IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2212.12318.html
   My bibliography  Save this paper

CDO calibration via Magnus Expansion and Deep Learning

Author

Listed:
  • Marco Di Francesco
  • Kevin Kamm

Abstract

In this paper, we improve the performance of the large basket approximation developed by Reisinger et al. to calibrate Collateralized Debt Obligations (CDO) to iTraxx market data. The iTraxx tranches and index are computed using a basket of size $K= 125$. In the context of the large basket approximation, it is assumed that this is sufficiently large to approximate it by a limit SPDE describing the portfolio loss of a basket with size $K\rightarrow \infty$. For the resulting SPDE, we show four different numerical methods and demonstrate how the Magnus expansion can be applied to efficiently solve the large basket SPDE with high accuracy. Moreover, we will calibrate a structural model to the available market data. For this, it is important to efficiently infer the so-called initial distances to default from the Credit Default Swap (CDS) quotes of the constituents of the iTraxx for the large basket approximation. We will show how Deep Learning techniques can help us to improve the performance of this step significantly. We will see in the end a good fit to the market data and develop a highly parallelizable numerical scheme using GPU and multithreading techniques.

Suggested Citation

  • Marco Di Francesco & Kevin Kamm, 2022. "CDO calibration via Magnus Expansion and Deep Learning," Papers 2212.12318, arXiv.org.
    • Handle: RePEc:arx:papers:2212.12318
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2212.12318
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kevin Kamm & Michelle Muniz, 2022. "Rating Triggers for Collateral-Inclusive XVA via Machine Learning and SDEs on Lie Groups," Papers 2211.00326, arXiv.org.
    2. Michael B. Giles & Christoph Reisinger, 2012. "Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance," Papers 1204.1442, arXiv.org.
    3. Conghui Hu & Xun Zhang & Qiuming Gao, 2015. "Synthetic CDO pricing: the perspective of risk integration," Applied Economics, Taylor & Francis Journals, vol. 47(15), pages 1574-1587, March.
    4. Nick Bush & Ben M. Hambly & Helen Haworth & Lei Jin & Christoph Reisinger, 2011. "Stochastic evolution equations in portfolio credit modelling with applications to exotic credit products," Papers 1103.4947, arXiv.org, revised Apr 2011.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ben Hambly & Nikolaos Kolliopoulos, 2020. "Fast mean-reversion asymptotics for large portfolios of stochastic volatility models," Finance and Stochastics, Springer, vol. 24(3), pages 757-794, July.
    2. Mike Giles & Lukasz Szpruch, 2012. "Multilevel Monte Carlo methods for applications in finance," Papers 1212.1377, arXiv.org.
    3. Ben Hambly & Nikolaos Kolliopoulos, 2018. "Fast mean-reversion asymptotics for large portfolios of stochastic volatility models," Papers 1811.08808, arXiv.org, revised Feb 2020.
    4. Kamm, Kevin & Pagliarani, Stefano & Pascucci, Andrea, 2023. "Numerical solution of kinetic SPDEs via stochastic Magnus expansion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 189-208.
    5. Pierre Rostan & Alexandra Rostan & François-Éric Racicot, 2020. "Increment Variance Reduction Techniques with an Application to Multi-name Credit Derivatives," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 1-35, January.
    6. A. K. Bahl & O. Baltzer & A. Rau-Chaplin & B. Varghese & A. Whiteway, 2013. "Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs," Papers 1308.2572, arXiv.org.
    7. Karolina Bujok & Ben Hambly & Christoph Reisinger, 2012. "Multilevel simulation of functionals of Bernoulli random variables with application to basket credit derivatives," Papers 1211.0707, arXiv.org, revised Feb 2018.
    8. Kay Giesecke & Konstantinos Spiliopoulos & Richard B. Sowers & Justin A. Sirignano, 2011. "Large Portfolio Asymptotics for Loss From Default," Papers 1109.1272, arXiv.org, revised Feb 2015.
    9. Andrei Cozma & Christoph Reisinger, 2015. "A mixed Monte Carlo and PDE variance reduction method for foreign exchange options under the Heston-CIR model," Papers 1509.01479, arXiv.org, revised Apr 2016.
    10. Ben Hambly & Nikolaos Kolliopoulos, 2019. "Stochastic PDEs for large portfolios with general mean-reverting volatility processes," Papers 1906.05898, arXiv.org, revised Mar 2024.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:2212.12318. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.