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Comparing Forecast Performance of Exchange Rate Models

Author

Listed:
  • Lillie Lam

    (Research Department, Hong Kong Monetary Authority)

  • Laurence Fung

    (Research Department, Hong Kong Monetary Authority)

  • Ip-wing Yu

    (Research Department, Hong Kong Monetary Authority)

Abstract

Exchange-rate movement is regularly monitored by central banks for macroeconomic-analysis and market-surveillance purposes. Notwithstanding the pioneering study of Meese and Rogoff (1983), which shows the superiority of the random-walk model in out-of-sample exchange-rate forecast, there is some evidence that exchange-rate movement may be predictable at longer time horizons. This study compares the forecast performance of the Purchasing Power Parity model, Uncovered Interest Rate Parity model, Sticky Price Monetary model, the model based on the Bayesian Model Averaging technique, and a combined forecast of all the above models with benchmarks given by the random-walk model and the historical average return. Empirical results suggest that the combined forecast outperforms the benchmarks and generally yields better results than relying on a single model.

Suggested Citation

  • Lillie Lam & Laurence Fung & Ip-wing Yu, 2008. "Comparing Forecast Performance of Exchange Rate Models," Working Papers 0808, Hong Kong Monetary Authority.
  • Handle: RePEc:hkg:wpaper:0808
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    File URL: http://www.info.gov.hk/hkma/eng/research/working/pdf/HKMAWP08_08_full.pdf
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    References listed on IDEAS

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    Cited by:

    1. Ásgeir Daníelsson, 2014. "Should Icelandic pension funds hedge currency risk in their foreign investments?," Economics wp65, Department of Economics, Central bank of Iceland.
    2. Mihaela Simionescu (Bratu), 2014. "The Performance of Predictions Based on the Dobrescu Macromodel for the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 179-195, October.
    3. 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.
    4. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    5. Federici, Daniela & Gandolfo, Giancarlo, 2012. "The Euro/Dollar exchange rate: Chaotic or non-chaotic? A continuous time model with heterogeneous beliefs," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 670-681.
    6. Bratu Mihaela, 2013. "An Evaluation Of Usa Unemployment Rate Forecasts In Terms Of Accuracy And Bias. Empirical Methods To Improve The Forecasts Accuracy," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 170-180, February.
    7. Seddha-udom, Thanaporn, 2014. "Daily Exchange Rate Determination: Short-Term Speculation And Longerterm Expectation," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 10(1-2), January.

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    More about this item

    Keywords

    Bayesian Analysis; Model Evaluation and Selection; Forecasting and Other Model Application;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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