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

Manish Kumar
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Manish Kumar: IREVNA, A Division of CRISIL, Chennai: 600016, India

International Journal of Business and Economic Sciences Applied Research (IJBESAR), 2010, vol. 3, issue 2, 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. 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.

Keywords: Stock Prices; Exchange Rates; Bivariate Causality; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 F31 G10 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:tei:journl:v:3:y:2010:i:2:p:21-39

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