CVA for IR-Swaps under Wrong Way Risk. A numerical evaluation using a semi-analytical model
Abstract
This thesis examines the background and nature of credit value adjustment (CVA), a concept that has heightened in its importance in the financial market after the 2008 financial crisis. Credit value adjustment is defined as a price deducted from the risk-free value of a bilateral derivative to adjust for the counterparty credit risk (CCR). The focus of this thesis is to quantify CVA of an interest rate swap (IRS) under wrong way risk (WWR). Interest rate swap is an agreement between counterparties to exchange future interest rate payments, and WWR is the risk of negative correlation between the credit exposure and the counterparty’s credit quality. The numerical studies in this thesis are conducted using the semi-analytical formula derived by Cerny and Witzany (2015). We investigate the behavior of CVA with respect to two factors, the default intensity and the WWR, where the results show that CVA increases with both factors, as has been proven in earlier studies such as Cerny and Witzany (2015) and Brigo & Pallavicini (2007). Additionally, we look at the evolvement of CVA before, during and after the 2008 financial crisis where we see that CVA was negligible before June 2007, but then it surged rapidly, which resulted in substantial financial losses for many institutions. Furthermore, we examine the possibility to compute CVA for a heterogeneous portfolio by regarding it as a homogeneous one, in which all obligors in the portfolio are considered to have identical parameters. The results show that the homogenous method works relatively well for portfolios that consist of similar obligors.
Degree
Master 2-years
Other description
MSc in Finance
Collections
View/ Open
Date
2016-09-21Author
Halldórsdóttir, Berglind
Zhang, Weili
Keywords
Credit Value Adjustment
Wrong Way Risk
Interest Rate Swap
Credit Default Swap
Homogeneous CVA Portfolio
Heterogeneous CVA Portfolio
Semi-Analytical Model
Series/Report no.
Master Degree Project
2016:124
Language
eng