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. 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.
Volume (Year): 3 (2010)
Issue (Month): 2 (December)
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- Qi, Min & Wu, Yangru, 2003. "Nonlinear prediction of exchange rates with monetary fundamentals," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 623-640, December.
- Woo, Wing T., 1985. "The monetary approach to exchange rate determination under rational expectations: The dollar-deutschmark rate," Journal of International Economics, Elsevier, vol. 18(1-2), pages 1-16, February.
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