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Financial Integration from a Time-Varying Cointegration Perspective

Author

Listed:
  • Xianguo Huang

    (National Graduate Institute for Policy Studies)

  • Roberto Leon-Gonzalez

    (National Graduate Institute for Policy Studies)

  • Somrasri Yupho

    (National Graduate Institute for Policy Studies)

Abstract

This paper applies a time-varying cointegration (TVC) model to study regional financial integration, measured by the drifting cointegration coefficient of the long-term interest rates between Singapore and Malaysia. Conditioned on long-run exchange rate equilibrium, the evolving relation can be used to test the hypothesis of uncovered interest parity (UIP) in the strong and weak forms, and examine how the integration changes over time on the basis of the long-term interest rates measure. In the case of Singapore and Malaysia, the findings show that financial integration first decreased after the 1997 Asian Financial Crisis and then enhanced gradually from late 2001 onward. The shocks to Singapore, characterized by a higher level and a leading effect, are positively correlated with the ones to Malaysia.

Suggested Citation

  • Xianguo Huang & Roberto Leon-Gonzalez & Somrasri Yupho, 2012. "Financial Integration from a Time-Varying Cointegration Perspective," GRIPS Discussion Papers 12-07, National Graduate Institute for Policy Studies.
    • Handle: RePEc:ngi:dpaper:12-07
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    References listed on IDEAS

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    1. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2011. "Bayesian inference in a time varying cointegration model," Journal of Econometrics, Elsevier, vol. 165(2), pages 210-220.
    2. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
    3. Feldstein, Martin & Horioka, Charles, 1980. "Domestic Saving and International Capital Flows," Economic Journal, Royal Economic Society, vol. 90(358), pages 314-329, June.
    4. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    5. Bierens, Herman J. & Martins, Luis F., 2010. "Time-Varying Cointegration," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1453-1490, October.
    6. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    7. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
    8. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    9. Rossella Calvi, 2010. "Assessing financial integration: a comparison between Europe and East Asia," European Economy - Economic Papers 2008 - 2015 423, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
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