Exploring the economic rationale of extremes in GARCH generated betas The case of U.S. banks

The estimation of time varying beta is an important and growing area of research. The Multivariate GARCH model has been used in the literature to generate estimates of time varying betas. A common feature of the time varying risk estimates generated by this approach, is that they exhibit large outliers. In this paper, we investigate the incidence of such extreme beta observations in order to establish whether they are a response by the market to the arrival of news or alternatively are a result of the model picking up noise from the mean. Using daily data for a sample of U.S. deposit taking institutions over the period 1976 to 1994, this paper uses a Multivariate GARCH model to generate conditional beta estimates. The presence of large outliers is established and investigated. Generally, the results of this study suggest that these extreme observations are economically induced.