Clustering of discretely observed diffusion processes
AbstractIn this paper a new dissimilarity measure to identify groups of assets dynamics is proposed. The underlying generating process is assumed to be a diffusion process solution of stochastic differential equations and observed at discrete time. The mesh of observations is not required to shrink to zero. As distance between two observed paths, the quadratic distance of the corresponding estimated Markov operators is considered. Analysis of both synthetic data and real financial data from NYSE/NASDAQ stocks, give evidence that this distance seems capable to catch differences in both the drift and diffusion coefficients contrary to other commonly used metrics.
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Bibliographic InfoPaper provided by Universitá degli Studi di Milano in its series UNIMI - Research Papers in Economics, Business, and Statistics with number unimi-1077.
Date of creation: 18 Sep 2008
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Clustering of time series; discretely observed diffusion processes; financial assets; markov processes;
Other versions of this item:
- De Gregorio, Alessandro & Maria Iacus, Stefano, 2010. "Clustering of discretely observed diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 598-606, February.
- Alessandro De Gregorio & Stefano Maria Iacus, 2008. "Clustering of discretely observed diffusion processes," Papers 0809.3902, arXiv.org.
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