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Une réévaluation sur données récentes des performances prédictives des modèles monétaires de taux de change relativement à la marche aléatoire

Published online by Cambridge University Press:  17 August 2016

Hélène Raymond
Hélène Raymond*
Affiliation:
Université de Paris I
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Résumé

L'étude de Meese et Rogoff [1983] a démontré que les modèles monétaires étaient incapables de mieux prévoir les taux de change qu'une simple marche aléatoire. Dix ans apràs la parution de cet article l'imprévisibilité des variations du dollar ne semble remise en cause que par des exemples isolés, sans portée générale. une réévaluation systématique des résultats de Meese et Rogoff sur données récentes permet de faire le point sur l'apport empirique des modéles monétaires de taux de change, sur données récentes. II apparaît que la marche aléatoire est insurpassable en prévision à un horizon court (un mois ou un trimestre). Au delé, certains modèles, sur des monnaies particulières, réussissent parfois à améliorer des prévisions. Cependant, même les modàles les moins restrictifs ne parviennent jamais à ≪ battre ≫la marche aléatoire sur l'ensemble des monnaies considérées.

Summary

Summary

Meese and Rogoff [1983] paper demonstrated that monetary models are unable to forecast exchange rates better than a random walk. Ten years after this study, exchange rates unpredictability remains unchallenged except by some isolated examples. A complete revaluation of Meese and Rogoff conclusions enables us to clarify this point, with the help of recent data. Our results show that the random walk is the best forecasting model at short term. Some models, for some dollar exchange rates, perform better at long term. But no model brings systematic improvement upon the random walk.

Keywords

F31C53F31C53
Type
Research Article
Information
Recherches Économiques de Louvain/ Louvain Economic Review , Volume 61 , Issue 1 , 1995 , pp. 69 - 97
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1995 

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References

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