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Monitoring the mean of multivariate financial time series

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  • Robert Garthoff
  • Vasyl Golosnoy
  • Wolfgang Schmid

Abstract

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.

Suggested Citation

  • Robert Garthoff & Vasyl Golosnoy & Wolfgang Schmid, 2014. "Monitoring the mean of multivariate financial time series," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(3), pages 328-340, May.
    • Handle: RePEc:wly:apsmbi:v:30:y:2014:i:3:p:328-340
    DOI: 10.1002/asmb.1980
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