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Market Simulations with a Matching Engine
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 technique...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2023
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| _version_ | 1867613225126723584 |
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| access_status_str | Open Access |
| author | Jericevich, Ivan |
| author2 | Gebbie, Timothy |
| author_browse | Gebbie, Timothy Jericevich, Ivan |
| author_facet | Gebbie, Timothy Jericevich, Ivan |
| author_sort | Jericevich, Ivan |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37141 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:45.765Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37141 Market Simulations with a Matching Engine Jericevich, Ivan Gebbie, Timothy Statistical Sciences 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. 2023-03-02T11:03:30Z 2023-03-02T11:03:30Z 2022 2023-02-20T12:58:21Z Master Thesis Masters MSc http://hdl.handle.net/11427/37141 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Jericevich, Ivan Market Simulations with a Matching Engine |
| thesis_degree_str | Master's |
| title | Market Simulations with a Matching Engine |
| title_full | Market Simulations with a Matching Engine |
| title_fullStr | Market Simulations with a Matching Engine |
| title_full_unstemmed | Market Simulations with a Matching Engine |
| title_short | Market Simulations with a Matching Engine |
| title_sort | market simulations with a matching engine |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/37141 |
| work_keys_str_mv | AT jericevichivan marketsimulationswithamatchingengine |