Quantitative Risk Assessment in Financial Derivatives: A Stochastic Volatility Framework for Credit Default Swaps

Posted: Oct 28, 2022

• Chen Wei
• Maria Rodriguez
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research develops a comprehensive quantitative framework for assessing risk in credit default swaps (CDS) using stochastic volatility models. We propose an enhanced Heston model that incorporates jump diffusion and correlation dynamics between underlying asset returns and volatility processes. Our methodology employs maximum likelihood estimation and Monte Carlo simulation to capture the complex behavior of CDS spreads during periods of market stress. The study analyzes 2,500 CDS contracts across multiple sectors from 2000-2003, demonstrating that traditional constant volatility models significantly underestimate tail risk. Our results show that the proposed stochastic volatility framework improves Value at Risk (VaR) estimates by 23.7% compared to standard approaches, providing financial institutions with more accurate risk measurement tools for derivative portfolios. The model's predictive capability is validated through backtesting against actual default events during the study period.