IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2201.10846.html
   My bibliography  Save this paper

Fat Tails and Optimal Liability Driven Portfolios

Author

Listed:
  • Jan Rosenzweig

Abstract

We look at optimal liability-driven portfolios in a family of fat-tailed and extremal risk measures, especially in the context of pension fund and insurance fixed cashflow liability profiles, but also those arising in derivatives books such as delta one books or options books in the presence of stochastic volatilities. In the extremal limit, we recover a new tail risk measure, Extreme Deviation (XD), an extremal risk measure significantly more sensitive to extremal returns than CVaR. Resulting optimal portfolios optimize the return per unit of XD, with portfolio weights consisting of a liability hedging contribution, and a risk contribution seeking to generate positive risk-adjusted return. The resulting allocations are analyzed qualitatively and quantitatively in a number of different limits.

Suggested Citation

  • Jan Rosenzweig, 2022. "Fat Tails and Optimal Liability Driven Portfolios," Papers 2201.10846, arXiv.org, revised Apr 2023.
  • Handle: RePEc:arx:papers:2201.10846
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2201.10846
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pietro BALESTRA & Alberto HOLLY, 1990. "A General Kronecker Formula for the Moments of the Multivariate Normal Distribution," Cahiers de Recherches Economiques du Département d'économie 9002, Université de Lausanne, Faculté des HEC, Département d’économie.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jan Rosenzweig, 2023. "A Tale of Tail Covariances (and Diversified Tails)," Papers 2302.13646, arXiv.org.

    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. Peñaranda, Francisco & Sentana, Enrique, 2012. "Spanning tests in return and stochastic discount factor mean–variance frontiers: A unifying approach," Journal of Econometrics, Elsevier, vol. 170(2), pages 303-324.
    2. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2021. "Multivariate Hermite polynomials and information matrix tests," Working Paper series 21-12, Rimini Centre for Economic Analysis.
    3. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2009. "Asymmetric multivariate normal mixture GARCH," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2129-2154, April.
    4. Gabriele Fiorentini & Enrique Sentana, 2021. "Specification tests for non‐Gaussian maximum likelihood estimators," Quantitative Economics, Econometric Society, vol. 12(3), pages 683-742, July.
    5. Gabriele Fiorentini & Enrique Sentana, 2007. "On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models," Working Paper series 38_07, Rimini Centre for Economic Analysis.
    6. Gabriele Fiorentini & Enrique Sentana, 2009. "Dynamic Specification Tests for Static Factor Models," Working Papers wp2009_0912, CEMFI.
    7. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2006. "Multivariate normal mixture GARCH," CFS Working Paper Series 2006/09, Center for Financial Studies (CFS).
    8. Fiorentini, Gabriele & Sentana, Enrique, 2019. "Consistent non-Gaussian pseudo maximum likelihood estimators," Journal of Econometrics, Elsevier, vol. 213(2), pages 321-358.

    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:2201.10846. 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.