Analysis of non-stationary dynamics in the financial system
Samar K. Guharay,
Gaurav S. Thakur,
Fred J. Goodman,
Scott L. Rosen and
Daniel Houser ()
Economics Letters, 2013, vol. 121, issue 3, pages 454-457
Novel data-driven analyses, appropriate for detecting economic instability in non-stationary time series, are developed using functional principal component analysis (fPCA) and Synchrosqueezing. fPCA is applied in a new way, aggregating multiple financial time series to identify periods of macroeconomic instability. Synchrosqueezing, a technique which generates a time-series’ time-dependent spectral decomposition, is modified to develop a new quantitative measure of local dynamical changes and structural breaks. The merit of this integrated technique is demonstrated by analyzing financial data from 1986 to 2012 that includes equity indices, securities and commodities, and foreign exchange. Both procedures successfully detect key historic periods of instability. Moreover, the results reveal distinctions between periods of long-term gradual change in addition to structural breaks. These tools offer new insights into the analysis of financial instability.
Keywords: Non-stationary time series; Functional PCA; Synchrosqueezing; Multi-time scale characteristics; Detection of macroeconomic instability (search for similar items in EconPapers)
JEL-codes: G01 C58 C32 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:eee:ecolet:v:121:y:2013:i:3:p:454-457
Access Statistics for this article
Economics Letters is currently edited by Economics Letters Editorial Office
More articles in Economics Letters from Elsevier
Series data maintained by Dana Niculescu ().