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Modelling and Forecasting the Indian Re/US Dollar Exchange Rate

Author

Listed:
  • Pami Dua

    (Department of Economics, Delhi School of Economics, Delhi, India)

  • Rajiv Ranjan

    (Reserve Bank of India, India)

Abstract

This paper develops vector autoregressive and Bayesian vector autoregressive models to forecast the Indian Re/US dollar exchange rate which is governed by a managed floating exchange rate regime. It considers extensions of the monetary model that include the forward premium, capital inflows, volatility of capital flows, order flows and central bank intervention. The study finds that the monetary model generally outperforms the naïve model. It also finds that forecast accuracy can be improved by extending the monetary model to include forward premium, volatility of capital inflows and order flow. Information on intervention by the central bank also helps to improve forecasts at the longer end. The study also reports that the Bayesian vector autoregressive models generally outperform their corresponding VAR variants.

Suggested Citation

  • Pami Dua & Rajiv Ranjan, 2011. "Modelling and Forecasting the Indian Re/US Dollar Exchange Rate," Working papers 197, Centre for Development Economics, Delhi School of Economics.
  • Handle: RePEc:cde:cdewps:197
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    File URL: http://www.cdedse.org/pdf/work197.pdf
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    References listed on IDEAS

    as
    1. Bjonnes, Geir Hoidal & Rime, Dagfinn, 2005. "Dealer behavior and trading systems in foreign exchange markets," Journal of Financial Economics, Elsevier, vol. 75(3), pages 571-605, March.
    2. Pasquale Della Corte & Lucio Sarno & Ilias Tsiakas, 2009. "An Economic Evaluation of Empirical Exchange Rate Models," Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3491-3530, September.
    3. Christopher J. Neely, 2001. "The practice of central bank intervention: looking under the hood," Review, Federal Reserve Bank of St. Louis, issue May, pages 1-10.
    4. Christopher J. Neely, 2005. "An analysis of recent studies of the effect of foreign exchange intervention," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 685-718.
    5. Artis, M. J. & Zhang, W., 1990. "BVAR forecasts for the G-7," International Journal of Forecasting, Elsevier, vol. 6(3), pages 349-362, October.
    6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
    2. Niyati Bhanja & Arif Billah Dar & Aviral Kumar Tiwari, 2015. "Exchange Rate and Monetary Fundamentals: Long Run Relationship Revisited," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 62(1), pages 33-54, March.
    3. Somesh Kumar Mathur & Surendra Babu, 2014. "Modelling & Forecasting of Re/$ Exchange rate – An empirical analysis," 2nd International Conference on Energy, Regional Integration and Socio-Economic Development 7741, EcoMod.

    More about this item

    Keywords

    exchange rate; monetary model; VAR and Bayesian VAR models;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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