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Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing

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  • Colin Turfus

    (Deutsche Bank, EC2N 2DB London, UK)

Abstract

We 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.

Suggested Citation

  • Colin Turfus, 2018. "Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing," IJFS, MDPI, vol. 6(2), pages 1-20, April.
  • Handle: RePEc:gam:jijfss:v:6:y:2018:i:2:p:39-:d:139355
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    References listed on IDEAS

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    1. Stefano Pagliarani & Andrea Pascucci, 2013. "Local Stochastic Volatility With Jumps: Analytical Approximations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 16(08), pages 1-35.
    2. Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
    3. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    4. Colin Turfus & Alexander Shubert, 2017. "ANALYTIC PRICING OF CoCo BONDS," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(05), pages 1-26, August.
    5. Pagliarani, Stefano & Pascucci, Andrea, 2011. "Analytical approximation of the transition density in a local volatility model," MPRA Paper 31107, University Library of Munich, Germany.
    6. Hideharu Funahashi, 2015. "An Analytical Approximation For European Option Prices Under Stochastic Interest Rates," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1-43.
    7. Norbert Jobst & Stavros A. Zenios, 2001. "Extending Credit Risk (Pricing) Models for the Simulation of Portfolios of Interest Rate and Credit Risk Sensitive Securities," Center for Financial Institutions Working Papers 01-25, Wharton School Center for Financial Institutions, University of Pennsylvania.
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    Cited by:

    1. Leunglung Chan, 2018. "Editorial for Special Issue “Finance, Financial Risk Management and their Applications”," IJFS, MDPI, vol. 6(4), pages 1-3, October.

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