Tracing the temporal evolution of clusters in a financial stock market
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies. We illustrate this graph representation of the evolution of clusters in time and its use on real data from the Madrid Stock Exchange market.
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- Otranto, Edoardo, 2008.
"Clustering heteroskedastic time series by model-based procedures,"
Computational Statistics & Data Analysis,
Elsevier, vol. 52(10), pages 4685-4698, June.
- E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
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