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Interest Rate Modeling and Forecasting in India

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Author Info

  • Pami Dua

    (Department of Economics, Delhi School of Economics, Delhi, India and Economic Cycle Research Institute, New York)

  • Nishita Raje

    (Department of Economic Analysis and Policy, Reserve Bank of India)

  • Satyananda Sahoo

    (Department of Economic Analysis and Policy, Reserve Bank of India)

Abstract

The study develops univariate (ARIMA and ARCH/GARCH) and multivariate models (VAR, VECM and Bayesian VAR) to forecast short- and long-term rates, viz., call money rate, 15-91 days Treasury Bill rates and interest rates on Government securities with (residual) maturities of one year, five years and ten years. Multivariate models consider factors such as liquidity, Bank Rate, repo rate, yield spread, inflation, credit, foreign interest rates and forward premium. The study finds that multivariate models generally outperform univariate ones over longer forecast horizons. Overall, the study concludes that the forecasting performance of Bayesian VAR models is satisfactory for most interest rates and their superiority in performance is marked at longer forecast horizons.

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Bibliographic Info

Paper provided by Centre for Development Economics, Delhi School of Economics in its series Occasional papers with number 3.

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Length: 84 pages
Date of creation: Jul 2004
Date of revision:
Handle: RePEc:cde:occpap:3

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Citations

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Cited by:
  1. Dilip Nachane & Jose Clavel, 2008. "Forecasting interest rates: a comparative assessment of some second-generation nonlinear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 493-514.
  2. Haroon Mumtaz & Nitin Kumar, 2012. "An application of data-rich environment for policy analysis of the Indian economy," Joint Research Papers 2, Centre for Central Banking Studies, Bank of England.
  3. Jayaraman, T. K. & Choong, Chee-Keong, 2011. "Impact of global growth fluctuations on India: an empirical study," MPRA Paper 33685, University Library of Munich, Germany.

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