Fixed Income Project (2024/25)

This project was developed as part of the MSc Mathematical Trading and Finance programme at Bayes Business School (formerly Cass). The work values a 5-year capped/floored Floating Rate Note (FRN) issued by BNP Paribas using market data from 2024. The analysis applies curve-building, derivative pricing, and risk management techniques in Python using QuantLib.

Overview

The bond is decomposed into two parts:

• A Floating Rate Note (FRN) valued using projected 3M Euribor rates
• A Cap/Floor Structure valued as a portfolio of options under the Black model

The fair value is then adjusted for credit risk using a simplified Credit Valuation Adjustment (CVA) based on CDS spreads and bootstrapped survival probabilities. Finally, the project assesses hedging strategies (via swaps and CDS) and quantifies risk exposures using a Monte Carlo framework.

Methodology Summary

• Curve Building: Construct the historical coupon schedule and interpolate a log-cubic discount curve from market data.
• Price bond: Forecast forward rates and price the bond as the present value of floating coupons.
• Price replicating portfolio:Price the cap/floor option strip using the Black model and implied volatility surface.
• Adjust for default possibility: Apply CVA to adjust for credit risk using a simplified hazard rate model.
• Adjust for interest rate risk: Hedge the bond’s interest rate and credit exposure using swaps and CDS contracts.
• Stress-test performance: Simulate bond value under market factor shocks (rate shifts, volatility shifts, CDS spread movements) to compute VaR and Expected Shortfall.

See code/ for full implementation, organized by question (Q1 to Q13).

Figures

Key figures from the analysis are embedded below. All output charts are located in the /figures directory.

Coupon Payoff Structure

Interpolated Log-Cubic Curve

Implied Volatility Surface

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Project Structure

Fixed-Income-Project/
├── code/                        # Full implementation for Q1–Q13
├── figures/                     # Output charts and plots
├── datasets/                    # Market data: interest rates, volatilities, CDS
├── Report.pdf                   # Final client-facing report
├── Task.pdf                     # Coursework brief
├── requirements.txt             # Required Python packages
└── README.md

Requirements

Before running, install required Python libraries:

pip install -r requirements.txt

Note: This project requires the QuantLib Python package. Refer to the QuantLib installation guide if needed.

Ensure that all market data files (MarketData.xlsx, shifted_black_vols.csv, etc.) are placed in the datasets/ folder.

How to Run

Open the relevant script in the code/ folder (e.g., Q1.py) and run via your preferred Python IDE.

Authors

• Shaan Ali Remani
• Basil Ibrahim
• José Santos
• Wincy So

Fixed Income Coursework