{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129291"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129291","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Forecast adjustment under shocks: similarity-based solutions to unprecedented events","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Lundquist, David Patrick"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Eck, Daniel J","Liang, Feng","Lee, Jihyung","Dayanikli, Gokce"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-02","date_published":"2025-05-02","updated_at":"2026-07-22T22:25:04Z","subjects":["forecasting","shock","unprecedented events","uncertainty"],"languages":["en","eng"],"rights":["Copyright 2025 David Lundquist"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129291","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Eck, Daniel J","Liang, Feng","Lee, Jihyung","Dayanikli, Gokce"]},{"key":"dc:creator","label":"Author","values":["Lundquist, David Patrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-02","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["forecasting","shock","unprecedented events","uncertainty"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 David Lundquist"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129291"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, David Lundquist, accepted the attached license on 2025-04-30 at 20:19.","The student, David Lundquist, submitted this Dissertation for approval on 2025-04-30 at 20:26.","This Dissertation was approved for publication on 2025-05-02 at 11:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22135 on 2025-10-19 at 18:11:24","This work examines various forecasting challenges under conditions that are not ideal. In the first chapter, we develop a procedure for forecasting the volatility of a time series immediately following a news shock by exploiting series that have experienced similar shocks. By positing news shocks to be affine functions of exogenous risk proxies, we are able to express particular news shocks as random realizations of a common data-generating process. In our model, random effects induce excess volatility. Those excess volatilities are then estimated as fixed effects and aggregated to adjust the GARCH volatility forecast of the time series under study by an additive term. The adjusted and unadjusted forecasts are evaluated using the unobservable but easily-estimated realized volatility (RV). Asymptotic results are established for our excess volatility estimators as well as our adjustment estimator. Numerical simulations are provided to illustrate conditions and hyperparameters under which our method thrives. A detailed real data application for forecasting volatility after the outcome of the 2016 United States presidential election demonstrates the applicability of the method. The second chapter dramatically abstracts from the comfort of a particular model and instead probes situations in which one’s default times series forecasting model must be adjusted, augmented, or abandoned completely, generating second-order questions about algorithmic design and the role of human judgment, as well as the information leveraged for forecast adjustment. This work further systematizes and unifies the rich landscape of model adjustment and model correction methods, with a special focus on forecast adjustment under the presence of news shocks, when unanticipated events may give an observer reason to doubt the credibility of the default forecasting function. We demonstrate the usefulness of similarity-based methods in forecasting and present a general framework dubbed Similarity-based Parameter Correction (SPC). We highlight several specific time series models that can benefit from SPC, along with formal results for some of those special cases.We close with a thorough discussion of the state of forecasting under non-ideal conditions and the directions in which it may find the most success."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Forecast adjustment under shocks: similarity-based solutions to unprecedented events"]}]}],"canonical_facts":{"dc:contributor":["Eck, Daniel J","Liang, Feng","Lee, Jihyung","Dayanikli, Gokce"],"dc:creator":["Lundquist, David Patrick"],"dc:date":["2025-05-02","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, David Lundquist, accepted the attached license on 2025-04-30 at 20:19.","The student, David Lundquist, submitted this Dissertation for approval on 2025-04-30 at 20:26.","This Dissertation was approved for publication on 2025-05-02 at 11:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22135 on 2025-10-19 at 18:11:24","This work examines various forecasting challenges under conditions that are not ideal. In the first chapter, we develop a procedure for forecasting the volatility of a time series immediately following a news shock by exploiting series that have experienced similar shocks. By positing news shocks to be affine functions of exogenous risk proxies, we are able to express particular news shocks as random realizations of a common data-generating process. In our model, random effects induce excess volatility. Those excess volatilities are then estimated as fixed effects and aggregated to adjust the GARCH volatility forecast of the time series under study by an additive term. The adjusted and unadjusted forecasts are evaluated using the unobservable but easily-estimated realized volatility (RV). Asymptotic results are established for our excess volatility estimators as well as our adjustment estimator. Numerical simulations are provided to illustrate conditions and hyperparameters under which our method thrives. A detailed real data application for forecasting volatility after the outcome of the 2016 United States presidential election demonstrates the applicability of the method. The second chapter dramatically abstracts from the comfort of a particular model and instead probes situations in which one’s default times series forecasting model must be adjusted, augmented, or abandoned completely, generating second-order questions about algorithmic design and the role of human judgment, as well as the information leveraged for forecast adjustment. This work further systematizes and unifies the rich landscape of model adjustment and model correction methods, with a special focus on forecast adjustment under the presence of news shocks, when unanticipated events may give an observer reason to doubt the credibility of the default forecasting function. We demonstrate the usefulness of similarity-based methods in forecasting and present a general framework dubbed Similarity-based Parameter Correction (SPC). We highlight several specific time series models that can benefit from SPC, along with formal results for some of those special cases.We close with a thorough discussion of the state of forecasting under non-ideal conditions and the directions in which it may find the most success."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129291"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 David Lundquist"],"dc:subject":["forecasting","shock","unprecedented events","uncertainty"],"dc:title":["Forecast adjustment under shocks: similarity-based solutions to unprecedented events"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}