Abstract
A general approach to measure statistical uncertainty of different filtration techniques for market network analysis is proposed. Two measures of statistical uncertainty are introduced and discussed. One is based on conditional risk for multiple decision statistical procedures and another one is based on average fraction of errors. It is shown that for some important cases the second measure is a particular case of the first one. The proposed approach is illustrated by numerical evaluation of statistical uncertainty for popular network structures (minimum spanning tree, planar maximally filtered graph, market graph, maximum cliques and maximum independent sets) in the framework of Gaussian network model of stock market.
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