Speaker
Karsten Schweikert
Description
We propose a continuous-time framework to estimate time-invariant information shares. For this purpose, we employ a cointegrated CAR(k) process which accounts for the degree of temporal aggregation in the observable time series and avoids the many lags needed to specify discrete-time models for high-frequency data. We estimate the parameters by maximizing the Gaussian likelihood and investigate the statistical properties of the continuous-time information shares under mixed in-fill and long span asymptotics. We use simulations and an empirical application to show the benefits of continuous-time modeling for the analysis of price discovery in fragmented markets.