{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/37333"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/37333","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Calibrating a Latent Order Book Model to Market Data","abstract":"We investigate the formulation of the Latent Order Book (LOB) as a reaction diffusion Partial Differential Equation (PDE) and its subsequent numerical solution through an explicit method based on discrete stochastic processes. The numerical solution is calibrated using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB reaction-diffusion PDE and certain free-parameters are recoverable with the iterative calibration techniques.","abstract_html":"We investigate the formulation of the Latent Order Book (LOB) as a reaction diffusion Partial Differential Equation (PDE) and its subsequent numerical solution through an explicit method based on discrete stochastic processes. The numerical solution is calibrated using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB reaction-diffusion PDE and certain free-parameters are recoverable with the iterative calibration techniques.","abstract_has_math":false,"creators":["Gant, Michael"],"institution":"Department of Statistical Sciences","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Gebbie, Timothy"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-22T22:23:16Z","subjects":["Statistical Sciences,"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/37333","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gebbie, Timothy"]},{"key":"dc:creator","label":"Author","values":["Gant, Michael"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-07T12:46:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-07T12:46:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2022"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Statistical Sciences"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Statistical Sciences,"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/37333"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We investigate the formulation of the Latent Order Book (LOB) as a reaction diffusion Partial Differential Equation (PDE) and its subsequent numerical solution through an explicit method based on discrete stochastic processes. The numerical solution is calibrated using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB reaction-diffusion PDE and certain free-parameters are recoverable with the iterative calibration techniques."]},{"key":"dc:title","label":"Title","values":["Calibrating a Latent Order Book Model to Market Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gebbie, Timothy"],"dc:creator":["Gant, Michael"],"dc:date.accessioned":["2023-03-07T12:46:00Z"],"dc:date.available":["2023-03-07T12:46:00Z"],"dc:date.issued":["2022"],"dc:description.abstract":["We investigate the formulation of the Latent Order Book (LOB) as a reaction diffusion Partial Differential Equation (PDE) and its subsequent numerical solution through an explicit method based on discrete stochastic processes. The numerical solution is calibrated using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB reaction-diffusion PDE and certain free-parameters are recoverable with the iterative calibration techniques."],"dc:identifier.uri":["http://hdl.handle.net/11427/37333"],"dc:publisher.department":["Department of Statistical Sciences"],"dc:subject":["Statistical Sciences,"],"dc:title":["Calibrating a Latent Order Book Model to Market Data"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:16Z"}