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Czasopismo
2021 | 17 | nr 4 | 27--33
Tytuł artykułu

Bayesian Online Change Point Detection in Finance

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. (original abstract)
Czasopismo
Rocznik
Tom
17
Numer
Strony
27--33
Opis fizyczny
Twórcy
autor
  • Central Bank of Iran
Bibliografia
  • Adams, R.P., MacKay, D.J. (2007). Bayesian Online Change Point Detection. arXiv Preprint, arXiv:0710.3742.
  • Davis, R., Lee, T., Rodriguez-Yam, G. (2006). Structural Break Estimation for Non-Stationary Time Series Models. Journal of the American Statistical Association, 101, 223-239.10.1198/016214505000000745
  • Gombay, E. (2008). Change Detection in Autoregressive Time Series. Journal of Multivariate Analysis, 99, 451-464.10.1016/j.jmva.2007.01.003
  • Gombay, E., Serban, D. (2009). Monitoring Parameter Change in Time Series Models. Journal of Multivariate Analysis, 100, 715-725.10.1016/j.jmva.2008.08.005
  • Habibi, H., Howard, I., Habibi, R. (2017). Bayesian Sensor Fault Detection in Markov Jump System. Asian Journal of Control, 19, 1465-1481.10.1002/asjc.1458
  • Koop, G.M., Potter, S.M. (2004). Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change Points. Technical report. USA: Federal Reserve Bank of New York.
  • Kurt, B., Yildiz, C., Ceritli, T.Y., Sankur, B., Cemgil, A.T. (2018). A Bayesian Change Point Model for Detecting SIP-based DDoS Attacks. Digital Signal Processing 77, 48-62.10.1016/j.dsp.2017.10.009
  • Lavielle, M., Moulines, E. (2000). Least-squares Estimation of an Unknown Number of Shifts in a Time Series. Journal of Time Series Analysis, 21, 33-59.10.1111/1467-9892.00172
  • Lavielle, M., Teyssiere, G. (2007). Adaptive Detection of Multiple Change-points in Asset Price Volatility. Long Memory in Economics, 129-156.10.1007/978-3-540-34625-8_5
  • Ombao, H., Raz, J., von Sachs, Malow, R. (2001). Automatic Statistical Analysis of Bi-variant Non-stationary Time Series. Journal of the American Statistical Association, 96, 543-560.10.1198/016214501753168244
  • Page, E.S. (1954). Continuous Inspection Schemes. Biometrika, 41, 100-115.10.1093/biomet/41.1-2.100
  • Saatci, Y., Turner, R.D., Rasmussen, C.E. (2010). Gaussian Process Change Point Models. In Proceedings of the 27th International Conference on Machine Learning. USA.
  • Taylor, S.J., Letham, B. (2018). Forecasting at Scale. The American Statistician, 72, 37-45.10.1080/00031305.2017.1380080
  • Truong, C., Oudre, L., Vayatis, N. (2018). A Review of Change Point Detection Methods. arXiv preprint, arXiv:1801.00718.
  • Xiao, Z., Hu, S., Zhang, Q., Tian, X., Chen, Y., Wang, J., Chen, Z. (2018). Ensembles of Change-point Detectors: Implications for Real-time BMI Applications. Journal of Computational Neuroscience, 10, 1-18.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171656974

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