Volatility Transmission acros the Term Structure of Swap Markets: International Evidence

We characterize the behavior of volatility across the term structure of interest rate swaps in three currencies (Deutsche mark, Japanese yen and US Dollar)Interest rate swaps, Term structure of interest rates, Autoregressive conditional heteroscedstic models, Volatility spillovers.

The Forecasting Ability of Factor Models of the Term Structure of IRS Markets

Using estimated principal components as factors, three-factors models are shown to produce forecasts comparable to those of autoregressive models for 2 to 10 year zaero coupon interest rates IRS markets both, for short- and medium- term forecasting horizons. Evidence is provided for the Deutsche mark, Spanish peseta, Japanese yen and US Dollar. Forecast from factor models are also shown to preserve the correlation matrix of interest rates across a given term structure, an important proprerty regarding risk management. The result is quite striking, because factor models are purely static, and forecasts for the factors must be obtained in advance of interest rate forecast.factor modelsFactor models, Term structure of interest rates, Principal components, Swap markets, IRS

Using The Nelson and Siegel Model of The term Structure in Value at Risk Estimation

Over the past decade, no other tool in financial risk management has been used as much as Value at Risk (VaR). VaR is an estimate to determine how much a specific portfolio can lose within a given time period at a given confidence level. Nowadays, in order to improve the performance of VaR methodologies, researchers have suggested numerous modifications of traditional techniques. Following this tendency, this paper explores the use of the model proposed by Nelson and Siegel (with the aim to estimate the term structure of interest rate, TSIR) to implement a simulation to calculate the VaR of a fixed income portfolio. In this approach the dimension of the problem is reduced as the price of the portfolio depends on a vector of four parameters. Subsequently, we can use Monte Carlo simulation techniques to generate future scenarios in these parameters and use them to reevaluate the portfolio. The resulting changes in portfolio value are arranged and the appropriate percentile is determined to provide the VaR estimate. Despite the fact that this approach theoretically facilitates the calculation of VaR on fixed income portfolios, we show that the PROBLEM in practise ignores price sensitivities. So this method cannot therefore be used to calculate VaR on fixed income portfolios.Value at Risk, Financial risk.