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A new method for jump detection: analysis of jumps in the S&P 500 financial index

Khaldoun Khashanah, Jing Chen Orcid Logo, Mike Buckle Orcid Logo, Alan Hawkes

Journal of the Royal Statistical Society Series C: Applied Statistics

Swansea University Authors: Mike Buckle Orcid Logo, Alan Hawkes

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DOI (Published version): 10.1093/jrsssc/qlaf025

Abstract

Financial jumps have occurred more frequently with the advent of high-frequency trading enabled by technological advancement. Most existing jump detection methods that treat a jump as a singular, random, and isolated shock event were not designed to capture the clustering of jumps related to contagi...

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Published in: Journal of the Royal Statistical Society Series C: Applied Statistics
ISSN: 0035-9254 1467-9876
Published: Oxford University Press (OUP) 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69118
first_indexed 2025-03-18T16:01:34Z
last_indexed 2025-08-01T14:31:09Z
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spelling 2025-07-31T17:27:26.1935436 v2 69118 2025-03-18 A new method for jump detection: analysis of jumps in the S&P 500 financial index 756422b319676d1b53856d85363a4ae3 0000-0002-5767-2217 Mike Buckle Mike Buckle true false 56dbf45233f1d80425924e81dc651635 Alan Hawkes Alan Hawkes true false 2025-03-18 CBAE Financial jumps have occurred more frequently with the advent of high-frequency trading enabled by technological advancement. Most existing jump detection methods that treat a jump as a singular, random, and isolated shock event were not designed to capture the clustering of jumps related to contagious behaviour, in which the occurrence of jumps increases the probability of further jumps soon after. This paper presents a new Med9 method that addresses the challenges of capturing both singular and consecutive jumps. This approach evaluates the size of individual returns with a measure of local volatility based on the median of consecutive absolute returns. We use this method to detect jumps in both S&P 500 and simulated time series, and compare its performance with several classic jump detection methods. Throughout, our Med9 consistently outperforms other approaches applied to both real and simulated financial return series. In addition, we demonstrate that the Med9 detection results are not biased or compromised by the intraday volatility pattern. Journal Article Journal of the Royal Statistical Society Series C: Applied Statistics 0 Oxford University Press (OUP) 0035-9254 1467-9876 contagion, financial series, jumps, S&amp;P 500 index, volatility 17 4 2025 2025-04-17 10.1093/jrsssc/qlaf025 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee 2025-07-31T17:27:26.1935436 2025-03-18T13:21:47.0465596 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Khaldoun Khashanah 1 Jing Chen 0000-0001-7135-2116 2 Mike Buckle 0000-0002-5767-2217 3 Alan Hawkes 4 69118__34127__c3039a75eeb84180b7ed66d225dbe213.pdf 69118.VoR.pdf 2025-04-28T13:39:35.2060038 Output 1047356 application/pdf Version of Record true © The Royal Statistical Society 2025. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng https:// creativecommons.org/licenses/by/4.0/
title A new method for jump detection: analysis of jumps in the S&P 500 financial index
spellingShingle A new method for jump detection: analysis of jumps in the S&P 500 financial index
Mike Buckle
Alan Hawkes
title_short A new method for jump detection: analysis of jumps in the S&P 500 financial index
title_full A new method for jump detection: analysis of jumps in the S&P 500 financial index
title_fullStr A new method for jump detection: analysis of jumps in the S&P 500 financial index
title_full_unstemmed A new method for jump detection: analysis of jumps in the S&P 500 financial index
title_sort A new method for jump detection: analysis of jumps in the S&P 500 financial index
author_id_str_mv 756422b319676d1b53856d85363a4ae3
56dbf45233f1d80425924e81dc651635
author_id_fullname_str_mv 756422b319676d1b53856d85363a4ae3_***_Mike Buckle
56dbf45233f1d80425924e81dc651635_***_Alan Hawkes
author Mike Buckle
Alan Hawkes
author2 Khaldoun Khashanah
Jing Chen
Mike Buckle
Alan Hawkes
format Journal article
container_title Journal of the Royal Statistical Society Series C: Applied Statistics
container_volume 0
publishDate 2025
institution Swansea University
issn 0035-9254
1467-9876
doi_str_mv 10.1093/jrsssc/qlaf025
publisher Oxford University Press (OUP)
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
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description Financial jumps have occurred more frequently with the advent of high-frequency trading enabled by technological advancement. Most existing jump detection methods that treat a jump as a singular, random, and isolated shock event were not designed to capture the clustering of jumps related to contagious behaviour, in which the occurrence of jumps increases the probability of further jumps soon after. This paper presents a new Med9 method that addresses the challenges of capturing both singular and consecutive jumps. This approach evaluates the size of individual returns with a measure of local volatility based on the median of consecutive absolute returns. We use this method to detect jumps in both S&P 500 and simulated time series, and compare its performance with several classic jump detection methods. Throughout, our Med9 consistently outperforms other approaches applied to both real and simulated financial return series. In addition, we demonstrate that the Med9 detection results are not biased or compromised by the intraday volatility pattern.
published_date 2025-04-17T05:21:31Z
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score 11.089864

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