Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility

We introduce a multivariate GARCH model that utilizes and models realized measures of volatility and covolatility. The realized measures extract information contained in high-frequency data that is particularly beneficial during periods with variation in volatility and covolatility. Applying the model to market returns in conjunction with an individual asset yields a model for the conditional regression coefficient, known as the beta. We apply the model to a set of highly liquid stocks and find that conditional betas are much more variable than usually observed with rolling-window OLS regressions with dailty data. In the empirical part of the paper we examine the cross-sectional as well as the time variation of the conditional beta series. The model links the conditional and realized second moment measures in a self-contained system of equations, making it amenable to extensions and easy to estimate. A multi-factor extension of the model is briefly discussed.