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Forecasting Exchange Rates: The Multi-State Markov-Switching Model with Smoothing

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Abstract

This paper presents an exchange rate forecasting model which combines the multi-state Markov-switching model with smoothing techniques. The model outperforms a random walk at short horizons and its superior forecastability appears to be robust over different sample spans. Our finding hinges on the fact that exchange rates tend to follow highly persistent trends and accordingly, the key to beating the random walk is to identify these trends. An attempt to link the trends in exchange rates to the underlying macroeconomic determinants further reveals that fundamentals-based linear models generally fail to capture the persistence in exchange rates and thus are incapable of outforecasting the random walk.

Suggested Citation

  • Chunming Yuan, 2008. "Forecasting Exchange Rates: The Multi-State Markov-Switching Model with Smoothing," UMBC Economics Department Working Papers 09-115, UMBC Department of Economics, revised 01 Nov 2009.
    • Handle: RePEc:umb:econwp:09115
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    1. is not listed on IDEAS
    2. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    3. Kodongo, Odongo & Ojah, Kalu, 2012. "The dynamic relation between foreign exchange rates and international portfolio flows: Evidence from Africa's capital markets," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 71-87.
    4. Jarosław Malczewski & Wawrzyniec Czubak, 2021. "Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    5. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    6. Iván Cárdenas-Gallo & Raha Akhavan-Tabatabaei & Mauricio Sánchez-Silva & Emilio Bastidas-Arteaga, 2016. "A Markov regime-switching framework to forecast El Niño Southern Oscillation patterns," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 829-843, March.
    7. A. ISLAS & Víctor M. GUERRERO & Eliud SILVA, 2019. "Forecasting Remittances to Mexico with a Multi-State Markov-Switching Model Applied to the Trend with Controlled Smoothness," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 38-56, March.
    8. Mirna Dumicic, 2015. "Financial stress indicators for small, open, highly euroized countries: the case of Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 39(2), pages 171-203.
    9. repec:ipf:finteo:v:39:y:2015:i:3:p:171-203 is not listed on IDEAS
    10. Mirna Dumičić, 2014. "Financial Stress Indicators for Small, Open, Highly Euroised Countries – the Case of Croatia," Working Papers 41, The Croatian National Bank, Croatia.
    11. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
    12. Iván Cárdenas-Gallo & Raha Akhavan-Tabatabaei & Mauricio Sánchez-Silva & Emilio Bastidas-Arteaga, 2016. "A Markov regime-switching framework to forecast El Niño Southern Oscillation patterns," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 829-843, March.
    13. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models," Papers 2011.03741, arXiv.org, revised Dec 2020.
    14. Pierdzioch, Christian & Rülke, Jan-Christoph, 2015. "On the directional accuracy of forecasts of emerging market exchange rates," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 369-376.
    15. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach," Future Internet, MDPI, vol. 12(3), pages 1-19, March.
    16. Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).
    17. Shaghayegh KORDNOORI & Hamidreza MOSTAFAEI & Shirin KORDNOORI, 2015. "Applied SCGM(1,1)c Model and Weighted Markov Chain for Exchange Rate Ratios," Hyperion Economic Journal, Faculty of Economic Sciences, Hyperion University of Bucharest, Romania, vol. 3(4), pages 12-22, December.
    18. Samet G nay, 2015. "Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 979-985.
    19. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.

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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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