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Using The Symmetric Models Garch (1.1) And Garch-M (1.1) To Investigate Volatility And Persistence For The European And Us Financial Markets


  • DUȚĂ, Violeta

    (Bucharest University of Economic Studies, Romania)


In this paper, we used the GARCH (1,1) and GARCH-M (1,1) models to investigate volatility and persistence at daily frequency for European and US financial markets. In the study we included fourteen stock indices (twelve Europeans and two Americans), during March 2013 - January 2017. The results of the GARCH (1.1) show that the models are correctly specified for most of the analysed series (except for the WIG30 index). The study found that the BET-BK index recorded the lower persistence of volatility, meaning that the conditional volatility tends to revert faster to the long-term mean than the other stock indices analysed. In the case of the GARCH-M (1.1) model, the variance coefficient in the mean equation was statistically significant and positive (thus confirming the hypothesis that an increase in volatility leads an increase in future returns), only for six of the analysed series. The strongest relationship was recorded for the US index, S&P500. It is also recorded for the Romanian stock indices: BET and BET-BK. For the BET index, the conclusions are in line with the results of previous studies.

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  • DUȚĂ, Violeta, 2018. "Using The Symmetric Models Garch (1.1) And Garch-M (1.1) To Investigate Volatility And Persistence For The European And Us Financial Markets," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 22(1), pages 64-86.
  • Handle: RePEc:vls:finstu:v:22:y:2018:i:1:p:64-86

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    References listed on IDEAS

    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Radu Lupu & Iulia Lupu, 2007. "Testing for Heteroskedasticity on the Bucharest Stock Exchange," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 10(23), pages 19-28, June.
    3. Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(1), pages 33-55, March.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    6. Iulian PANAIT & Ecaterina Oana SLĂVESCU, 2012. "Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(5(570)), pages 55-76, May.
    7. Bekaert, Geert & Harvey, Campbell R., 1997. "Emerging equity market volatility," Journal of Financial Economics, Elsevier, vol. 43(1), pages 29-77, January.
    8. Iulian Panait, 2011. "Study of the Correlation between the Romanian Stock Market and S&P500 Index during 2007-2009," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 14(39), pages 233-255, March.
    9. Miron, Dumitru & Tudor, Cristiana, 2010. "Asymmetric Conditional Volatility Models: Empirical Estimation and Comparison of Forecasting Accuracy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), September.
    10. 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.
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    More about this item


    stock market; volatility clustering; volatility persistence;
    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
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation


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