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In a variety of different settings cumulative sum (CUSUM) procedures havebeen applied for the sequential detection of structural breaks in theparameters of stochastic models. Yet their performance depends strongly on thetime of change and is best under early-change scenarios. For later changestheir finite sample behavior is rather questionable. We therefore proposemodified CUSUM procedures for the detection of abrupt changes in the regressionparameter of multiple time series regression models, that show a higherstability with respect to the time of change than ordinary CUSUM procedures.The asymptotic distributions of the test statistics and the consistency of theprocedures are provided. In a simulation study it is shown that the proposedprocedures behave well in finite samples. Finally the procedures are applied toa set of capital asset pricing data related to the Fama-French extension of thecapital asset pricing model.

Page's Sequential Procedure for Change-Point Detection in Time Series Regression