Nonparametric dependence modeling via cluster analysis : a financial contagion application.
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Date
2019
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Abstract
Dependence measures, from linear correlation coefficients to recent
copula-based methods, have been widely used to find out associations between variables. Although the latter type of measure has
overcome many drawbacks of traditional measures, copula has intrinsically some undesirable characteristics for particular applications. In
this paper, we discuss dependence modeling from a pattern recognition perspective and then introduce a new non-parametric approach
based on anomaly detection through cluster analysis. The proposed
methodology uses a weighting procedure based on Voronoi cells
densities, named Weighted Voronoi Distance (WVD), to identify
potentially atypical associations between univariate time series. The
advantages are two-fold. First, the time series structure is respected
and neither independence nor homoscedasticity is presumed within
data. Second, any distribution of the data and any dependence function is allowed. An inference procedure is presented and simulation
studies help to visualize the behavior and benefits of the proposed
measure. Finally, real financial data is used to analyze the detection
capacity of the contagion effect in financial markets during the 2007
sub-prime crisis. Different asset classes were included, and the WVD
was able to signalize anomalies more strongly than the Extreme
Value Theory and copula approach.
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Keywords
Anomaly detection, Extreme Value Theory, Non-linear data structures, Scan statistics, Pattern recognition
Citation
COUTO, R. et al. Nonparametric dependence modeling via cluster analysis : a financial contagion application. Communications in Statistics - Simulation and Computation, p. 1-21, fev. 2019. Disponível em: <https://www.tandfonline.com/doi/abs/10.1080/03610918.2018.1563152?af=R&journalCode=lssp20>. Acesso em: 19 mar. 2019.