Detecting regime shifts in Malaysian exchange rates

Mohd Tahir Ismail,Zaidi Isa

doi:10.11113/mjfas.v3n2.30

Abstract

Many financial and economic time series undergo episodes where the behaviour of the series seems to change quite dramatically. Such phenomena’s are referred to as regime shifts and cannot be modelled by a single equation linear model. Therefore to overcome this problem a nonlinear time series model is typically designed to accommodate this nonlinear feature in the data. In this paper, we use a univariate 2-regime Markov switching autoregressive model (MSAR) to capture regime shifts behaviour in both the mean and the variance in Malaysia ringgit exchange rates against four other countries namely the British pound sterling, the Australian dollar, the Singapore dollar and the Japanese yen between 1990 and 2005. The MS-AR model is found to successfully capture the timing of regime shifts in the four series and this regime shifts occurred because of financial crises such as the European financial crisis in 1992 and the Asian financial crisis in 1997. Furthermore, the significant result of the likelihood ratio test (LR test) justified the used of nonlinear MS-AR model rather than linear AR model.

Highlights

Financial time series always undergo episodes in which the behaviour of the series seems to change quite dramatically
In the following paper by Caporale and Spagnolo [8], they employed the Markov switching model to investigate the nonlinearity behaviour in three South East Asian countries exchange rates against the US dollar. They found that the Markov switching model with regime shifts in the mean and the variance were well suited to capture the nonlinearity in three exchange rates rather than the smooth transition autoregressive model (STAR) and the random walk specification
——— 2 As stated on the website all the exchange rates values are collected from Federal Reserve Bank of New York and/or International Monetary Fund (IMF). 3 As the Malaysian ringgit against the US dollar has been fixed since 1998, we can not use the series in our modelling because the analysis is done in changes

Summary

Introduction

Financial time series always undergo episodes in which the behaviour of the series seems to change quite dramatically. The study continues by March [7] who examined the performance of Markov switching model in capturing the behaviour of daily exchange rates of three countries against the US dollar He finds the data are well estimated by the Markov switching model but the out-sample forecasting are very poor because of parameter instability. In the following paper by Caporale and Spagnolo [8], they employed the Markov switching model to investigate the nonlinearity behaviour in three South East Asian countries exchange rates against the US dollar They found that the Markov switching model with regime shifts in the mean and the variance were well suited to capture the nonlinearity in three exchange rates rather than the smooth transition autoregressive model (STAR) and the random walk specification. In this paper we use a 2-regimes model to capture periods where the changes of exchange rates appreciate or depreciate

The Markov Switching Autoregressive Model

Application to Malaysian Exchange Rates

Nonlinearity Testing

Model Estimation

Conclusions