases, indicating elevated risk.
- Highlights the importance of monitoring joint movements in exposure and credit quality.
- Supports assessing tail risk and informs stress scenario design.
Figure: Exposure and Credit Spread Variation Under Stress
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Interpreting Key Quantitative Inputs and Risk Metrics
Key Discussion Points
- Stress testing evaluates exposures, potential future exposures (PFE), and credit valuation adjustments (CVA) under stressed conditions.
- Metrics help quantify tail risk not captured by traditional mark-to-market views.
- Assumptions often include correlations, default probabilities, and loss given default estimates; results are scenario and model dependent.
- Limitations include model risk and challenges incorporating wrong-way risk fully.
Illustrative Stress Testing Metrics Table
Sample data reflecting counterparty exposure metrics under baseline and stress scenarios.
| Counterparty | Baseline Exposure (M) | Stressed Exposure (M) | Stressed CVA (M) |
|---|---|---|---|
| Counterparty A | 50 | 75 | 3.5 |
| Counterparty B | 30 | 45 | 2.1 |
| Counterparty C | 40 | 60 | 2.9 |
| Counterparty D | 20 | 35 | 1.7 |
Graphical Analysis – Stress Testing CCR: Exposure Distribution by Counterparty Type
Context and Interpretation
- This bar chart depicts stressed exposures segmented by counterparty categories such as financial institutions, corporates, sovereigns, and others.
- Highlights concentration risks within financial institutions, a critical factor for systemic risk assessments.
- Enables risk managers to prioritize monitoring and capital allocation strategies.
- Supports regulatory and internal risk management perspectives on exposure diversification.
Figure: Stressed Exposure Distribution Across Counterparty Types
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Code Example: Python for CCR Stress Testing Calculation
Code Description
This Python script calculates the Credit Valuation Adjustment (CVA) under a stress scenario incorporating expected exposure and default probabilities.
import numpy as np
def calculate_cva(lgd, expected_exposures, default_probs):
"""Calculate CVA using expected exposures and default probabilities."""
cva = np.sum(lgd * expected_exposures * default_probs)
return cva
# Inputs
lgd = 0.6 # Loss Given Default
expected_exposures = np.array([50, 45, 40, 35]) # Expected exposures over time
# Default probabilities reflecting stressed conditions
default_probs = np.array([0.01, 0.02, 0.015, 0.025])
cva_value = calculate_cva(lgd, expected_exposures, default_probs)
print(f"Calculated CVA under stress: {cva_value:.2f} million")Conclusion and Next Steps in CCR Stress Testing
Synthesis and Path Forward for Effective CCR Stress Testing
- Stress testing CCR is essential to detect vulnerabilities in counterparty portfolios under extreme but plausible scenarios.
- Multifactor and wrong-way risk considerations enhance the relevance of stress tests.
- Next steps include integrating results into risk appetite frameworks and capital planning.
- Continued model validation and scenario updating are crucial for maintaining effectiveness.
