Statistical signatures in times of panic: markets as a self-organizing system

Lisa Borland

Quantitative Finance, 2012, vol. 12, issue 9, 1367-1379

Abstract: We study properties of the cross-sectional distribution of returns. A significant anti-correlation between dispersion and cross-sectional kurtosis is found such that dispersion is high but kurtosis is low in panic times, and the opposite in normal times. The co-movement of stock returns also increases in panic times. We define a simple statistic s , the normalized sum of signs of returns on a given day, to capture the degree of correlation in the system. s can be seen as the order parameter of the system because if s = 0 there is no correlation (a disordered state), whereas for s ≠ 0 there is correlation among stocks (an ordered state). We make an analogy to non-equilibrium phase transitions and hypothesize that financial markets undergo self-organization when the external volatility perception rises above some critical value. Indeed, the distribution of s is unimodal in normal times, shifting to bimodal in times of panic. This is consistent with a second-order phase transition. Simulations of a joint stochastic process for stocks use a multi-timescale process in the temporal direction and an equation for the order parameter s for the dynamics of the cross-sectional correlation. Numerical results show good qualitative agreement with the stylized facts of real data, in both normal and panic times.

Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2011.653388 (text/html)
Access to full text is restricted to subscribers.

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:taf:quantf:v:12:y:2012:i:9:p:1367-1379

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2011.653388

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().