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

Author

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  • Manish Kumar

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

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.

Suggested Citation

  • Manish Kumar, 2010. "A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 3(2), pages 21-39, December.
    • Handle: RePEc:tei:journl:v:3:y:2010:i:2:p:21-39
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    References listed on IDEAS

    as
    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. Vygodina, Anna V., 2006. "Effects of size and international exposure of the US firms on the relationship between stock prices and exchange rates," Global Finance Journal, Elsevier, vol. 17(2), pages 214-223, December.
    3. 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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    Cited by:

    1. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    2. Boubekeur Baba & Güven Sevil, 2021. "Bayesian analysis of time-varying interactions between stock returns and foreign equity flows," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-25, December.
    3. Mihaela Simionescu, 2014. "Directional accuracy for inflation and unemployment rate predictions in Romania," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 7(2), pages 129-138, September.
    4. Lai, Hung-Cheng & Wang, Kuan-Min, 2014. "Relationship between the trading behavior of three institutional investors and Taiwan Stock Index futures returns," Economic Modelling, Elsevier, vol. 41(C), pages 156-165.

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    More about this item

    Keywords

    Stock Prices; Exchange Rates; Bivariate Causality; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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