Non-Gaussian Time Series and Nonlinear Dependence in Finance Markets, 2016

Two key statistical features of finance markets are non-linearity and Non-Gaussianity. Between 2011 and 2012, Dag Tjøstheim coordinated the research of the project "Non-Guassian Time Series and Nonlinear Dependence", financed by The Finance Market Fund. The activity of the project was spread over three sub-projects, namely i) Local Gaussian correlation, ii) Nonlinear cointegration and iii) Integer-valued time series. In the referee report for the project it was pointed out that i) was most relevant for financial markets. Therefore the focus was directed towards this in the project "Non-Gaussian Time Series and Nonlinear Dependence in Finance Markets, 2016", for which metadata is presented here. Topics from i) that received attention were multivariate heavy tail dependence, multivariate extreme events, and multivariate portfolio analysis and risk. In addition we considered nonlinear cointegration theory using local Gaussian correlation, this combining i) and ii). Finally we explored the relationships to multiple copula theory, in particular the vine theory. All of this was relevant for the statistical analysis of financial markets.