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Quantifying Model Risk in Credit Derivatives Pricing
Version 1
: Received: 3 January 2018 / Approved: 4 January 2018 / Online: 4 January 2018 (03:24:24 CET)
A peer-reviewed article of this Preprint also exists.
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 †. Int. J. Financial Stud. 2018, 6, 39.
Abstract
We propose a methodology for the quantification of model risk in the context of credit derivatives pricing and CVA, where the uncertain or unmodelled parameter is often the correlation between rates and credit. We take the rates model to be Hull-White (normal) and the credit model to be Black-Karasinski (lognormal). We show how highly accurate analytic pricing formulae, hitherto unpublished, can be derived for CDS and extended to address instruments with defaultable Libor flows which may in addition be capped and/or floored. We also consider the pricing of a contingent CDS with an interest rate swap underlying. We derive explicit expressions showing how to good accuracy the dependence of model prices on the uncertain parameter(s) can be captured in analytic formulae which are readily amenable to computation without recourse to Monte Carlo or lattice-based computation. In so doing, we take into account the impact on model calibration of the uncertain (or unmodelled) parameter.
Keywords
perturbation expansion; Green’s function; model risk; model uncertainty; credit derivatives; CVA; correlation risk
Subject
Business, Economics and Management, Finance
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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