This paper compares the methods for variability extraction from a univariate time series in real time. The online scale estimation is achieved by applying a robust scale functional to a moving time window. Scale estimators based on the residuals of a preceding regression step are compared with regression-free and model-free techniques in a simulation study and in an application to a real time series. In the presence of level shifts or strong non-linear trends in the signal level, the model-free scale estimators perform especially well. However, the investigated regression-free and regression-based methods have higher breakdown points, they are applicable to data containing temporal correlations, and they are much more efficient.

Additional Metadata
Keywords real-time estimation, robustness, time series, variability, volatility
Persistent URL dx.doi.org/10.1080/00949650902911565, hdl.handle.net/1765/19654
Note Accepted manuscript, First Published on: 23 October 2009
Citation
Schettlinger, K., Gelper, S.E.C., Gather, U., & Croux, C.. (2010). Regression-based, regression-free and model-free approaches for robust online scale estimation. Journal of Statistical Computation and Simulation, 80(9), 1023–1040. doi:10.1080/00949650902911565