Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing†
AbstractWe propose a simple but practical methodology for the quantification of correlation risk in the context of credit derivatives pricing and credit valuation adjustment (CVA), where the correlation between rates and credit is often uncertain or unmodelled. We take the rates model to be Hull–White (normal) and the credit model to be Black–Karasinski (lognormal). We summarise recent work furnishing highly accurate analytic pricing formulae for credit default swaps (CDS) including with defaultable Libor flows, extending this to the situation where they are capped and/or floored. We also consider the pricing of contingent CDS with an interest rate swap underlying. We derive therefrom explicit expressions showing how the dependence of model prices on the uncertain parameter(s) can be captured in analytic formulae that are readily amenable to computation without recourse to Monte Carlo or lattice-based computation. In so doing, we crucially take into account the impact on model calibration of the uncertain (or unmodelled) parameters. View Full-Text
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Turfus, C. Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing. Int. J. Financial Stud. 2018, 6, 39.
Turfus C. Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing. International Journal of Financial Studies. 2018; 6(2):39.Chicago/Turabian Style
Turfus, Colin. 2018. "Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing." Int. J. Financial Stud. 6, no. 2: 39.
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