The reconstruction of financial signals using fast ICA for systemic risk

Kuan Heng Chen, Khaldoun Khashanah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Independent component analysis (ICA) is a statistical method for transforming multidimensional observed signals into components, which are statistically independent from each other, which is a case of redundancy reduction. In this paper, we implement Fast ICA proposed by Hyvarinen and Oja to investigate the relationship between systemic risk and ICA in the US financial market. We propose a systemic risk indicator based on observing the redundancy level of signals in running 10 variables including 10 S&P 500 sector indices. We find that not only the redundancy level of signals becomes larger during a crisis than during a normal period, but also the financial system becomes more vulnerable when the redundancy level grows up.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
Pages885-889
Number of pages5
ISBN (Electronic)9781479975600
DOIs
StatePublished - 2015
EventIEEE Symposium Series on Computational Intelligence, SSCI 2015 - Cape Town, South Africa
Duration: 8 Dec 201510 Dec 2015

Publication series

NameProceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015

Conference

ConferenceIEEE Symposium Series on Computational Intelligence, SSCI 2015
Country/TerritorySouth Africa
CityCape Town
Period8/12/1510/12/15

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