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Markov switching in exchange rate models: will more regimes help?

  • Josh StillwagonEmail author
  • Peter Sullivan
Article
  • 18 Downloads

Abstract

This paper examines the performance of Markov switching models of the exchange rate using a data-driven approach to determine the number of regimes rather than simply assuming two states. The analysis is conducted for the British pound, Canadian dollar, and Japanese yen exchange rates against the US dollar over the last 30 years with alternative specifications including a simple segmented trends model and Markov switching autoregressive models with monetary fundamentals. A noteworthy finding is that the number of regimes that minimizes mean square forecast errors tends to correspond to the number of regimes selected by Bayesian information criteria (but not Markov-switching-specific information criteria). For the monetary models, the number of regimes that minimizes forecast errors also tends to correspond to the most parsimonious model with well-behaved residuals. Although allowing for more regimes yields forecasting improvement over single- or two-regime models, the Markov switching model is still unable to outperform a random walk. This suggests that exchange rate models need to allow for novel, as opposed to repetitive or predetermined, structural change.

Keywords

Exchange rates Markov switching Monetary models Segmented trends 

JEL Classification

F31 C24 

Notes

Compliance with ethical standards

Funding

This research was funded by the Institute for New Economic Thinking (INET), Grant Number #INO16-00012.

Conflict of interest

The authors declare that they have no conflict of interest to report.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

181_2019_1623_MOESM1_ESM.pdf (95 kb)
Supplementary material 1 (pdf 95 KB)

References

  1. Akaike H (1973) Information theory as an extension of the maximum likelihood principle. In: Petrov N, Csaki F (eds) Second international symposium on information theory. Akademiai Kiado, Budapest, pp 1–21Google Scholar
  2. Bacchetta P, van Wincoop E (2004) A scapegoat model of exchange-rate fluctuations. Am Econ Rev 94(2):114–118Google Scholar
  3. Balcilar M, Hammoudeh S, Asaba N-AF (2018) A regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates. Int Rev Econ Finance 40:42–89Google Scholar
  4. Basher SA, Haug AA, Sadorsky P (2016) The impact of oil shocks on exchange rates: a Markov-switching approach. Energy Econ 54:11–23Google Scholar
  5. Beckmann J, Czudaj R (2013) Oil prices and effective dollar exchange rates. Int Rev Econ Finance 27:621–636Google Scholar
  6. Beckmann J, Belke A, Kühl M (2011) The Dollar-Euro exchange rate and macroeconomic fundamentals: a time-varying coefficients approach. Rev World Econ 147(1):11–40Google Scholar
  7. Caporale GM, Menla Ali F, Spagnolo F, Spagnolo N (2017) International portfolio flows and exchange rate volatility in emerging Asian markets. J Int Money Finance 76:1–15Google Scholar
  8. Casarin R, Sartore D, Tronzano M (2018) A Bayesian Markov-switching correlation model for contagion analysis on exchange rate markets. J Bus Econ Stat 36(1):101–114Google Scholar
  9. Cavicchioli M (2014a) Analysis of the likelihood function for Markov-switching VAR(CH) models. J Time Ser Anal 35(6):624–639Google Scholar
  10. Cavicchioli M (2014b) Determining the number of regimes in Markov-switching VAR and VMA models. J Time Ser Anal 35(2):173–186Google Scholar
  11. Cheung Y-W, Chinn M (2001) Currency traders and exchange rate dynamics: a survey of the US market. J Int Money Finance 20:439–471Google Scholar
  12. Cheung Y-W, Erlandsson UG (2005) Exchange rates and Markov switching dynamics. J Bus Econ Stat 23(2):314–320Google Scholar
  13. Cheung Y-W, Chinn MD, Pascual AG (2005) Empirical exchange rate models of the nineties: are any fit to survive? J Money Finance 24:1150–1175Google Scholar
  14. De Grauwe P, Grimaldi M (2006) Exchange rate puzzles: a tale of switching attractors. Eur Econ Rev 50(1):1–33Google Scholar
  15. De Grauwe P, Vansteenkiste I (2007) Exchange rates and fundamentals: a non-linear relationship? Tech Rep 1:37–54Google Scholar
  16. Dielbold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13:253–263Google Scholar
  17. Engel C (1994) Can the Markov switching model forecast exchange rates? J Int Econ 36:689–713Google Scholar
  18. Engel C, Hamilton JD (1990) Long swings in the exchange rate: are they in the data and do markets know it? Am Econ Rev 80:689–713Google Scholar
  19. Engel C, Mark NC, West KD (2008) Exchange rate models are not as bad as you think. In: NBER macroeconomics annual 2007, vol 36, no 1. University of Chicago Press, pp 151–165Google Scholar
  20. Francq C, Zakoïa JM (2001) Stationarity of multivariate Markov-switching ARMA models. J Econ 102(2):339–364Google Scholar
  21. Frankel JA (1979) On the mark: a theory of floating exchange rates based on real interest differentials. Am Econ Rev 69(4):610–622Google Scholar
  22. Frankel JA, Froot KA (1990) Chartists, fundamentalists and trading in the foreign exchange market. Am Econ Rev 80(2):181–185Google Scholar
  23. Frankel JA, Rose AK (1995) A panel project on purchasing power parity: mean reversion within and between countries. In: NBER working papers 5006. National Bureau of Economic ResearchGoogle Scholar
  24. Frömmel M, MacDonald R, Menkhoff L (2005) Markov switching regimes in a monetary exchange rate model. Econ Model 22:485–502Google Scholar
  25. Frydman R, Goldberg MD (2007) Imperfect knowledge economics: exchange rates and risk. Princeton University Press, PrincetonGoogle Scholar
  26. Frydman R, Goldberg MD (2011) Beyond mechanical markets. Princeton University Press, PrincetonGoogle Scholar
  27. Frydman R, Stillwagon JR (2018) Fundamental factors and extrapolation in stock-market expectations: The central role of structural change. J Econ Behav Organ 148(12):189–198Google Scholar
  28. Goldberg MD, Frydman R (1996) Imperfect knowledge and behaviour in the foreign exchange market. Econ J 106(473):869–893Google Scholar
  29. Goldberg MD, Frydman R (2001) Macroeconomic fundamentals and the DM/$ exchange rate: temporal instability and the monetary model. Int J Finance Econ 6(4):421–435Google Scholar
  30. Greenwood R, Shleifer A (2014) Expectations of returns and expected returns. Rev Financ Stud 27(3):714–746Google Scholar
  31. Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384Google Scholar
  32. Hamilton JD (2016) Macroeconomic regimes and regime shifts. In: Taylor J, Uhlig H (eds) Handbook of macroeconomics, vol 2. University of Chicago, ChicagoGoogle Scholar
  33. Johansen S, Juselius K, Frydman R, Goldberg MD (2010) Testing hypotheses in an I(2) model with piecewise linear trends An analysis of long persistent swings in the Dmk/\({\$}\) rate. J Econ 158(1):117–129Google Scholar
  34. Juselius K, Stillwagon JR (2018) Are outcomes driving expectations or the other way around? An I(2) CVAR with interest rate expectations in the Dollar/Pound market. J Int Money Finance 83(60):93–105Google Scholar
  35. Kilian L, Taylor MP (2003) Why is it so difficult to beat the random walk forecast of exchange rates? J Int Econ 60(1):85–107Google Scholar
  36. Krolzig H-M, Hendry DF (2001) Computer automation of general-to-specific model selection procedures. J Econ Dyn Control 25(6):831–866Google Scholar
  37. Marsh IW (2000) High-frequency Markov switching models in the foreign exchange market. J Forecast 19(2):123–134Google Scholar
  38. Meese R, Rogoff KD (1983) Empirical exchange rate models of the seventies: do they fit out of sample. J Int Econ 14(2):3–24Google Scholar
  39. Psaradakis Z, Spagnolo N (2006) Joint determination of the state dimension and autoregressive order for models with in Markov regime switching. J Time Ser Anal 27(5):753–766Google Scholar
  40. Rossi B (2006) Are exchange rates really random walks? Some evidence robust to parameter instability. Macroecon Dyn 10(3):554–579Google Scholar
  41. Sarno L, Valente G (2009) Exchange rates and fundamentals: footloose or evolving relationship. J Eur Econ Assoc 7(4):789–830Google Scholar
  42. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464Google Scholar
  43. Smith A, Naik PA, Tsai C-L (2006) Markov-switching model selection using Kullback-Leibler divergence. J Econ 134:553–557Google Scholar
  44. Soros G (2008) The crash of 2008 and what it means: The new paradigm for financial markets. PublicAffairsGoogle Scholar
  45. Stillwagon JR (2018) Are risk premia related to real exchange rate swings? Evidence from I(2) CVARs with survey expectations. Macroecon Dyn 22(2):255–278Google Scholar
  46. Wolff CCP (1987) Time-varying parameters and the out-of-sample forecasting performance of structural exchange rate models. J Bus Econ Stat 5(1):87–97Google Scholar
  47. Zhang J, Stine RA (2001) Autocovariance structure of Markov regime switching models and model selection. J Time Ser Anal 22(1):107–124Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Economics DivisionBabson CollegeBabson ParkUSA
  2. 2.Department of EconomicsUniversity of Puget SoundTacomaUSA
  3. 3.Institute for New Economic Thinking (INET) Program on Imperfect Knowledge Economics (IKE)New YorkUSA

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