Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published May 1, 2018 | Submitted
Journal Article Open

Time series analysis of S&P 500 index: A horizontal visibility graph approach

Abstract

The behavior of stock prices has been thoroughly studied throughout the last century, and contradictory results have been reported in the corresponding literature. In this paper, a network theoretical approach is provided to investigate how crises affected the behavior of US stock prices. We analyze high frequency data from S&P500 via the Horizontal Visibility Graph method, and find that all major crises that took place worldwide in the last twenty years, affected significantly the behavior of the price-index. Nevertheless, we observe that each of those crises impacted the index in a different way and magnitude. Interestingly, our results suggest that the predictability of the price-index series increases during the periods of crises.

Additional Information

© 2018 Elsevier B.V. Received 4 September 2017, Revised 1 December 2017, Available online 10 January 2018. This paper benefited from the comments of participants at the 2016 Financial Risk & Network Theory seminar, Cambridge, UK, the 3rd Quantitative Finance and Risk Analysis (QFRA2017) Symposium, Corfu, Greece and the 13th Econophysics colloquium & 9th Polish Symposium on Physics in Economy and Social Sciences (EC & FENS 2017), Warsaw, Poland as well seminar talks in the University of Liverpool (UK) and Shanghai University (China). Warm thanks are due to our EPSRC/ESRC CDT industrial and academic partners, Dr Kimmo Soramäki (FNA, UK) and Dr Eugene Neduv (Columbia University, US) who commented on a preliminary version of our paper and who have afforded us considerable assistance in enhancing both the quality of the findings and the clarity of their presentation. Any remaining errors are ours.

Attached Files

Submitted - SSRN-id3031781.pdf

Files

SSRN-id3031781.pdf
Files (690.1 kB)
Name Size Download all
md5:e288dd3264870ece4b5157c0d71314f6
690.1 kB Preview Download

Additional details

Created:
August 21, 2023
Modified:
October 18, 2023