Variance reduction technique for calculating value at risk in fixed income portfolios
Financial institutions and regulators increasingly use Value at Risk (VaR) as a standard measure for market risk. Thus, a growing amount of innovative VaR methodologies is being developed by researchers in order to improve the performance of traditional techniques. A variance-covariance approach for...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/11222 |
| Acceso en línea: | https://hdl.handle.net/2099/11222 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical statistics Value at Risk Market risk Fixed Income Portfolios Nelson and Siegel model Estadística matemàtica -- Aplicacions Classificació AMS::62 Statistics::62P Applications Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | Financial institutions and regulators increasingly use Value at Risk (VaR) as a standard measure for market risk. Thus, a growing amount of innovative VaR methodologies is being developed by researchers in order to improve the performance of traditional techniques. A variance-covariance approach for fixed income portfolios requires an estimate of the variance-covariance matrix of the interest rates that determine its value. We propose an innovative methodology to simplify the calculation of this matrix. Specifically, we assume the underlying interest rates parameterization found in the model proposed by Nelson and Siegel (1987) to estimate the yield curve. As this paper shows, our VaR calculating methodology provides a more accurate measure of risk compared to other parametric methods |
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