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Asymmetric Conditional Volatility Models: Empirical Estimation and Comparison of Forecasting Accuracy


  • Miron, Dumitru

    () (International Business and Economics Department, Academy of Economic Studies from Bucharest, Romania)

  • Tudor, Cristiana

    () (International Business and Economics Department, Academy of Economic Studies from Bucharest, Romania)


This paper compares several statistical models for daily stock return volatility in terms of sample fit and out-of-sample forecast ability. The focus is on U.S. and Romanian daily stock return data corresponding to the 2002-2010 time interval. We investigate the presence of leverage effects in empirical time series and estimate different asymmetric GARCH-family models (EGACH, PGARCH and TGARCH) specifying successively a Normal, Student's t and GED error distribution. We find that GARCH family models with normal errors are not capable to capture fully the leptokurtosis in empirical time series, while GED and Student’s t errors provide a better description for the conditional volatility. In addition, we outline some stylized facts about volatility that are not captured by conventional ARCH or GARCH models, but are considered by the asymmetric models and document their presence in empirical time series. Finally, we report that volatility estimates given by the EGARCH model exhibit generally lower forecast errors and are therefore more accurate than the estimates given by the other asymmetric GARCH models.

Suggested Citation

  • 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.
  • Handle: RePEc:rjr:romjef:v::y:2010:i:3:p:

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    Cited by:

    1. OPREANA Claudiu & BRATIAN Vasile, 2012. "Modeling Of Volatility In The Romanian Capital Market," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 7(3), pages 113-128, December.
    2. Iorember, Paul & Sokpo, Joseph & Usar, Terzungwe, 2017. "Inflation and Stock Market Returns Volatility: Evidence from the Nigerian Stock Exchange 1995Q1-2016Q4: An E-GARCH Approach," MPRA Paper 85656, University Library of Munich, Germany.
    3. N. Chitra Devi & S. Chandramohan, 2016. "Asymmetric relationship between stock market returns and macroeconomic variables," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(2), pages 79-94.
    4. repec:vls:finstu:v:22:y:2018:i:1:p:64-86 is not listed on IDEAS
    5. Acatrinei, Marius & Gorun, Adrian & Marcu, Nicu, 2013. "A DCC-GARCH Model To Estimate the Risk to the Capital Market in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 136-148, March.
    6. repec:eco:journ1:2018-02-38 is not listed on IDEAS
    7. repec:rjr:romjef:v::y:2017:i:3:p:37-53 is not listed on IDEAS
    8. Cristiana Tudor, 2011. "Changes in Stock Markets Interdependencies as a Result of the Global Financial Crisis: Empirical Investigation on the CEE Region," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 58(4), pages 525-543, December.

    More about this item


    stylized facts; leverage effects; asymmetric GARCH; volatility modeling; volatility forecasting;

    JEL classification:

    • 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


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