IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Estimating the Complexity Function of Financial Time Series: An Estimation Based on Predictive Stochastic Complexity

  • Ching-Wei Tan

    ()

    (National Chengchi University)

Registered author(s):

    Using a measure of predictive stochastic complexity, this paper examines the complexity of two types of financial time series of several Pacific Rim countries, including 11 series on stock returns and 9 series on exchange-rate returns. Motivated by Chaitin's application of Kolmogorov complexity to the definition of "Life," we examine complexity as a function of sample size and call it the complexity function. According to Chaitin (1979), if a time series is truly random, then its complexity should increase at the same rate as the sample size, which means one would not gain or lose any information by fine tuning the sample size. Geometrically, this means that the complexity function is a 45 degree line. Based on this criterion, we estimate the complexity function for 20 financial time series and their iid normal surrogates. It is found that, while the complexity functions of all surrogates lie near to the 45 degree line, those of the financial time series are above it, except for the Indonesian stock return. Therefore, while the complexity of most financial time series is initially low compared to pseudo random time series, it gradually catches up as sample size increases. The catching-up effect indicates a short-lived property of financial signals. This property may lend support to the hypothesis that financial time series are not random but are composed of a sequence of structures whose birth and death can be characterized by a jump process with an embedded Markov chain. The significance of this empirical finding is also discussed in light of the recent progress in financial econometrics. Further exploration of this property may help data miners to select moderate sample sizes in either their data-preprocessing procedures or active-learning designs.

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below under "Related research" whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 1143.

    as
    in new window

    Length:
    Date of creation: 01 Mar 1999
    Date of revision:
    Handle: RePEc:sce:scecf9:1143
    Contact details of provider: Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA
    Fax: +1-617-552-2308
    Web page: http://fmwww.bc.edu/CEF99/

    More information through EDIRC

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:sce:scecf9:1143. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

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

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.