Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Calibrating high frequency trading data to agent based models using approximate Bayesian computation
We consider Sequential Monte Carlo Approximate Bayesian Computation (SMC ABC) as a method of calibration for the use of agent based models in market micro-structure. To date, there are no successful calibrations of agent based models to high frequency trading data. Here we test whether a more sophis...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Statistical Sciences
2021
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613295098200064 |
|---|---|
| access_status_str | Open Access |
| author | Goosen, Kelly |
| author2 | Gebbie, Timothy |
| author_browse | Gebbie, Timothy Goosen, Kelly |
| author_facet | Gebbie, Timothy Goosen, Kelly |
| author_sort | Goosen, Kelly |
| collection | Thesis |
| description | We consider Sequential Monte Carlo Approximate Bayesian Computation (SMC ABC) as a method of calibration for the use of agent based models in market micro-structure. To date, there are no successful calibrations of agent based models to high frequency trading data. Here we test whether a more sophisticated calibration technique, SMC ABC, will achieve this feat on one of the leading agent based models in high frequency trading literature (the Preis-Golke-Paul-Schneider Agent Based Model (Preis et al., 2006)). We find that, although SMC ABC's naive approach of updating distributions can successfully calibrate simple toy models, such as autoregressive moving average models, it fails to calibrate this agent based model for high frequency trading. This may be for two key reasons, either the parameters of the model are not uniquely identifiable given the model output or the SMC ABC rejection mechanism results in information loss rendering parameters unidentifiable given insucient summary statistics. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/33699 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:51.607Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:11427/33699 Calibrating high frequency trading data to agent based models using approximate Bayesian computation Goosen, Kelly Gebbie, Timothy agent based models high frequency trading calibration approximate Bayesian computation sequential Monte Carlo stylised facts market micro-structure We consider Sequential Monte Carlo Approximate Bayesian Computation (SMC ABC) as a method of calibration for the use of agent based models in market micro-structure. To date, there are no successful calibrations of agent based models to high frequency trading data. Here we test whether a more sophisticated calibration technique, SMC ABC, will achieve this feat on one of the leading agent based models in high frequency trading literature (the Preis-Golke-Paul-Schneider Agent Based Model (Preis et al., 2006)). We find that, although SMC ABC's naive approach of updating distributions can successfully calibrate simple toy models, such as autoregressive moving average models, it fails to calibrate this agent based model for high frequency trading. This may be for two key reasons, either the parameters of the model are not uniquely identifiable given the model output or the SMC ABC rejection mechanism results in information loss rendering parameters unidentifiable given insucient summary statistics. 2021-08-04T10:44:55Z 2021-08-04T10:44:55Z 2021 2021-08-04T10:44:32Z Master Thesis Masters MSc http://hdl.handle.net/11427/33699 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | agent based models high frequency trading calibration approximate Bayesian computation sequential Monte Carlo stylised facts market micro-structure Goosen, Kelly Calibrating high frequency trading data to agent based models using approximate Bayesian computation |
| thesis_degree_str | Master's |
| title | Calibrating high frequency trading data to agent based models using approximate Bayesian computation |
| title_full | Calibrating high frequency trading data to agent based models using approximate Bayesian computation |
| title_fullStr | Calibrating high frequency trading data to agent based models using approximate Bayesian computation |
| title_full_unstemmed | Calibrating high frequency trading data to agent based models using approximate Bayesian computation |
| title_short | Calibrating high frequency trading data to agent based models using approximate Bayesian computation |
| title_sort | calibrating high frequency trading data to agent based models using approximate bayesian computation |
| topic | agent based models high frequency trading calibration approximate Bayesian computation sequential Monte Carlo stylised facts market micro-structure |
| url | http://hdl.handle.net/11427/33699 |
| work_keys_str_mv | AT goosenkelly calibratinghighfrequencytradingdatatoagentbasedmodelsusingapproximatebayesiancomputation |