In this thesis we explore the problem of detecting change-points in cross-asset correlations using a non-parametric approach. We began by comparing and contrasting several common methods for change-point detection as well as methods for measuring correlation. We finally settle on a statistic introduced in early 2012 by Herold Dehling et.al. and test this statistic against real world financial data. We provide the estimated change-point for this data as well as the asymptotic p-value associated with this statistic. Once this process was complete we went on to use simulated data to measure the accuracy, power, and type 1 error associated with this new statistic. Finally, we were able to draw conclusions on the functionality and usefulness of this statistic.
","abstract_html":"<p>In this thesis we explore the problem of detecting change-points in cross-asset correlations using a non-parametric approach. We began by comparing and contrasting several common methods for change-point detection as well as methods for measuring correlation. We finally settle on a statistic introduced in early 2012 by Herold Dehling et.al. and test this statistic against real world financial data. We provide the estimated change-point for this data as well as the asymptotic p-value associated with this statistic. Once this process was complete we went on to use simulated data to measure the accuracy, power, and type 1 error associated with this new statistic. Finally, we were able to draw conclusions on the functionality and usefulness of this statistic.</p>","abstract_has_math":false,"creators":["Diamond, L. Kaili"],"institution":null,"degree_name":"Master of Science in Mathematics (M.S.)","degree_level":"Thesis (open access)","degree_discipline":"Department of Mathematical Sciences","degree_department":null,"school":null,"contributors":["Martha Abell","Jonathan Duggins"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-10-01T07:00:00Z","date_published":"2012-10-01T07:00:00Z","updated_at":"2026-07-24T02:26:17Z","subjects":["ETD","Cross-asset correlation","Non-parametric","Change-point","Diversification","Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/16","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Martha Abell","Jonathan Duggins"]},{"key":"dc:creator","label":"Author","values":["Diamond, L. Kaili"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-07-19T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (open access)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mathematics (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","Cross-asset correlation","Non-parametric","Change-point","Diversification","Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/16"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis we explore the problem of detecting change-points in cross-asset correlations using a non-parametric approach. We began by comparing and contrasting several common methods for change-point detection as well as methods for measuring correlation. We finally settle on a statistic introduced in early 2012 by Herold Dehling et.al. and test this statistic against real world financial data. We provide the estimated change-point for this data as well as the asymptotic p-value associated with this statistic. Once this process was complete we went on to use simulated data to measure the accuracy, power, and type 1 error associated with this new statistic. Finally, we were able to draw conclusions on the functionality and usefulness of this statistic.
"]},{"key":"dc:title","label":"Title","values":["A Non-Parametric Approach to Change-Point Detection in Cross-Asset Correlations"]}]}],"canonical_facts":{"dc:contributor":["Martha Abell","Jonathan Duggins"],"dc:creator":["Diamond, L. Kaili"],"dc:date.available":["2013-07-19T07:00:00Z"],"dc:description.abstract":["In this thesis we explore the problem of detecting change-points in cross-asset correlations using a non-parametric approach. We began by comparing and contrasting several common methods for change-point detection as well as methods for measuring correlation. We finally settle on a statistic introduced in early 2012 by Herold Dehling et.al. and test this statistic against real world financial data. We provide the estimated change-point for this data as well as the asymptotic p-value associated with this statistic. Once this process was complete we went on to use simulated data to measure the accuracy, power, and type 1 error associated with this new statistic. Finally, we were able to draw conclusions on the functionality and usefulness of this statistic.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/16"],"dc:subject":["ETD","Cross-asset correlation","Non-parametric","Change-point","Diversification","Mathematics"],"dc:title":["A Non-Parametric Approach to Change-Point Detection in Cross-Asset Correlations"],"thesis:degree_discipline":["Department of Mathematical Sciences"],"thesis:degree_level":["Thesis (open access)"],"thesis:degree_name":["Master of Science in Mathematics (M.S.)"]},"updated_at":"2026-07-24T02:26:17Z"}