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Predicting sharp depreciations in industrial country exchange rates

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  • Joseph E. Gagnon
  • Jonathan H. Wright

Abstract

This paper considers the prediction of large depreciations (both nominal and real) in a panel of industrialized countries using a probit methodology. The current account balance/GDP ratio has a modest but statistically significant effect on the estimated probability of a large depreciation, and gives slight predictive power in an out-of-sample forecasting exercise. The CPI inflation rate also has a modest but statistically significant effect in predicting nominal depreciations and has slight predictive power, but this effect is not present for real exchange rates. The GDP growth rate occasionally has a significant effect. A higher current account balance (surplus) tends to reduce the probability of a sharp depreciation; a higher inflation rate tends to increase the probability of a sharp depreciation; and a higher GDP growth rate perhaps tends to reduce the probability of a sharp depreciation.

Suggested Citation

  • Joseph E. Gagnon & Jonathan H. Wright, 2006. "Predicting sharp depreciations in industrial country exchange rates," International Finance Discussion Papers 881, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:881
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    1. Hali J. Edison, 2003. "Do indicators of financial crises work? An evaluation of an early warning system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 11-53.
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Kumar, Mohan & Moorthy, Uma & Perraudin, William, 2003. "Predicting emerging market currency crashes," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 427-454, September.
    4. Freund, Caroline, 2005. "Current account adjustment in industrial countries," Journal of International Money and Finance, Elsevier, vol. 24(8), pages 1278-1298, December.
    5. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    6. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    7. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    8. Caroline L. Freund, 2000. "Current account adjustment in industrialized countries," International Finance Discussion Papers 692, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. Meredith Beechey & Pär Österholm, 2008. "A Bayesian Vector Autoregressive Model with Informative Steady‐state Priors for the Australian Economy," The Economic Record, The Economic Society of Australia, vol. 84(267), pages 449-465, December.
    2. Cappiello, Lorenzo & Mehl, Arnaud, 2007. "Uncovered interest parity at distant horizons: evidence on emerging economies & nonlinearities," Working Paper Series 801, European Central Bank.
    3. Carol C. Bertaut & Steven B. Kamin & Charles P. Thomas, 2008. "How long can the unsustainable U.S. current account deficit be sustained?," International Finance Discussion Papers 935, Board of Governors of the Federal Reserve System (U.S.).
    4. Pao-Lin Tien, 2009. "Using Long-Run Restrictions to Investigate the Sources of Exchange Rate Fluctuations," Wesleyan Economics Working Papers 2009-004, Wesleyan University, Department of Economics.

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    Keywords

    Foreign exchange rates; Econometric models;

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