Integrated data-driven analytics to identify instability signatures in nonstationary financial time series

With the objective of identifying instability signatures of the financial system, this article integrates two classes of data-driven techniques. The first class of techniques is utilized to investigate macroeconomic behaviour by aggregating an ensemble of heterogeneous nonstationary time-series data and the second class of techniques examines the local dynamics of the microstructures in each time series. Moving window principal component analysis (PCA) and functional PCA (fPCA) are shown to extract collective signatures of the financial system for understanding macroeconomic behaviour, and the Synchrosqueezing and Markov switching techniques are used to study local dynamics within each individual time series. The integrated data analytics successfully identifies the diverse events from 1986 to 2012. All events, both major and minor, have been identified by fPCA. The major economic events, especially the 2008 Great Recession, along with several minor events, showed a strong leading indicator in the density index derived from Synchrosqueezing. The capability of this integrated analytics suite is demonstrated in this article, and it motivates further studies encompassing data sets from broader sectors. As a complement to existing model-driven approach, this would lead to achieving a robust and reliable method that can help in taking measures to avoid catastrophic collapse in the constantly evolving financial system.