Advanced Counterparty Risk and CVA via Stochastic Volatility
Forthcoming publication in the Journal of Financial Transformation
Exposure models in the context of counterparty risk have become central to financial institutions. They are a main driver of CVA pricing, capital calculation and risk management. It is general practice in the industry to use constant-volatility normal or log-normal models for it. Ignacio Ruiz and Ricardo Pachón explain some of the strong limitations of those models and show how stochastic volatility can improve the situation substantially. This is shown with illustrative examples that tackle day-to-day problems that practitioners face. Using a coupled Black-Karasinski model for the volatilty and a GBM model for the spot as an example, it is shown how stochastic volatility models can provide tangible benefits by improving netting effects, CVA pricing accuracy, regulatory capital calculation, initial margin calculations and quality of exposure management.