Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/66655
Authors: 
Kumar, Manish
Year of Publication: 
2010
Citation: 
[Journal:] International Journal of Economic Sciences and Applied Research [ISSN:] 1791-3373 [Volume:] 3 [Year:] 2010 [Issue:] 2 [Pages:] 21-39
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.
Subjects: 
stock prices
exchange rates
bivariate causality
forecasting
JEL: 
C22
C52
C53
F31
G10
Document Type: 
Article

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