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.

Keywords:

• ARMA models
• forecasting accuracy
• Kalman filter
• logistic regression
• MACD technical models
• neural network regression
• technical trading models
• trading efficiency

Acknowledgements

The authors wish to thank the referees and the Editor of the European Journal of Finance for very helpful comments on a first version of this paper. The usual disclaimer applies.

Notes

¹ On the use of the yield curve as a predictor of future output growth and inflation, see, among others, Fama (1990) and Ivanova et al. (2000).

² Period of UK Gilt observations: 11 April 2001 to 4 June 2003 (out-of-sample period starts 24 January 2003);

Period of US T-Bond observations: 6 April 2001 to 4 June 2003 (out-of-sample period starts 27 January 2003);

Period of German Bund observations: 17 April 2001 to 4 June 2003 (out-of-sample period starts 27 January 2003).

³ Yield curves derived by subtracting the 3 month middle rate from the 10-year benchmark redemption yield, e.g. JPBRYLDYC = JPBRYLD—ECJAP3M.

⁴ Detailed results are not reported here in order to conserve space. They are available from the authors upon request.

⁵ The detailed model specifications and in-sample results are not reported here in order to conserve space. They are available from the authors upon request.

⁶ The general class of ARMA models is for stationary time series. If the series is not stationary, an appropriate transformation is necessary.

⁷ Again, in-sample results are not reported here in order to conserve space. They are available from the authors upon request.