A New Approach of Kernel Bandwidth Applications for Time-Series Using the Example of the Prediction of the Euro's Exchange Rate

18 Pages Posted: 14 Feb 2004

See all articles by Tim Brailsford

Tim Brailsford

Bond University

Jack H.W. Penm

Australian National University - School of Finance and Applied Statistics, Faculty of Economics and Commerce

R. Deane Terrell

Australian National University (ANU) - National Graduate School of Management

Abstract

In recent years the application of kernel smoothing methods in nonparametric regression framework to financial time-series analysis has become widespread. Kernel smoothing methods have not been applied, however, to a wide range of problems arising in time-series simulations and forecasting. Forgetting factors can be both fixed and variable. Gijbels et al (1999) propose an understanding of fixed forgetting factors via kernel smoothing. However the variable forgetting approach is not mentioned. This paper describes the variable forgetting factor and the fixed forgetting factor, and establishes the linkage for the first time between the variable forgetting factor approach and kernel smoothing. The forgetting factor method uses a sample of data and estimates the value of the forgetting factor from the sample. This method fits better than a parametric approach, which uses some assumed parameters. Since the forgetting factor method is equivalent to a kernel estimation - which is a non-parametric method - it is likely to give more accurate estimates and better forecasting performance in financial time-series than a parametric one. The major area of interest in this application is whether kernel estimation, using Cho's approach [see Cho et al (1991) and Brailsford et al (2002)] for kernel bandwidth selection, can improve the Euro's forecasting performance within the framework of subset AR modelling. The forecasting performance is compared with the performance of AR modelling without the use of the forgetting factor. If improved forecasting performance is achieved, this can increase the potential use of kernel smoothing methods in time-series forecasting. The findings show that the kernel bandwidth so determined can improve the forecasting performance.

Keywords: Kernel bandwidth application; variable forgetting factor; subset autoregressive model

JEL Classification: C50, F30, G10

Suggested Citation

Brailsford, Timothy John and Penm, Jack and Terrell, R. Deane, A New Approach of Kernel Bandwidth Applications for Time-Series Using the Example of the Prediction of the Euro's Exchange Rate. Available at SSRN: https://ssrn.com/abstract=500502 or http://dx.doi.org/10.2139/ssrn.500502

Timothy John Brailsford

Bond University ( email )

Gold Coast, QLD 4229
Australia

HOME PAGE: http://www.bond.edu.au

Jack Penm (Contact Author)

Australian National University - School of Finance and Applied Statistics, Faculty of Economics and Commerce ( email )

Canberra, Australian Capital Territory 0200
Australia
+61 (02) 61250535 (Phone)
+61 (02) 61250087 (Fax)

R. Deane Terrell

Australian National University (ANU) - National Graduate School of Management ( email )

Sir Roland Wilson Building (120)
Canberra, Australian Capital Territory 0200
Australia

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