IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-030-37110-4_7.html
   My bibliography  Save this book chapter

On Comparing the Influences of Exogenous Information on Bitcoin Prices and Stock Index Values

In: Mathematical Research for Blockchain Economy

Author

Listed:
  • Luis Montesdeoca

    (University of Southampton)

  • Mahesan Niranjan

    (University of Southampton)

Abstract

We consider time series analysis on cryptocurrencies such as Bitcoin. The traded values of any financial instrument could be seen as being influenced by market forces as well as underlying fundamentals relating to the performance of the asset. Bitcoin is somewhat different in this respect because there isn’t an underlying asset upon which its value may depend on. Here, by constructing a simple linear time series model, and by attempting to explain the variation in the residual signal by means of macroeconomic and currency exchange variables, we illustrate that the influencing variables are vastly different for cryptocurrencies from a stock indices (S&P 500) in both timescales analysed (daily and monthly values). We use a sequential estimation scheme (Kalman filter) to estimate the autoregressive model and a sparsity inducing linear regression with lags (LagLasso) to select relevant subsets of influencing variables to compare.

Suggested Citation

  • Luis Montesdeoca & Mahesan Niranjan, 2020. "On Comparing the Influences of Exogenous Information on Bitcoin Prices and Stock Index Values," Springer Proceedings in Business and Economics, in: Panos Pardalos & Ilias Kotsireas & Yike Guo & William Knottenbelt (ed.), Mathematical Research for Blockchain Economy, pages 93-100, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-37110-4_7
    DOI: 10.1007/978-3-030-37110-4_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below 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.

    Citations

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


    Cited by:

    1. Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020. "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers 2005.02217, arXiv.org.

    More about this item

    Keywords

    Bitcoin; Kalman filter; LagLasso;
    All these keywords.

    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:spr:prbchp:978-3-030-37110-4_7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.