IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp1519.html
   My bibliography  Save this paper

Hedging with Small Uncertainty Aversion

Author

Listed:
  • Sebastian Herrmann

    (University of Michigan at Ann Arbor)

  • Johannes Muhle-Karbe

    (Imperial College London - Department of Mathematics)

  • Frank Thomas Seifried

    (University of Trier)

Abstract

We study the pricing and hedging of derivative securities with uncertainty about the volatility of the underlying asset. Rather than taking all models from a prespecified class equally seriously, we penalise less plausible ones based on their "distance" to a reference local volatility model. In the limit for small uncertainty aversion, this leads to explicit formulas for prices and hedging strategies in terms of the security’s cash gamma.

Suggested Citation

  • Sebastian Herrmann & Johannes Muhle-Karbe & Frank Thomas Seifried, 2015. "Hedging with Small Uncertainty Aversion," Swiss Finance Institute Research Paper Series 15-19, Swiss Finance Institute, revised Apr 2017.
  • Handle: RePEc:chf:rpseri:rp1519
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2625965
    Download Restriction: no

    Citations

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


    Cited by:

    1. Frank Bosserhoff & Mitja Stadje, 2019. "Robustness of Delta hedging in a jump-diffusion model," Papers 1910.08946, arXiv.org.

    More about this item

    Keywords

    volatility uncertainty; ambiguity aversion; option pricing and hedging; asymptotics;

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

    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:rp1519. 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: (Ridima Mittal). General contact details of provider: http://edirc.repec.org/data/fameech.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.