01325nas a2200133   4500000000100000000000100001008004100002260001200043100002100055245007600076490000800152520101700160022001401177       2020                            d  c07/20201 aKrzysztof Domino00aMultivariate cumulants in outlier detection for financial data analysis0 v5583 a<p>There are many research papers yielding the financial data models, where returns are tied either to the fundamental analysis or to the individual, often irrational, behaviour of investors. In the second case the bubble followed by the crisis is possible on the market. Such bubble or crisis is reflected by the cross-correlated extreme positive or negative returns of many assets. Such returns are modelled by the copula with the meaningful tail dependencies. The typical model of such cross-correlation provides the t-Student copula. The author demonstrates that the mutual information tied to this copula can be measured by the 4th order multivariate cumulants. Tested on the artificial data, the 4th order multivariate cumulant approach was used successfully for the financial crisis detection. For this end the author introduces the outliers detection algorithm. In addition this algorithm displays the potential application for the crisis prediction, complementary to the auto-correlation analysis.</p>
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