--- name: modeling-counterparty-credit-exposure-7a8d3d3090f2 language: en description: Calculates potential future exposure and CVA with simulation-based approaches and netting agreement analysis. Use when modeling counterparty exposure, calculating CVA/DVA, or assessing counterparty risk. tags: - modeling - derivatives-and-structured-products - risk metadata: author: casemark practice_areas: - Derivatives - Structured Products - Hedging document_types: - Financial Model skill_modes: - Modeling - Forecasting --- # Modeling Counterparty Credit Exposure ## When To Use - Calculating potential future exposure (PFE) profiles for OTC derivative portfolios - Computing credit valuation adjustment (CVA) and debit valuation adjustment (DVA) for pricing or accounting (ASC 820 / IFRS 13) - Assessing counterparty credit limits and concentration risk across netting sets - Evaluating the impact of collateral agreements (CSAs) on exposure profiles - Stress-testing counterparty exposure under adverse market scenarios - Supporting regulatory capital calculations under SA-CCR or IMM [VERIFY: applicable regulatory framework] ## Inputs To Gather - **Trade population**: Full list of trades per counterparty, including notional, trade date, maturity, currency, and product type (IRS, CDS, FX forwards, equity options, etc.) - **Netting agreements**: ISDA Master Agreement status, netting set definitions, and any close-out netting enforceability opinions [VERIFY: jurisdiction-specific enforceability] - **Collateral terms**: CSA parameters — threshold, minimum transfer amount (MTA), independent amount, eligible collateral types, rehypothecation rights, margin period of risk (MPR) - **Market data**: Yield curves, volatility surfaces, FX spot rates, credit spreads (CDS or bond-implied), correlation assumptions - **Counterparty credit data**: Credit ratings, CDS spreads (or proxy spreads), probability of default (PD) term structures, loss given default (LGD) assumptions - **Own credit data**: Entity CDS spreads or proxy for DVA calculation - **Simulation parameters**: Number of Monte Carlo paths, time grid granularity, confidence level for PFE (typically 95th or 97.5th percentile), simulation horizon ## Workflow 1. **Map netting sets and collateral** - Group trades by legal netting agreement; confirm enforceability per jurisdiction - Parse CSA terms: thresholds, MTAs, MPR, eligible collateral haircuts - Identify wrong-way risk exposures (counterparty credit quality correlated with exposure) 2. **Calibrate market models** - Select diffusion processes per risk factor (e.g., Hull-White for rates, GBM or local vol for equities/FX, reduced-form for credit) - Calibrate to current market data; document calibration targets and fit quality - Set correlation matrix across risk factors — use historical estimation with appropriate lookback window 3. **Run Monte Carlo simulation** - Simulate joint evolution of all risk factors across the time grid - Reprice each trade at each time step on each path (use analytic approximations or regression-based methods like Longstaff-Schwartz for early-exercise products) - Aggregate to netting-set level, apply close-out netting: net exposure = max(0, sum of MTMs within netting set) 4. **Apply collateral dynamics** - Model collateral calls with MPR lag (typically 10–20 business days for bilateral, 5 for cleared) - Apply thresholds, MTAs, and independent amounts per CSA - Compute collateralized exposure at each time step: E(t) = max(0, net MTM − collateral held) 5. **Calculate exposure metrics** - **Expected Exposure (EE)**: Mean of positive exposures across paths at each time step - **Potential Future Exposure (PFE)**: Quantile of exposure distribution (95th/97.5th percentile) at each time step - **Expected Positive Exposure (EPE)**: Time-weighted average of EE over the profile - **Peak PFE**: Maximum PFE across the time grid - **Effective EPE**: Non-decreasing transformation of EPE for regulatory purposes 6. **Compute CVA and DVA** - CVA = integral of discounted EE × counterparty default probability × LGD over the exposure horizon - DVA = integral of discounted ENE (expected negative exposure) × own default probability × LGD - Use counterparty CDS spreads for market-implied PD; use rating-based PD for accounting if required [VERIFY: CVA methodology — market-implied vs. historical PD per firm policy] - Consider bilateral CVA (BCVA = CVA − DVA) and FVA if required 7. **Stress test and validate** - Run exposure profiles under stressed market scenarios (rate shocks, spread widening, FX moves) - Test sensitivity to correlation assumptions, number of simulation paths, and time-grid density - Compare model PFE against add-on based methods (SA-CCR) as a reasonableness check - Back-test exposure predictions against realized MTMs where historical data is available ## Output - **Exposure profile report**: Time-series charts of EE, PFE (with confidence level), and peak PFE per netting set and counterparty - **CVA/DVA summary**: Unilateral CVA, DVA, and bilateral CVA per counterparty with breakdown by netting set - **Collateral impact analysis**: Comparison of uncollateralized vs. collateralized exposure profiles showing CSA benefit - **Sensitivity tables**: CVA sensitivity to credit spread moves (CS01), interest rate shifts, and correlation changes - **Wrong-way risk flags**: Identification of netting sets where exposure and counterparty default are positively correlated - **Methodology documentation**: Model choices, calibration details, simulation parameters, and all assumptions ## Quality Checks - Verify that netting-set groupings match legal documentation — incorrect netting inflates or deflates exposure - Confirm collateral parameters (thresholds, MTA, MPR) are sourced from actual CSA terms, not defaults - Check that the number of Monte Carlo paths produces stable results (re-run with doubled paths; PFE should not shift materially) - Validate that EE converges to zero as trades approach maturity - Ensure PFE at the 95th percentile exceeds EE at every time step — if not, check for simulation errors - Cross-check CVA magnitude against market benchmarks: CVA as basis points of notional should be plausible given counterparty spread level - Confirm discount curves and credit curves are as-of the same valuation date - Flag any trades with missing market data or valuation failures — do not silently drop them from the simulation - Mark all jurisdiction-dependent assumptions (netting enforceability, MPR regulatory minimums, capital framework) with [VERIFY] --- Source attribution: CaseMark Original project: https://github.com/CaseMark/skills/blob/449269ec49b6a8f1b4614cea52aa44b2a76e4e94/skills/capital/modeling-counterparty-credit-exposure/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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