IDEAS home Printed from https://ideas.repec.org/a/brf/journl/v10y2012i1p49-70.html
   My bibliography  Save this article

Volatility Estimation and Forecasting During Crisis Periods: A Study Comparing GARCH Models with Semiparametric Additive Models

Author

Listed:
  • Douglas Gomes dos Santos

    (Universidade Federal do Rio Grande do Sul (UFRGS))

  • Flávio Augusto Ziegelmann

    (UFRGS)

Abstract

In this paper, we compare semiparametric additive models with GARCH models in terms of their capability to estimate and forecast volatility during crisis periods. Our Monte Carlo studies indicate a better performance for GARCH models when their functional forms do not differ from that of the specified Data Generating Process (DGP). However, if they differ from the DGP, the results suggest the superiority of additive models. Additionally, we perform an empirical application in three selected periods of high volatility of IBOVESPA returns series, in which both families of models obtain similar results.

Suggested Citation

  • Douglas Gomes dos Santos & Flávio Augusto Ziegelmann, 2012. "Volatility Estimation and Forecasting During Crisis Periods: A Study Comparing GARCH Models with Semiparametric Additive Models," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(1), pages 49-70.
    • Handle: RePEc:brf:journl:v:10:y:2012:i:1:p:49-70
    as

    Download full text from publisher

    File URL: http://bibliotecadigital.fgv.br/ojs/index.php/rbfin/article/download/3321/2451
    Download Restriction: no
    File URL: http://bibliotecadigital.fgv.br/ojs/index.php/rbfin/article/view/3321
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    volatility; semiparametric additive models; GARCH models; crisis;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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

    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:brf:journl:v:10:y:2012:i:1:p:49-70. 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: Marcio Laurini (email available below). General contact details of provider: .

    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.