Clustering of discretely observed diffusion processes
Alessandro De Gregorio, Università di Milano, Italy
Stefano Iacus, Department of Economics, Business and Statistics, University of Milan, IT
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ABSTRACT:
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.
SUGGESTED CITATION:
Alessandro De Gregorio and Stefano Iacus,
"Clustering of discretely observed diffusion processes"
(September 2008).
UNIMI - Research Papers in Economics, Business, and Statistics.
Statistics and Mathematics.
Working Paper 39.
http://services.bepress.com/unimi/statistics/art39
Paper presented by C. Tommasi.
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