An Analysis of Asynchronous Data

Risk analysis and financial decision making requires true and appropriate estimates of correlations today and how they are expected to evolve in the future. If a portfolio consists of assets traded in markets with different trading hours, there could potentially occur an underestimation of the right correlation. This is due the asynchronous data - there exist an asynchronicity within the assets time series in the portfolio. The purpose of this paper is twofold. First, we suggest a modified synchronization model of Burns, Engle and Mezrich (1998) which replaces the first-order vector moving average with an first-order vector autoregressive process. Second, we study the time-varying dynamics along with forecasting the conditional variance-covariance and correlation through a DCC model. The performance of the DCC model is compared to the industrial standard RiskMetrics Exponentially Weighted Moving Averages (EWMA) model. The analysis shows that the covariance of the DCC model is slightly lower than of the RiskmMetrics EWMA model. Our conclusion is that the DCC model is simple and powerful and therefore a promising tool. It provides good insight into how correlations are likely to evolve in the short-run time horizon.