Detecting regime shifts in Malaysian exchange rates


  • Mohd Tahir Ismail
  • Zaidi Isa



Exchange rates, Nonlinearity, Structural breaks, Markov switching autoregressive model,


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.


R. E. Quandt, Journal of The American Statistical Association. 53 (1958) 873-880.

S. M. Goldfeld, and R. E. Quandt, Journal of Econometrics. 1 (1973) 3–16.

J.D. Hamilton, Econometrica, 57 (1989), 357-384.

C. Engle, and J. D. Hamilton, American Economic Review 80 (1990) 689-713.

C.J. Kim and C.R. Nelson, MIT Press, MIT 1999.

C. Engle, and C. Hakkio, International Journal of Finance Economics 1 (1996) 55-67.

I. W. Marsh, Journal of Forecasting 19 (2000) 123-134.

G.M. Caporale and N. Spagnolo, Applied Financial Economics, 14 (2004), 233-242.

U.M. Bergman and J. Hansson, Journal of International Money and Finance 24 (2005) 121-138.

R. Garcia and P. Perron, Review of Economics and Statistics, 78 (1996), 111-125.

S. J. Taylor, John Wiley & Son, New York 1986.

C. J. Kim, Journal of Econometrics 60 (1994) 1–22.

J.D. Hamilton, Journal of Econometrics, 45 (1990) 39–70.

J.D. Hamilton, in: G.S. Maddala, C. R. Rao, and H. D. Vinod (Eds.), Handbook of Statistics 11, North-Holland, Amsterdam 1993, pp. 231-260.

J.D. Hamilton, Princeton University Press, Princeton 1994.

C.J. Kim and C.R. Nelson, MIT Press, MIT 1999.

A. I. McLeod, and W. K. Li, Journal of Time Series Analysis. 4 (1983) 269-273.

J. B. Ramsey, Journal of the Royal Statistical Society B. 31 (1969) 350-371.

W. A. Brock, W. D. Dechert, J. Scheinkman, and B. LeBaron, Econometrics Reviews 115 (1996) 197-235.

B. L. Brown, J. Durbin, and J. M.Evan, Journal of the Royal Statistical Society B 35 (1975) 149-192.

D. W. K. Andrew, and W. Ploberger, Econometrica 62 (1994) 1383-1414.

J. Bai, and P. Perron, Journal of Applied Econometrics 18 (2003) 1-22.

R.B. Davies, Biometrika, 74 (1987), 33-43.

H. Akaike, in: B. N. Petrov, and F. Csaki, (Eds.), 2nd International Symposium on Information Theory, Akademiai Kiado, Budapest, 1973, pp. 267-281.

G. Schwartz, Annals of Statistics 6 (1978) 461-464.

S. H. Kim, and M. Haque, Multinational Business Review 10(2002) 37-44.

M. S. M. Peria, Empirical Economics 27(2002) 299-334.