The Economic Value of Advanced Time Series Methods for Modelling and Trading 10-year Government Bonds

Abstract

The motivation for this paper is to determine the potential economic value of advanced modelling methods for devising trading decision tools for 10-year Government bonds. Two advanced methods are used: time-varying parameter models with the implementation of state space modelling using a Kalman filter and nonparametric nonlinear models with Neural Network Regression (NNR). These are benchmarked against more traditional forecasting techniques to ascertain their potential as a forecasting tool and their economic value as a base for a trading decision tool. The models were developed using data from the UK Gilt market, US T-Bond market and German Bund market. Using in-sample data from April 2001to January 2003to develop the models, their results were assessed using the out-of-sample period of January 2003 to June 2003. Performance evaluation was based upon forecasting accuracy measures and financial criteria using a simulated trading strategy incorporating realistic trading costs. It is concluded that for the time series studied and for the period under investigation, the performance of the advanced models is mixed. While the NNR models have the ability to forecast the 10-year Government bond yield and add economic value as a trading decision tool, the Kalman filter models' performance is not as conclusive. The Kalman filter models outperformed the traditional techniques using forecasting accuracy measures, however they did not perform as well in the simulated trading strategy.