The pricing of credit valuation adjustment for over-the-counter traded derivatives

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School of Science | Master's thesis
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TU-91

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en

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81 + [11]

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Abstract

The purpose of this thesis is to contemplate the theory and practical aspects of credit valuation adjustment pricing for over-the-counter traded derivatives. Furthermore, an internal pricing framework based on Monte Carlo simulations has be introduced and compared with the standard models proposed by the Basel III regulation. Counterparty credit risk is one of the most urgent challenges facing the modern financial system and it is now considered by many market participants to be the single most important financial risk to manage. New regulatory changes have made counterparty credit risk and credit value adjustment one of the most debated topics in the field of quantitative finance. The Basel regulatory framework has introduced the concept of credit valuation adjustment in order to mitigate counterparty risk for over-the-counter traded derivatives. Furthermore, new accounting rules require a mark-to-market of derivative positions that includes the possibility of counterparty default. Credit valuation adjustment is the market value of the counterparty credit risk. I.e. it is the difference between the risk-free value of a portfolio and the portfolio value that takes into account the possibility of counterparty default. The credit valuation adjustment component depends on default probability, expected exposure, the recovery rate and on the correlation between them. It is also highly model-dependent and an alarming result is that the standardized models presented by the Basel regulatory framework seem to be underestimating the counterparty risk. At the moment, there exists no industry standard as how counterparty credit risk should be priced. Due to the complexity of credit valuation adjustment, simple add-on methods are inevitably inadequate to measure risk. Consequently, I have used a pricing framework relying on Monte Carlo simulations, this is also the approach adopted by most investment banks.

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Wallenius, Hannele

Thesis advisor

Kaila, Ruth

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