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Measuring asymmetry and persistence in conditional volatility in real output: evidence from three East Asian tigers using a multivariate GARCH approach

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  • Vu Thanh Hai
  • Albert K. Tsui
  • Zhaoyong Zhang

Abstract

We search for evidence of conditional volatility in the quarterly real Gross Domestic Product (GDP) growth rates of three East Asian tigers: Singapore, Hong Kong and Taiwan. The widely accepted Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH)-type model is used to capture the existence of asymmetric volatility and the potential structural break points in the volatility. We find evidence of asymmetry and persistence in the volatility of GDP growth rates. It is noted that the structural breakpoints of volatility correspond reasonably well to the historical economic and political events in these economies. Policy implications from our findings are discussed.

Suggested Citation

  • Vu Thanh Hai & Albert K. Tsui & Zhaoyong Zhang, 2013. "Measuring asymmetry and persistence in conditional volatility in real output: evidence from three East Asian tigers using a multivariate GARCH approach," Applied Economics, Taylor & Francis Journals, vol. 45(20), pages 2909-2914, July.
  • Handle: RePEc:taf:applec:v:45:y:2013:i:20:p:2909-2914
    DOI: 10.1080/00036846.2012.687098
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    References listed on IDEAS

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    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Ho, Kin-Yip & Tsui, Albert K. C., 2003. "Asymmetric volatility of real GDP: some evidence from Canada, Japan, the United Kingdom and the United States," Japan and the World Economy, Elsevier, vol. 15(4), pages 437-445, December.
    4. Allan D. Brunner, 1997. "On The Dynamic Properties Of Asymmetric Models Of Real GNP," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 321-352, May.
    5. Ho, Kin Yip & Tsui, Albert K.C., 2004. "Analysis of real GDP growth rates of greater China: An asymmetric conditional volatility approach," China Economic Review, Elsevier, vol. 15(4), pages 424-442.
    6. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    7. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    9. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November.
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    Cited by:

    1. Irene Brunetti & Davide fiaschi & Lisa Gianmoena, 2013. "An Index of Growth Rate Volatility: Methodology and an Application to European Regions," Discussion Papers 2013/169, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    2. Irene Brunetti & Davide Fiaschi & Lisa Gianmoena & Angela Parenti, 2017. "Volatility in European regions," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 697-720, November.
    3. Prakash L. Dheeriya & Fahimeh Rezayat & Burhan F. Yavas, 2014. "Relations between Volatility and Returns of Exchange Traded Funds of Emerging Markets and of USA," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 44-46, Feburary.
    4. Yavas, Burhan F. & Dedi, Lidija, 2016. "An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries," Research in International Business and Finance, Elsevier, vol. 37(C), pages 583-596.

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    More about this item

    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • P21 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Planning, Coordination, and Reform

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