Forecast comparison of exchange rate models with the Kalman filter

Pin Ng, Asdollah Heidari

Research output: Contribution to journalArticle

Abstract

We show that the structural models' out-of-sample predictive performance of the dollar/pound, dollar/mark, dollar/yen, and trade-weighted dollar exchange rates is inferior to that of the simple random walk. However, the application of a Kalman filter to the structural models, which corrects the time-varying properties of coefficients over time, improves the predictive performance of exchange rate models. Our findings suggest that the coefficients of time-series models and structural models which use time-series data have shifted over time. Thus, a model that utilizes this information may increase its predictive performance in in-sample and out-of-sample forecasts.

Original languageEnglish (US)
Pages (from-to)435-443
Number of pages9
JournalTechnological Forecasting and Social Change
Volume41
Issue number4
DOIs
StatePublished - Jun 1992
Externally publishedYes

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation

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