Multivariate Garch with dynamic beta
Matthias Raddant and
Papers from arXiv.org
We present a solution for the problems related to the application of multivariate Garch models to markets with a large number of stocks by restricting the form of the 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. The model also has the advantage that it contains the calculation of dynamic beta values. 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 the relationship of our model with the leverage effect.
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Date: 2016-09, Revised 2019-08
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1609.07051
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