A time-varying parameter vector autoregression model for forecasting emerging market exchange rates
Part of : International Journal of Business and Economic Sciences Applied Research ; Vol.3, No.2, 2010, pages 21-39
Issue:
Pages:
21-39
Author:
Abstract:
In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting performance of the TVP-VAR model is evaluated against the simple VAR and ARIMA models, by employing a cross-validation process and metrics such as mean absolute error, root mean square error, and directional accuracy. Out-of-sample results in terms of conventional forecast evaluation statistics and directional accuracy show TVP-VAR model consistently outperforms the simple VAR and ARIMA models.
Subject:
Keywords:
stock prices, exchange rates, bivariate causality, forecasting
Notes:
Περιέχει πίνακες και βιβλιογραφία, JEL Classification: C22, C52, C53, F31, G10
