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Multivariate Nearest-Neighbor Forecasts of EMS Exchange Rates

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  • Mizrach, B

Abstract

Exchange rate modelling has been a persistent puzzle for international economists. Forecasts from popular models for the exchange rate generally fail to improve upon the random walk out-of-sample. While a multivariate nonparametric approach provides useful information about exchange rates, the model produces forecasts superior to the random walk for only one of the three EMS currencies examined. Using a statistic developed in Mizrach (1991), I find that the forecast improvement, a 4.5 percent reduction in mean squared error for the Lira in daily returns, is not statistically significant. A cross-validation exercise suggests that the improvement is also not robust. Copyright 1992 by John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 7 (1992)
Issue (Month): S (Suppl. Dec.)
Pages: S151-63

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Handle: RePEc:jae:japmet:v:7:y:1992:i:s:p:s151-63

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Cited by:
  1. António Portugal Duarte & João Sousa Andrade & Adelaide Duarte, 2010. "Exchange Rate Target Zones: A Survey of the Literature," GEMF Working Papers 2010-14, GEMF - Faculdade de Economia, Universidade de Coimbra.
  2. Adam Clements & Joanne Fuller, 2012. "Forecasting increases in the VIX: A time-varying long volatility hedge for equities," NCER Working Paper Series 88, National Centre for Econometric Research.
  3. Theodore Panagiotidis, 2010. "An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application," Working Paper Series 20_10, The Rimini Centre for Economic Analysis.
  4. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero & Julián Andrada-Félix, . "Technical Analysis in Foreign Exchange Markets: Linear Versus Nonlinear Trading Rules," Working Papers on International Economics and Finance 00-02, FEDEA.
  5. Manzan, S., 2002. "Model Selection for Nonlinear Time Series," CeNDEF Working Papers 02-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  6. Óscar Bajo Rubio & Simón Sosvilla Rivero & Fernando Fernández Rodríguez, 2000. "Asymmetry In The Ems: New Evidence Based On Non-Linear Forecasts," Documentos de Trabajo - Lan Gaiak Departamento de Economía - Universidad Pública de Navarra 0001, Departamento de Economía - Universidad Pública de Navarra.
  7. Bruce Mizrach, 1996. "Mean Reversion in EMS Exchange Rates," Departmental Working Papers 199525, Rutgers University, Department of Economics.
  8. John Barkoulas & Christopher F. Baum & Atreya Chakraborty, 1996. "Nearest-Neighbor Forecasts of U.S. Interest Rates," Boston College Working Papers in Economics 313., Boston College Department of Economics, revised 01 Apr 2003.
  9. F. FernAndez-RodrIguez & S. Sosvilla-Rivero & J. Andrada-FElix, 2003. "Technical analysis in foreign exchange markets: evidence from the EMS," Applied Financial Economics, Taylor & Francis Journals, vol. 13(2), pages 113-122.
  10. Srichander Ramaswamy, 1998. "One-step prediction of financial time series," BIS Working Papers 57, Bank for International Settlements.
  11. Cao, Liangyue & Soofi, Abdol S., 1999. "Nonlinear deterministic forecasting of daily dollar exchange rates," International Journal of Forecasting, Elsevier, vol. 15(4), pages 421-430, October.
  12. António Portugal Duarte & João Sousa Andrade & Adelaide Duarte, 2012. "Testing for Nonlinear Adjustment in the Portuguese Target Zone: Is there a Honeymoon Effect?," GEMF Working Papers 2013-03, GEMF - Faculdade de Economia, Universidade de Coimbra.
  13. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
  14. Michael Pippenger & Gregory Goering, 1998. "Exchange Rate Forecasting: Results from a Threshold Autoregressive Model," Open Economies Review, Springer, vol. 9(2), pages 157-170, April.

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