Monitoring the mean of multivariate financial time series
Article first published online: 25 APR 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry
Volume 30, Issue 3, pages 328–340, May/June 2014
How to Cite
2014), Monitoring the mean of multivariate financial time series, Applied Stochastic Models in Business and Industry, 30, pages 328–340. DOI: 10.1002/asmb.1980and (
- Issue published online: 13 JUN 2014
- Article first published online: 25 APR 2013
- Manuscript Accepted: 17 MAR 2013
- Manuscript Revised: 6 OCT 2012
- Manuscript Received: 13 JUL 2011
- Dynamic conditional correlation model;
- multivariate CUSUM charts;
- multivariate EWMA charts
Timely detection of changes in the mean vector of multivariate financial time series is of great practical importance. In this paper, the covariance dynamics of the multivariate stochastic processes is assessed by either the RiskMetrics approach, the constant conditional correlation, or the dynamic conditional correlation models. For online monitoring of mean changes, we introduce several control schemes based on exponential smoothing and cumulative sums, which explicitly account for heteroscedasticity. The detecting ability of the introduced charts is compared for different processes in a Monte Carlo simulation study. The empirical study illustrates monitoring of changes in the mean vector of daily returns of exchange rates. Copyright © 2013 John Wiley & Sons, Ltd.