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

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  • Manish Kumar

    (IREVNA, A Division of CRISIL, Chennai: 600016, India)

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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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    Bibliographic Info

    Article provided by Technological Educational Institute (TEI) of Kavala, Greece in its journal International Journal of Economic Sciences and Applied Research (IJESAR).

    Volume (Year): 3 (2010)
    Issue (Month): 2 (December)
    Pages: 21-39

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    Handle: RePEc:tei:journl:v:3:y:2010:i:2:p:21-39

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    Related research

    Keywords: Stock Prices; Exchange Rates; Bivariate Causality; Forecasting;

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    1. 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.
    2. 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.
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