A mixed-frequency approach for exchange rates predictions


Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding both alternative predictors (e.g., interest rates, price levels) and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach with an application to CAD/USD exchange rate predictions.


DOI Code: 10.1285/i20705948v14n1p230

Keywords: MIDAS; linear regression; frequency alignment; forecasting


Abhyankar, A., Sarno, L., and Valente, G. (2005). Exchange rates and fundamentals: evidence on the economic value of predictability. Journal of International Economics, 66(2):325–348.

Alquist, R. and Chinn, M. D. (2008). Conventional and unconventional approaches to exchange rate modelling and assessment. International Journal of Finance & Economics, 13(1):2–13.

Bacchetta, P., van Wincoop, E., and Beutler, T. (2009). Can parameter instability explain the Meese-Rogoff puzzle? NBER International Seminar on Macroeconomics, 6(1):125–173.

Berkowitz, J. and Giorgianni, L. (2001). Long-horizon exchange rate predictability? Review of Economics and Statistics, 83(1):81–91.

Carlstrom, C. T. and Fuerst, T. S. (1999). Forecasts and sunspots: Looking back for a better future. Economic commentary, Federal Reserve Bank of Cleveland.

Cheung, Y.-W., Chinn, M. D., and Pascual, A. G. (2005). Empirical exchange rate models of the nineties: Are any fit to survive? Journal of International Money and Finance, 24(7):1150–1175.

Cheung, Y.-W., Chinn, M. D., Pascual, A. G., and Zhang, Y. (2019). Exchange rate prediction redux: new models, new data, new currencies. Journal of International Money and Finance, 95:332–362.

Chinn, M. (2012). Macro approaches to foreign exchange determination. Handbook of Exchange Rates, pages 45–71.

Chinn, M. D. and Meese, R. A. (1995). Banking on currency forecasts: how predictable is change in money? Journal of International Economics, 38(1-2):161–178.

Choi, W. G. and Oh, S. (2003). A money demand function with output uncertainty, monetary uncertainty, and financial innovations. Journal of Money, Credit, and Banking, 35(5):685–709.

Chung, S. S. and Zhang, S. (2017). Volatility estimation using support vector machine: Applications to major foreign exchange rates. Electronic Journal of Applied Statistical Analysis, 10(2):499–511.

Clark, T. E. and West, K. D. (2006). Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. Journal of Econometrics, 135(1-2):155–186.

Diebold, F. X. (2015). Comparing predictive accuracy, twenty

years later: A personal perspective on the use and abuse of Diebold–Mariano tests. Journal of Business & Economic Statistics, 33(1):1–1.

Diebold, F. X. and Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics, 20(1):134–144.

Ferraro, D., Rogoff, K., and Rossi, B. (2015). Can oil prices forecast exchange rates? an empirical analysis of the relationship between commodity prices and exchange rates. Journal of International Money and Finance, 54:116–141.

Fisher, I. (1896). Appreciation and Interest: A Study of the Influence of Monetary Appreciation and Depreciation on the Rate of Interest with Applications to the Bimetallic Controversy and the Theory of Interest. Macmillan Company.

Foroni, C., Marcellino, M., and Schumacher, C. (2015). Unrestricted mixed data sampling (midas): Midas regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(1):57–82.

Frenkel, J. A. (1976). A monetary approach to the exchange rate: doctrinal aspects and empirical evidence. The Scandinavian Journal of Economics, pages 200–224.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004). The midas touch: Mixed data sampling regression models.

Ghysels, E., Sinko, A., and Valkanov, R. (2007). Midas regressions: Further results and new directions. Econometric Reviews, 26(1):53–90.

Giacomini, R. and Rossi, B. (2010). Forecast comparisons in unstable environments. Journal of Applied Econometrics, 25(4):595–620.

Groen, J. J. (1999). Long horizon predictability of exchange rates: Is it for real? Empirical Economics, 24(3):451–469.

Groen, J. J. (2002). Cointegration and the monetary exchange rate model revisited. Oxford Bulletin of Economics and Statistics, 64(4):361–380.

Hodrick, R. J. and Prescott, E. C. (1997). Postwar us business cycles: an empirical investigation. Journal of Money, Credit, and Banking, pages 1–16.

Kilian, L. (1999). Exchange rates and monetary fundamentals: what do we learn from long-horizon regressions? Journal of Applied Econometrics, 14(5):491–510.

Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3):159–178.

Marcellino, M. (1999). Some consequences of temporal aggregation in empirical analysis. Journal of Business & Economic Statistics, 17(1):129–136.

Mark, N. C. (1995). Exchange rates and fundamentals: Evidence on long-horizon predictability. The American Economic Review, pages 201–218.

Meese, R. A. and Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1-2):3–24.

Meese, R. A. and Rogoff, K. S. (1988). Was it real? the exchange rate-interest differential relation over the modern floating-rate period. the Journal of Finance, 43(4):933–948.

Molodtsova, T., Nikolsko-Rzhevskyy, A., and Papell, D. H. (2011). Taylor rules and the euro. Journal of Money, Credit and Banking, 43(2-3):535–552.

Molodtsova, T. and Papell, D. H. (2009). Out-of-sample exchange rate predictabilitywith taylor rule fundamentals. Journal of International Economics, 77(2):167–180.

Ramzan, S., Ramzan, S., and Zahid, F. M. (2012). Modeling and forecasting exchange rate dynamics in pakistan using arch family of models. Electronic Journal of Applied Statistical Analysis, 5(1):15–29.

Rogoff, K. S. and Stavrakeva, V. (2008). The continuing puzzle of short horizon exchange rate forecasting. Working paper 14071, National Bureau of Economic Research.

Rossi, B. (2013). Exchange rate predictability. Journal of economic literature, 51(4):1063–1119.

Rossi, B. and Inoue, A. (2012). Out-of-sample forecast tests robust to the choice of window size. Journal of Business & Economic Statistics, 30(3):432–453.

Said, S. E. and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3):599–607.

Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester conference series on public policy, 39:195–214.

Wieland, V. and Wolters, M. (2013). Forecasting and policy making. In Elliott, G. and Timmermann, A., editors, Handbook of economic forecasting, volume 2A, pages 239–325. North Holland.

Wright, J. H. (2008). Bayesian model averaging and exchange rate forecasts. Journal of Econometrics, 146(2):329–341.

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