Detecting regime shifts in Malaysian exchange rates

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.

Modeling South African Banks closing stock prices: a Markov-Switching Approach

“…Therefore, theLR test results support a two-state regime for all the five variables. Similar results were reported by Ismail and Isa (2007), Psaradakis et al (2009), Wasin and Bandi (2011) and Yarmohammadi et al (2012). The information gathered from preliminary analysis was consolidated to run the analysis using the MS-AR(1) model and the results are summarised in…”

Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model

“…This class of models is flexible and has interesting properties, with the models being described by a mixture of two or more distributions. Ismail and Isa (2007) captured regime shifts behaviour in both the mean and variance of Malaysian ringgit exchange rates against British pound sterling, Australian dollar, Singapore dollar and Japanese yen in the period 1990 to 2005 using univariate 2-regime Markov switching autoregressive model (MS-AR) model. The results show that the model captured regime shifts successfully in all four series.…”

“…The study applied several tests of nonlinearity and nonstationry to assess if it is appropriate to use nonlinear models. Isa and Ismail (2007) in their work advised that it is wise to use different nonlinearity tests, since nonlinearity in time series may appear in several ways. We used two portmanteau tests are the McLeod-Li test and the BDS test.…”

Identifying Regime Shifts in South African Exchange Rates

“…Several studies have proved that conventional models are good in term of modelling and forecasting, but the models are incapable of reveal same attributes that can be found in financial and economic data. For instance, studies by Yarmohammadi et al, (2012), Amiri (2012), Ismail & Isa (2007) revealed that conventional models cannot explain the business cycles behaviour. In support of the mentioned studies, Medereios and Sobral (2011) pointed out that the analysis of business cycles have been restricted to linear methods, which are incapable to follow the fast and accelerated rhythm of constant change of the countries' economies.…”

“…The results indicate that the MS-AR model can be considered as useful model, with the best fit, to evaluate the behaviours of Iran's exchange rate. Ismail & Isa (2007) in their study used a univariate 2-regime Markov switching autoregressive model (MS-AR) 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.…”