The projects performed Standardised Approach for Counterparty Credit Risk (SA-CCR) exposure & Exposure At Default (EAD) analytics with C++ and Python, using supervisory add-on logic. This counterparty credit risk analytics prototype simulates future exposure profiles for interest rate, foreign exchange (FX), and equity derivatives using Monte Carlo methods. It demonstrate the end-to-end workflow used in front-office and XVA teams, as market simulation → trade revaluation → exposure aggregation (EPE/PFE) → regulatory-style EAD computation.
Exposure Simulation
- Monte Carlo simulation of future market states.
- Pathwise revaluation of derivative contracts.
- Exposure calculation under positive exposure conventions.
Supported Instruments
- Interest Rate Swap
- Linearized PV via DV01 under flat-rate assumptions.
- FX Forward
- Discounted forward valuation with domestic/foreign rates.
- European Option
- Black–Scholes valuation for equity or FX underlyings.
Exposure Metrics
- Expected Positive Exposure (EPE).
- Potential Future Exposure (PFE) at configurable confidence levels.
SA-CCR Analytics
- Replacement Cost:
RC = max(MtM − collateral, 0) - Supervisory add-on aggregation across asset classes
EAD = α × (RC + PFE_addon)with configurable supervisory parameters
C++17
- Performance-oriented Monte Carlo engine
Python
- Unit tests validating simulation and exposure mechanics