Modelling Counterparty Credit Risk in Czech Interest Rate Swaps

ZLATOŠOVÁ, Silvie and Lenka KŘIVÁNKOVÁ. Modelling Counterparty Credit Risk in Czech Interest Rate Swaps. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. Brno: Mendelova univerzita v Brně, 2017, vol. 65, No 3, p. 1015-1022. ISSN 1211-8516. Available from: https://dx.doi.org/10.11118/actaun201765031015.

Basic information

• Original name: Modelling Counterparty Credit Risk in Czech Interest Rate Swaps
• Authors: ZLATOŠOVÁ, Silvie (203 Czech Republic, guarantor, belonging to the institution) and Lenka KŘIVÁNKOVÁ (203 Czech Republic, belonging to the institution).
• Edition: Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Brno, Mendelova univerzita v Brně, 2017, 1211-8516.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 50206 Finance
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• RIV identification code: RIV/00216224:14560/17:00097240
• Organization unit: Faculty of Economics and Administration
• Doi: http://dx.doi.org/10.11118/actaun201765031015
• Keywords in English: counterparty credit risk; credit valuation adjustment; probability of default; interest rate swaps; yield curve; Hull-White model; Monte Carlo simulations; credit exposure
• Tags: International impact, Reviewed

Abstract

According to the Basel Committee’s estimate, three quarters of counterparty credit risk losses during the financial crisis in 2008 originate from credit valuation adjustment’s losses and not from actual defaults. Therefore, from 2015, the Third Basel Accord (EU, 2013a) and (EU, 2013b) instructed banks to calculate the capital requirement for the risk of credit valuation adjustment (CVA). Banks are trying to model CVA to hold the prescribed standards and also reach the lowest possible impact on their profit. In this paper, we try to model CVA using methods that are in compliance with the prescribed standards and also achieve the smallest possible impact on the bank’s earnings. To do so, a data set of interest rate swaps from 2015 is used. The interest rate term structure is simulated using the Hull-White one-factor model and Monte Carlo methods. Then, the probability of default for each counterparty is constructed. A safe level of CVA is reached in spite of the calculated the CVA achieving a lower level than CVA previously used by the bank. This allows a reduction of capital requirements for banks.

Links

• MUNI/A/1039/2016, interní kód MU: Name: Modelování volatility na finančních trzích a její aplikace v oblasti řízení rizik a oceňování aktiv (Acronym: VOLATILITA)

Investor: Masaryk University, Category A