IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp2412.html
   My bibliography  Save this paper

Scheduling Processes and Inference of Scheduled Events From Price Data

Author

Listed:
  • Markus Leippold

    (University of Zurich; Swiss Finance Institute)

  • Michal Svaton

    (University of Zurich - Department of Banking and Finance)

Abstract

We introduce 'scheduling processes,' a novel class of processes tailored for modeling scheduled events in financial markets. These processes, driven by a Poisson mechanism, enable the endogenous arrival of events. Their most notable feature is the closed-form characteristic function, facilitating efficient derivative pricing through Fourier inversion methods. We also developed a specific filter method that allows us to draw conclusions about these planned events from derivative prices. An application of this model to VIX options from 2016 to 2020 not only identifies key events but also demonstrates superior performance compared to models without this feature.

Suggested Citation

  • Markus Leippold & Michal Svaton, 2024. "Scheduling Processes and Inference of Scheduled Events From Price Data," Swiss Finance Institute Research Paper Series 24-12, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2412
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4694428
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Option pricing; scheduling processes; particle filtering; event pricing;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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:chf:rpseri:rp2412. 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: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.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.