A time-varying parameter vector autoregression model for forecasting emerging market exchange rates

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: International Journal of Economic Sciences and Applied Research ; ISSN: 1791-3373 ; Volume: 3 ; Year: 2010 ; Issue: 2 ; Pages: 21-39 ; Kavala: Kavala Institute of Technology

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; Foreign Exchange ; General Financial Markets: General (includes Measurement and Data)
• Thema: stock prices ; exchange rates ; bivariate causality ; forecasting

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Kumar, Manish
• Ereignis: Veröffentlichung
• (wer): Kavala Institute of Technology
• (wo): Kavala
• (wann): 2010

Weitere Informationen

• Handle: http://hdl.handle.net/10419/66655
• Letzte Aktualisierung: 10.03.2025, 11:41 MEZ