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A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates

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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. 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.

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