IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v9y2009i1p71-91.html
   My bibliography  Save this article

Efficient factor GARCH models and factor-DCC models

Author

Listed:
  • Kun Zhang
  • Laiwan Chan

Abstract

We report that, in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on this, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; factors in best-factor GARCH have the largest autocorrelation in their squared values such that their volatilities could be forecast well by univariate GARCH; and factors in conditional-decorrelation GARCH are conditionally as uncorrelated as possible. A convenient two-step method for estimating these models is introduced. Since the extracted factors may still have weak conditional correlations, we further propose factor-DCC models as an extension to the above factor GARCH models with dynamic conditional correlation (DCC) modelling the remaining conditional correlations between factors. Experimental results for the Hong Kong stock market show that conditional-decorrelation GARCH and independent-factor GARCH have better generalization performance than the original GO-GARCH, and that conditional-decorrelation GARCH (among factor GARCH models) and its extension with DCC embedded (among factor-DCC models) behave best.

Suggested Citation

  • Kun Zhang & Laiwan Chan, 2009. "Efficient factor GARCH models and factor-DCC models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 71-91.
    • Handle: RePEc:taf:quantf:v:9:y:2009:i:1:p:71-91
    DOI: 10.1080/14697680802039840
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/14697680802039840
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697680802039840?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jeroen Rombouts & Marno Verbeek, 2009. "Evaluating portfolio Value-at-Risk using semi-parametric GARCH models," Quantitative Finance, Taylor & Francis Journals, vol. 9(6), pages 737-745.
    2. Andreia Dionisio & Rui Menezes & Diana A. Mendes, 2003. "Mutual information: a dependence measure for nonlinear time series," Econometrics 0311003, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    2. E. M. S. Ribeiro & G. A. Prataviera, 2014. "Information theoretic approach for accounting classification," Papers 1401.2954, arXiv.org, revised Sep 2014.
    3. Moralles, Herick Fernando & do Nascimento Rebelatto, Daisy Aparecida, 2016. "The effects and time lags of R&D spillovers in Brazil," Technology in Society, Elsevier, vol. 47(C), pages 148-155.
    4. Witold Orzeszko, 2010. "Measuring Nonlinear Serial Dependencies Using the Mutual Information Coefficient," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 97-106.
    5. Pei Pei, 2010. "Backtesting Portfolio Value-at-Risk with Estimated Portfolio Weights," Caepr Working Papers 2010-010, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    6. Akimitsu Inoue, 2016. "Density estimation based on pointwise mutual information," Economics Bulletin, AccessEcon, vol. 36(2), pages 1138-1148.
    7. Cristi Spulbar & Ramona Birau & Iqbal Thonse Hawaldar & Jatin Trivedi & Anca Ioana Iacob (Troto), 2023. "Measuring Asymmetric Volatility Of Uk, France, And German Stock Markets," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 134-146, February.
    8. Lumengo BONGA-BONGA & Lebogang NLEYA, 2018. "Assessing Portfolio Market Risk in the BRICS Economies: Use of Multivariate GARCH Models," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 71(2), pages 87-128.
    9. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    10. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
    11. He, Kaijian & Wang, Lijun & Zou, Yingchao & Lai, Kin Keung, 2014. "Value at risk estimation with entropy-based wavelet analysis in exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 62-71.
    12. Francq, Christian & Zakoïan, Jean-Michel, 2020. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Journal of Econometrics, Elsevier, vol. 217(2), pages 356-380.
    13. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, vol. 5(4), pages 1-26, April.
    14. Wei Kuang, 2024. "High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-35, May.
    15. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas P�rez-I�igo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi.
    16. Andreia Teixeira Marques Dionísio, 2010. "On the globalization of stock markets: An application of VECM, SSA technique and mutual information to the G7?," CEFAGE-UE Working Papers 2010_06, University of Evora, CEFAGE-UE (Portugal).
    17. Chunxia, Yang & Xueshuai, Zhu & Luoluo, Jiang & Sen, Hu & He, Li, 2016. "Study on the contagion among American industries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 601-612.
    18. Ribeiro, E.M.S. & Prataviera, G.A., 2014. "Information theoretic approach for accounting classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 651-660.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:taf:quantf:v:9:y:2009:i:1:p:71-91. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.