IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2010i3p.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

Abstract

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:
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef3_10/rjef3_10_4.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Urom, Christian & Onwuka, Kevin O. & Uma, Kalu E. & Yuni, Denis N., 2020. "Regime dependent effects and cyclical volatility spillover between crude oil price movements and stock returns," International Economics, Elsevier, vol. 161(C), pages 10-29.
    3. 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.
    4. Kumar Arya & Sahoo Jyotirmayee & Sahoo Jyotsnarani & Nanda Subhashree & Debyani Devi, 2024. "Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility," Folia Oeconomica Stetinensia, Sciendo, vol. 24(1), pages 105-123.
    5. Curtis Nybo, 2021. "Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks," Papers 2110.09489, arXiv.org.
    6. 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.
    7. 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.
    8. 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.
    9. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    10. Hasan, Md Abu, 2019. "Co-Movement and Volatility Transmission between Islamic and Conventional Equity Index in Bangladesh," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 26, pages 43-71.
    11. Charan Raj Chimrani & Farhan Ahmed & Vinesh Kumar Panjwani, 2018. "Modeling Sectoral Stock Indexes Volatility: Empirical Evidence from Pakistan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 8(2), pages 319-324.
    12. Krzysztof DRACHAL, 2017. "Volatility Clustering, Leverage Effects and Risk-Return Tradeoff in the Selected Stock Markets in the CEE Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-53, September.
    13. 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

    Keywords

    stylized facts; leverage effects; asymmetric GARCH; volatility modeling; volatility forecasting;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rjr:romjef:v::y:2010:i:3:p:. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.