IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2019i3p51-61.html
   My bibliography  Save this article

Modelling Non-Stationary Financial Time Series with Input Warped Student T-Processes

Author

Listed:
  • Gheorghe RUXANDA

    (Bucharest University of Economic Studies)

  • Sorin OPINCARIU

    (Bucharest University of Economic Studies)

  • Stefan IONESCU

    (Romanian American University.)

Abstract

The evolution of financial assets is known to be non-stationary and to present long tails and non-Gaussian. Gaussian processes are a flexible and general Bayesian nonparametric generative model that provide flexible priors on function spaces and interpretable uncertainty quantification. While GP are extremely flexible function approximators, their Gaussian marginal distribution makes them inappropriate to model financial assets returns distributions. We present the Student t-processes that are known to fit heavier tail. We also augment the model with input warping to account with the financial time series non stationarity. We present a case study of fitting the evolution of SP500 index stressing the importance of good uncertainty estimates, especially when the series manifests structural breaks.

Suggested Citation

  • Gheorghe RUXANDA & Sorin OPINCARIU & Stefan IONESCU, 2019. "Modelling Non-Stationary Financial Time Series with Input Warped Student T-Processes," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 51-61, September.
    • Handle: RePEc:rjr:romjef:v::y:2019:i:3:p:51-61
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef3_19/rjef3_2019p51-61.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Petro Bidiuk & Tetyana Prosyankina-Zharova & Valerii Diakon & Dmytro Diakon, 2023. "The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 4(2(72)), pages 37-46, August.

    More about this item

    Keywords

    Bayesian nonparametric; Student processes; Gaussian processes; stylized facts;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rjr:romjef:v::y:2019:i:3:p:51-61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.