Analysis of Interest Rate Risk Estimation Methods in Developing Bond Markets

This paper reviews a variety of approaches to measure risk in fixed income portfolios in terms of value-at-risk (VaR). More specifically, we use constant yield model, linear yield model, Vasicek model, CIR model, Nelson-Siegel model, G-Curve and Hull-White model for predicting bond prices and further computation of the parametric VaR of a portfolio composed of fixed income securities using Monte Carlo simulations. We also consider different dynamic models based on Brownian motion, Vasicek and CIR stochastic differential equation and autoregressive model for forecasting tomorrow parameters of bond price models using maximum likelihood estimation (MLE). In this paper we test the hypothesis that the increase in number of random factors in dynamic models does not improve the risk measure. An empirical application involving a data set of 18 Russian government bonds with different maturities demonstrates the performance of multifactor dynamic models with calibration and backtesting based on statistical tests and loss functions. There was found out that multifactor models better assesses risk measures in terms of firm losses.