Testing for nonlinearity of exchange rates: an information‐theoretic approach

Abstract

Purpose

This study seeks to explore the nature of a data‐generating process for four dollar exchange rates.

Design/methodology/approach

Using a discrete parametric modeling approach, an efficient test statistic was computed for nonlinearity in terms of variance of the residuals of the linear and nonlinear autoregressive models by Akaike Information Criterion, and a surrogate data analysis was conducted.

Findings

It shows that a nonlinear autoregressive model outperforms a linear stochastic model in certain subsamples of baht, pound, ringgit, and yen dollar exchange rates. However, when the test statistics using different model orders and the data for the entire samples are estimated, it appears that the nonlinear model has a better performance than the linear model in fitting Thai and Malaysian currencies. The nonlinear model performs better than the linear model in the case of the UK pound in two thirds of the models, but the linear models completely outperform the nonlinear models for the yen data.

Research limitations/implications

More financial and economic time series will be explored to employ the methodology used in the study, and tests for possible presence of nonlinear deterministic dynamics (chaos) in the exchange rates series will be conducted based on the present findings in further study.

Practical implications

These findings suggest that the assumption of linear stochastic process as the underlying dynamics for all currencies examined in this study may not be justifiable.

Originality/value

To the best of the authors' knowledge, this study is the first attempt to use the test statistic based on the information‐theoretical method in testing nonlinearity in financial and economic time series.

Citation

Zhang, Y., Soofi, A.S. and Wang, S. (2011), "Testing for nonlinearity of exchange rates: an information‐theoretic approach", Journal of Economic Studies, Vol. 38 No. 6, pp. 637-657. https://doi.org/10.1108/01443581111177367