<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is an important task in financial time series analysis. Change point detection is the identification of abrupt changes in the generative parameters of sequential data. In application areas such as finance, online rather than offline detection of change points in time series is mostly required, due to their use in predictive tasks, possibly embedded in automatic trading systems. However, the complex structure of the data generation processes makes this a challenging endeavor. This paper is concerned with online change point detection in financial time series using the Bayesian setting. To this end, the Bayesian posterior probability of change at a specific time is proposed and some procedures are presented for selecting the priors and estimation of parameters. Applications in simulated financial time series are given. Finally, conclusions are proposed.</p>
</abstract>

It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is an important task in financial time series analysis. Change point detection is the identification of abrupt changes in the generative parameters of sequential data. In application areas such as finance, online rather than offline detection of change points in time series is mostly required, due to their use in predictive tasks, possibly embedded in automatic trading systems. However, the complex structure of the data generation processes makes this a challenging endeavor. This paper is concerned with online change point detection in financial time series using the Bayesian setting. To this end, the Bayesian posterior probability of change at a specific time is proposed and some procedures are presented for selecting the priors and estimation of parameters. Applications in simulated financial time series are given. Finally, conclusions are proposed.

Bayesian Online Change Point Detection in Finance

Financial Internet Quarterly

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Bayesian Online Change Point Detection in Finance"}

It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments...

Bayesian Online Change Point Detection in Finance

Pubblicato online: 01 gen 2022

Pagine: 27 - 33

Ricevuto: 11 ott 2021

Accettato: 01 dic 2021

DOI: https://doi.org/10.2478/fiqf-2021-0025

Parole chiave
Bayesian setting, change point, financial time series, online detection, probability of change

© 2021 Reza Habibi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Bayesian Online Change Point Detection in Finance

Pubblicato online: 01 gen 2022

Pagine: 27 - 33

Ricevuto: 11 ott 2021

Accettato: 01 dic 2021

DOI: https://doi.org/10.2478/fiqf-2021-0025

Parole chiaveBayesian setting, change point, financial time series, online detection, probability of change

© 2021 Reza Habibi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Bayesian setting, change point, financial time series, online detection, probability of change