IDEAS home Printed from https://ideas.repec.org/p/tky/fseres/2016cf1036.html
   My bibliography  Save this paper

Generalized Exponential Moving Average (EMA) Model with Particle Filtering and Anomaly Detection

Author

Listed:
  • Masafumi Nakano

    (Graduate School of Economics, The University of Tokyo)

  • Akihiko Takahashi

    (Faculty of Economics, The University of Tokyo)

  • Soichiro Takahashi

    (Graduate School of Economics, The University of Tokyo)

Abstract

This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in nancial markets. In par- ticular, we effectively apply a particle lter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detec- tors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum- variance and risk parity portfolios. Our dataset is monthly total returns of global nancial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns.

Suggested Citation

  • Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2016. "Generalized Exponential Moving Average (EMA) Model with Particle Filtering and Anomaly Detection," CIRJE F-Series CIRJE-F-1036, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2016cf1036
    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.

    More about this item

    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:tky:fseres:2016cf1036. 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: CIRJE administrative office (email available below). General contact details of provider: https://edirc.repec.org/data/ritokjp.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.