Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model

We model high-frequency trading processes by a multivariate multiplicative error model that is driven by component-specific observation driven dynamics as well as a common latent autoregressive factor. The model is estimated using efficient importance sampling techniques. Applying the model to 5Â min return volatilities, trade sizes and trading intensities from four liquid stocks traded at the NYSE, we show that a subordinated common process drives the individual components and captures a substantial part of the dynamics and cross-dependencies of the variables. Common shocks mainly affect the return volatility and the trade size. Moreover, we identify effects that capture rather genuine relationships between the individual trading variables.