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Multivariate GARCH for a large number of stocks

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  • Raddant, Matthias
  • Wagner, Friedrich

Abstract

The problems related to the application of multivariate GARCH models to a market with a large number of stocks are solved by restricting the form of the conditional covariance matrix. It contains one component describing the market and a second simple component to account for the remaining contribution to the volatility. This allows the analytical calculation of the inverse covariance matrix. We compare our model with the results of other GARCH models for the daily returns from the S&P500 market. The description of the covariance matrix turns out to be similar to the DCC model but has fewer free parameters and requires less computing time. As applications we use the daily values of beta coefficients available from the market component to confirm a transition of the market in 2006. Further we discuss properties of the leverage effect.

Suggested Citation

  • Raddant, Matthias & Wagner, Friedrich, 2016. "Multivariate GARCH for a large number of stocks," Kiel Working Papers 2049, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:2049
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Multivarite GARCH models; CAPM; market risk;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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