- Review interest rate modeling and derivatives pricing theory
- Practical examples of derivative pricing using QuantLib.
- Practical examples of Monte Carlo simulations with risk metrics computation such as Expected Exposure (EE), Value at Risk (VaR), Potential Future Exposure (PFE), or Credit Valuation Adjustment (CVA)
- Term structure models
- Analytic pricing formulas of Term rates (SOFR, SONIA, ESTR, ...)
- Closed-form formulas for Risk-Free-Rate (RFR) based instruments:
- Linear products (FRAs, IRD)
- Caps/Floors
- Swaptions
- Options on RFR Futures
- .md files for theory refresher
- .py files for QuantLib implementation
- .ipynb files for mixed theory + QuantLib implementation + MC Pricing
The project is structured into three main components: theory summaries, modular Python source code, and interactive Jupyter Notebooks.
theory/: Mathematical explanations and formulations for term structures, RFR pricing, options, and risk metrics.src/: Python codebase containing clean QuantLib wrappers for curves, pricing engines, Hull-White simulations, and metric calculations.notebooks/: Step-by-step demonstrations and visualizations of bootstrapping, instrument pricing, and Monte Carlo risk simulations.tests/: Unit tests to verify the correctness of the curve construction, analytical pricing, and simulator models.
- Phase 1: Curves & RFR Construction
- Bootstrapping curves from SOFR/SONIA overnight index swaps (OIS).
- Establishing multi-curve framework (forecasting vs. discounting).
- Phase 2: Product Pricing (Linear & Options)
- Pricing FRAs and Interest Rate Swaps (IRS).
- Valuation of Caps/Floors, Swaptions, and Futures Options (analytical models and QuantLib pricing engines).
- Phase 3: Monte Carlo Simulator & Risk Metrics
- Simulating short-rate paths using the Hull-White 1-Factor model.
- Valuing interest rate derivatives along simulated paths.
- Calculating Expected Exposure (EE), Peak Forward Exposure (PFE), Value at Risk (VaR), and Credit Valuation Adjustment (CVA).