IDEAS home Printed from https://ideas.repec.org/p/fau/wpaper/wp2008_18.html
   My bibliography  Save this paper

Value-at-Risk on Central and Eastern European Stock Markets: An Empirical Investigation Using GARCH Models

Author

Abstract

Using daily return data from the four major Central and Eastern European stock markets including fourteen highly liquid stocks and ATX (Vienna), PX (Prague), BUX (Budapest), and WIG20 (Warsaw) market indices, we model the value-at-risk using a set of univariate GARCH-type models. Our results show that, in both in-sample and out-of-sample value-at-risk estimations, the models based on asymmetric distribution of the error term tend to perform better or at least as well as the models based on symmetric distribution (i.e., Normal or Student) when the left tails of daily return distributions are concerned. Evaluation of the same models is less clear, however, when the right tails of the distribution of daily returns must be modelled. We suggest an asset-specific approach to selecting the correct parametric VaR model that depends not only on the risk level considered but also on the position in the underlying asset.

Suggested Citation

  • Vít Bubák, 2008. "Value-at-Risk on Central and Eastern European Stock Markets: An Empirical Investigation Using GARCH Models," Working Papers IES 2008/18, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2008.
  • Handle: RePEc:fau:wpaper:wp2008_18
    as

    Download full text from publisher

    File URL: http://ies.fsv.cuni.cz/default/file/download/id/8917
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.

    More about this item

    Keywords

    Value-at-Risk; Expected Shortfall; Backtesting;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fau:wpaper:wp2008_18. 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: Natalie Svarcova (email available below). General contact details of provider: https://edirc.repec.org/data/icunicz.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.