{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/37141"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/37141","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Market Simulations with a Matching Engine","abstract":"We demonstrate the CoinTossX Java web-application as a low-latency, high-throughput, open-source matching engine/artificial exchange/simulation platform and deploy it to a cloud environment for asynchronous order matching and submission in a controlled framework via two seperate simulation techniques — Hawkes processes and agent-based modelling. A 10-variate Hawkes model stress tests the software whilst measuring the extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a continuous-double auction. Estimation and calibration to the subsequent trade-and-quote data results in a model specification statistically different from the original — providing insight into the limits of the software, inference conducted on HFT models and future market microstructure modelling considerations. An asynchronous ABM with interacting low-frequency liquidity takers and high-frequency liquidity-providers is subsequently formulated with the aim of producing realistic trading scenarios/price action without relying on restrictive modelling assumptions or additional sources of noise. The resulting simulations are shown to replicate many stylized facts along with non-trivial price-impact curves and we use this to argue for future simple, reactive/actor-based financial model specifications that mimics real-world work-flow and system implementation.","abstract_html":"We demonstrate the CoinTossX Java web-application as a low-latency, high-throughput, open-source matching engine/artificial exchange/simulation platform and deploy it to a cloud environment for asynchronous order matching and submission in a controlled framework via two seperate simulation techniques — Hawkes processes and agent-based modelling. A 10-variate Hawkes model stress tests the software whilst measuring the extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a continuous-double auction. Estimation and calibration to the subsequent trade-and-quote data results in a model specification statistically different from the original — providing insight into the limits of the software, inference conducted on HFT models and future market microstructure modelling considerations. An asynchronous ABM with interacting low-frequency liquidity takers and high-frequency liquidity-providers is subsequently formulated with the aim of producing realistic trading scenarios/price action without relying on restrictive modelling assumptions or additional sources of noise. The resulting simulations are shown to replicate many stylized facts along with non-trivial price-impact curves and we use this to argue for future simple, reactive/actor-based financial model specifications that mimics real-world work-flow and system implementation.","abstract_has_math":false,"creators":["Jericevich, Ivan"],"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":"2026-07-22T22:23:00Z","subjects":["Statistical Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/37141","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":["Jericevich, Ivan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-02T11:03:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-02T11:03:30Z"]},{"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/11427/37141"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We demonstrate the CoinTossX Java web-application as a low-latency, high-throughput, open-source matching engine/artificial exchange/simulation platform and deploy it to a cloud environment for asynchronous order matching and submission in a controlled framework via two seperate simulation techniques — Hawkes processes and agent-based modelling. A 10-variate Hawkes model stress tests the software whilst measuring the extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a continuous-double auction. Estimation and calibration to the subsequent trade-and-quote data results in a model specification statistically different from the original — providing insight into the limits of the software, inference conducted on HFT models and future market microstructure modelling considerations. An asynchronous ABM with interacting low-frequency liquidity takers and high-frequency liquidity-providers is subsequently formulated with the aim of producing realistic trading scenarios/price action without relying on restrictive modelling assumptions or additional sources of noise. The resulting simulations are shown to replicate many stylized facts along with non-trivial price-impact curves and we use this to argue for future simple, reactive/actor-based financial model specifications that mimics real-world work-flow and system implementation."]},{"key":"dc:title","label":"Title","values":["Market Simulations with a Matching Engine"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gebbie, Timothy"],"dc:creator":["Jericevich, Ivan"],"dc:date.accessioned":["2023-03-02T11:03:30Z"],"dc:date.available":["2023-03-02T11:03:30Z"],"dc:date.issued":["2022"],"dc:description.abstract":["We demonstrate the CoinTossX Java web-application as a low-latency, high-throughput, open-source matching engine/artificial exchange/simulation platform and deploy it to a cloud environment for asynchronous order matching and submission in a controlled framework via two seperate simulation techniques — Hawkes processes and agent-based modelling. A 10-variate Hawkes model stress tests the software whilst measuring the extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a continuous-double auction. Estimation and calibration to the subsequent trade-and-quote data results in a model specification statistically different from the original — providing insight into the limits of the software, inference conducted on HFT models and future market microstructure modelling considerations. An asynchronous ABM with interacting low-frequency liquidity takers and high-frequency liquidity-providers is subsequently formulated with the aim of producing realistic trading scenarios/price action without relying on restrictive modelling assumptions or additional sources of noise. The resulting simulations are shown to replicate many stylized facts along with non-trivial price-impact curves and we use this to argue for future simple, reactive/actor-based financial model specifications that mimics real-world work-flow and system implementation."],"dc:identifier.uri":["http://hdl.handle.net/11427/37141"],"dc:publisher.department":["Department of Statistical Sciences"],"dc:subject":["Statistical Sciences"],"dc:title":["Market Simulations with a Matching Engine"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:00Z"}