Operators on Inhomogeneous Time Series

Posted: 3 Mar 2000

See all articles by Gilles O. Zumbach

Gilles O. Zumbach

affiliation not provided to SSRN; Edgelab

Ulrich A. Müller

Olsen & Associates

Multiple version iconThere are 2 versions of this paper


We present a toolbox to compute and extract information from inhomogeneous (i. e. unequally spaced) time series.The toolbox contains a large set of operators, mapping from the space of inhomogeneous time series to itself.

These operators are computationally efficient (time and memory-wise) and suitable for stochastic processes. This makes them attractive for processing high-frequency data in finance and other fields. Using a basic set of operators, we easily construct more powerful combined operators which cover a wide set of typical applications.

The operators are classified in macroscopic operators (that have a limit value when the sampling frequency goes to infinity)and microscopic operators (that strongly depend on the actual sampling). For inhomogeneous data, macroscopic operators are more robust and more important. Examples of macroscopic operators are (exponential) moving averages, differentials, derivatives, moving volatilities, etc....

JEL Classification: C14, C52, C63, C80, G10

Suggested Citation

Zumbach, Gilles and Müller, Ulrich A., Operators on Inhomogeneous Time Series. Available at SSRN: https://ssrn.com/abstract=208528

Gilles Zumbach

affiliation not provided to SSRN

Edgelab ( email )

Avenue de la Rasude 5
Lausanne, 1006
+41764351267 (Phone)

Ulrich A. Müller (Contact Author)

Olsen & Associates ( email )

Seefeldstrasse 233
CH-8008 Zurich
+41 (1) 386 48 16 (Phone)
+41 (1) 422 22 82 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics