Next Article in Journal
The Impact of Revenue Diversification on Bank Profitability and Stability: Empirical Evidence from South Asian Countries
Next Article in Special Issue
Cross Hedging Stock Sector Risk with Index Futures by Considering the Global Equity Systematic Risk
Previous Article in Journal
How Macro Transactions Describe the Evolution and Fluctuation of Financial Variables
Previous Article in Special Issue
An Empirical Investigation of Risk-Return Relations in Chinese Equity Markets: Evidence from Aggregate and Sectoral Data
Article

Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing

Deutsche Bank, EC2N 2DB London, UK
The views expressed herein should not be considered as investment advice or promotion. They represent personal research of the author and do not purport to reflect the views of his employers (current or past), or the associates or affiliates thereof.
Int. J. Financial Stud. 2018, 6(2), 39; https://doi.org/10.3390/ijfs6020039
Received: 3 January 2018 / Revised: 24 March 2018 / Accepted: 27 March 2018 / Published: 3 April 2018
(This article belongs to the Special Issue Finance, Financial Risk Management and their Applications)
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. View Full-Text
Keywords: perturbation expansion; Green’s function; model risk; model uncertainty; credit derivatives; CVA; correlation risk perturbation expansion; Green’s function; model risk; model uncertainty; credit derivatives; CVA; correlation risk
Show Figures

Figure 1

MDPI and ACS Style

Turfus, C. Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing. Int. J. Financial Stud. 2018, 6, 39. https://doi.org/10.3390/ijfs6020039

AMA Style

Turfus C. Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing. International Journal of Financial Studies. 2018; 6(2):39. https://doi.org/10.3390/ijfs6020039

Chicago/Turabian Style

Turfus, Colin. 2018. "Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing" Int. J. Financial Stud. 6, no. 2: 39. https://doi.org/10.3390/ijfs6020039

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop