Clustering of discretely observed diffusion processes
Alessandro De Gregorio and
Stefano Iacus ()
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Alessandro De Gregorio: Università di Milano, Italy
No unimi-1077, UNIMI - Research Papers in Economics, Business, and Statistics from Universitá degli Studi di Milano
In 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.
Keywords: Clustering of time series; discretely observed diffusion processes, financial assets, markov processes, (search for similar items in EconPapers)
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Journal Article: Clustering of discretely observed diffusion processes (2010)
Working Paper: Clustering of discretely observed diffusion processes (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:bep:unimip:unimi-1077
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