Quantitative Risk Assessment in Financial Derivatives: A Stochastic Volatility Framework for Credit Default Swaps
Keywords:
credit default swaps, stochastic volatility, risk management, }nancial derivatives, quantitative }nance, Heston model, jump dizusion, Value at RiskAbstract
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
dizusion 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 signi}cantly 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 }nancial 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.
