Transition drivers and crisis signaling in stock markets

Alessandro Spelta, Nicolò Pecora, Andrea Flori and Fabio Pammolli ()

MPRA Paper from University Library of Munich, Germany

Abstract: The present paper introduces an up-to-date methodology to detect Early Warning Signals of critical transitions, that manifest when distress stages in financial markets are about to take place. As a first step, we demonstrate that a high-dimensional dynamical system can be formulated in a simpler form but in an abstract phase space. Then we detect its approaching towards a critical transition by means of a set of observable variables that exhibit some particular statistical features. We name these variables the Leading Temporal Module. The impactful change in the properties of this group reflects the transition of the system from a normal to a distress state. Starting from these observations we develop an early warning indicator for determining the proximity of a financial crisis. The proposed measure is model free and the application to three different stock markets, together with the comparison with alternative systemic risk measures, highlights the usefulness in signaling upcoming distress phases. Computational results establish that the methodology we propose is effective and it may constitute a relevant decision support mechanism for macro prudential policies.

Keywords: Financial Crisis; Early Warning Signals; Critical Transition; Leading Temporal Module (search for similar items in EconPapers)
JEL-codes: C02 C53 E37 G01 G17 (search for similar items in EconPapers)
Date: 2018-07-23
New Economics Papers: this item is included in nep-fmk, nep-mac and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/88127/1/MPRA_paper_88127.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:88127

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().