Detection of rare events in multidimensional financial datasets with zonoid depth functions

Parisa Golbayani, Dragos Bozdog

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

Abstract

The analysis of occurrence of rare events along with their significant negative economic impacts has been a challenging topic in recent years. In this study, we construct a framework around the development of new techniques that extracts information out of big datasets that spans the areas of data science and financial model analysis. In particular, we investigate methods to reduce the complexity of the analysis, map large amounts of data and construct a model to detect rare events on multivariate financial datasets. Zonoid depth functions are implemented and classification criteria based on convex hulls are constructed. The results identify rare events in high-frequency transaction level data and the distribution of these rare events for the period investigated indicates a significant higher concentration of rare events in the first trading hours of each trading day.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Pages1-6
Number of pages6
ISBN (Electronic)9781538627259
DOIs
StatePublished - 1 Jul 2017
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Country/TerritoryUnited States
CityHonolulu
Period27/11/171/12/17

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