Artikel
A time-varying parameter vector autoregression model for forecasting emerging market exchange rates
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.
- Language
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Englisch
- Bibliographic citation
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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
- Classification
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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)
- Subject
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stock prices
exchange rates
bivariate causality
forecasting
- Event
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Geistige Schöpfung
- (who)
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Kumar, Manish
- Event
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Veröffentlichung
- (who)
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Kavala Institute of Technology
- (where)
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Kavala
- (when)
-
2010
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Kumar, Manish
- Kavala Institute of Technology
Time of origin
- 2010
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