Variance reduction technique for calculating value at risk in fixed income portfolios

Abstract

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