<p>This work will study different methods to estimate counterparty credit risk, where the methods represent both analytical approximation and simulation based method. The somewhat more analytical approximation that will be used is the current exposure method from the Bank for International Settlements and is based on simple add-on factor to the current market value. In the simulation part, Monte Carlo methods will be used. The paper will show that Monte Carlo methods enable estimation of the full exposure distribution as a function of time. From that distribution two measures of exposure will be used. The first use the peak at the 95% percentile and the second uses the concept of effective expected exposure. Those three alternative measures will be tested on six different portfolios. The portfolios are based on real data and represent both private persons, small companies, life insurance, investment bank and some of more academic interest. The estimate of exposure in those portfolios will be estimated with and without the establishment of netting agreements in order to see how that affects the exposure. The numerical results indicate that netting results in reduced exposure. In the comparisons between the different exposure measures the results show that the simulation based method in general estimates a lower exposure, but it depends intently on the construction of the portfolio. Based on those observations the main conclusion is that a simulation based approach is preferable since it enables better risk control within the firm as a consequence of enabling anatomizes of the evolution of exposure through time. Keywords: Counterparty Credit Risk, Libor Market Model and Monte Carlo simulation</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:ntnu-9449 |
| Date | January 2006 |
| Creators | Øvergaard, Hans Michael |
| Publisher | Norwegian University of Science and Technology, Department of Mathematical Sciences, Institutt for matematiske fag |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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