IDEAS home Printed from https://ideas.repec.org/p/arx/papers/math-0506125.html
   My bibliography  Save this paper

A Fast Algorithm for Computing Expected Loan Portfolio Tranche Loss in the Gaussian Factor Model

Author

Listed:
  • Pavel Okunev

Abstract

We propose a fast algorithm for computing the expected tranche loss in the Gaussian factor model. We test it on a 125 name portfolio with a single factor Gaussian model and show that the algorithm gives accurate results. We choose a 125 name portfolio for our tests because this is the size of the standard DJCDX.NA.HY portfolio. The algorithm proposed here is intended as an alternative to the much slower Moody's FT method.

Suggested Citation

  • Pavel Okunev, 2005. "A Fast Algorithm for Computing Expected Loan Portfolio Tranche Loss in the Gaussian Factor Model," Papers math/0506125, arXiv.org, revised Jun 2005.
    • Handle: RePEc:arx:papers:math/0506125
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/math/0506125
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Pavel Okunev, 2005. "Using Hermite Expansions for Fast and Arbitrarily Accurate Computation of the Expected Loss of a Loan Portfolio Tranche in the Gaussian Factor Model," Finance 0506015, University Library of Munich, Germany.
    2. Pavel Okunev, 2005. "Fast Computation of the Economic Capital, the Value at Risk and the Greeks of a Loan Portfolio in the Gaussian Factor Model," Risk and Insurance 0507004, University Library of Munich, Germany.

    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:arx:papers:math/0506125. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.