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Bayesian Model Averaging and exchange rate forecasts

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Author Info
Jonathan H. Wright

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Abstract

Exchange rate forecasting is hard and the seminal result of Meese and Rogoff (1983) that the exchange rate is well approximated by a driftless random walk, at least for prediction purposes, has never really been overturned despite much effort at constructing other forecasting models. However, in several other macro and financial forecasting applications, researchers in recent years have considered methods for forecasting that combine the information in a large number of time series. One method that has been found to be remarkably useful for out-of-sample prediction is simple averaging of the forecasts of different models. This often seems to work better than the forecasts from any one model. Bayesian Model Averaging is a closely related method that has also been found to be useful for out-of-sample prediction. This starts out with many possible models and prior beliefs about the probability that each model is the true one. It then involves computing the posterior probability that each model is the true one, and averages the forecasts from the different models, weighting them by these posterior probabilities. This is effectively a shrinkage methodology, but with shrinkage over models not just over parameters. I apply this Bayesian Model Averaging approach to pseudo-out-of-sample exchange rate forecasting over the last ten years. I find that it compares quite favorably to a driftless random walk forecast. Depending on the currency-horizon pair, the Bayesian Model Averaging forecasts sometimes do quite a bit better than the random walk benchmark (in terms of mean square prediction error), while they never do much worse. The forecasts generated by this model averaging methodology are however very close to (but not identical to) those from the random walk forecast.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 779.

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Date of creation: 2003
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Keywords: Foreign exchange rates ; Forecasting;

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile. [Downloadable!]
  2. Jorge Selaive & Vicente Tuesta, 2004. "Can Fluctuations in the Consumption-Wealth Ratio Help to Predict Exchange Rates?," International Finance 0404014, EconWPA. [Downloadable!]
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  3. Theo Eicher & Christian Henn & Chris Papageorgiou, 2008. "Trade Creation and Diversion Revisited: Accounting for Model Uncertainty and Natural Trading Partner Effects," IMF Working Papers 08/66, International Monetary Fund. [Downloadable!]
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  4. William A. Brock & Steven N. Durlauf & Kenneth D. West, 2004. "Model Uncertainty and Policy Evaluation: Some Theory and Empirics," NBER Working Papers 10916, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  5. George Kapetanios & Vincent Labhard & Simon Price, . "Forecast combination and the Bank of England’s suite of statistical forecasting models," Bank of England working papers 323, Bank of England. [Downloadable!]
    Other versions:
  6. Andrew Swiston & Tamim Bayoumi, 2008. "Spillovers Across NAFTA," IMF Working Papers 08/3, International Monetary Fund. [Downloadable!]
  7. Jonathan H. Wright, 2003. "Forecasting U.S. inflation by Bayesian Model Averaging," International Finance Discussion Papers 780, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  8. Diego Nocetti & William T. Smith, 2006. "Why Do Pooled Forecasts Do Better Than Individual Forecasts Ex Post?," Economics Bulletin, Economics Bulletin, vol. 4(36), pages 1-7. [Downloadable!]
  9. Inoue, Atsushi & Kilian, Lutz, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  10. George Kapetanios & Vincent Labhard & Simon Price, . "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Bank of England working papers 268, Bank of England. [Downloadable!]
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  11. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York. [Downloadable!]
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  12. Andrew Swiston & Tamim Bayoumi, 2007. "Foreign Entanglements: Estimating the Source and Size of Spillovers Across Industrial Countries," IMF Working Papers 07/182, International Monetary Fund. [Downloadable!]
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  13. Neil R. Ericsson, 2008. "The fragility of sensitivity analysis: an encompassing perspective," International Finance Discussion Papers 959, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  14. Han Hong & Bruce Preston, 2008. "Bayesian Averaging, Prediction and Nonnested Model Selection," NBER Working Papers 14284, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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