A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates
A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates
Author(s): Manish KumarSubject(s): Economy
Published by: Τεχνολογικό Εκπαιδευτικό Ίδρυμα Ανατολικής Μακεδονίας και Θράκης
Keywords: Stock Prices; Exchange Rates; Bivariate Causality; Forecasting
Summary/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. Outof- sample results in terms of conventional forecast evaluation statistics and directional accuracy show TVP-VAR model consistently outperforms the simple VAR and ARIMA models.
Journal: International Journal of Economic Sciences and Applied Research
- Issue Year: III/2010
- Issue No: 2
- Page Range: 21-39
- Page Count: 19
- Language: English