IDEAS home Printed from https://ideas.repec.org/p/fth/caldec/00-02.html

Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data

Author

Listed:
  • Oscar Jorda
  • Massimiliano Marcellino

Abstract

This paper is a general investigation of temporal aggregation in time series analysis. It encompasses traditional research on time aggregation as a particular case and extends the analysis to irregular intervals of aggregation. The Data Generating Process is allowed to evolve at regular, deterministic-irregular or even stochastic intervals of time (operational time). The time scale of this process is then transformed to generate the observational time process. This transformation can be deterministic (such as the familiar aggregation of monthly data into quarters) or more generally, stochastic (such as aggregating stock market quotes by the hour). In general, the observational time model exhibits persistence, time-varying parameters and non-spherical disturbances. Consequently, we review detection, specification, estimation and structural inference in this context, provide new solutions to these issues, and apply our results to high frequency, FX data.

Suggested Citation

  • Oscar Jorda & Massimiliano Marcellino, "undated". "Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data," Department of Economics 00-02, California Davis - Department of Economics.
    • Handle: RePEc:fth:caldec:00-02
    as

    Download full text from publisher

    File URL: http://www.econ.ucdavis.edu/working_papers/00-2.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fth:caldec:00-02. 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: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/educdus.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.