IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1705.05572.html
   My bibliography  Save this paper

A Novel Approach to Quantification of Model Risk for Practitioners

Author

Listed:
  • Zuzana Krajcovicova
  • Pedro Pablo Perez-Velasco
  • Carlos Vazquez

Abstract

Models continue to increase their already broad use across industry as well as their sophistication. Worldwide regulation oblige financial institutions to manage and address model risk with the same severity as any other type of risk, which besides defines model risk as the potential for adverse consequences from decisions based on incorrect and misused model outputs and reports. Model risk quantification is essential not only in meeting these requirements but for institution's basic internal operative. It is however a complex task as any comprehensive quantification methodology should at least consider the data used for building the model, its mathematical foundations, the IT infrastructure, overall performance and (most importantly) usage. Besides, the current amount of models and different mathematical modelling techniques is overwhelming. Our proposal is to define quantification of model risk as a calculation of the norm of some appropriate function that belongs to a Banach space, defined over a weighted Riemannian manifold endowed with the Fisher--Rao metric. The aim of the present contribution is twofold: Introduce a sufficiently general and sound mathematical framework to cover the aforementioned points and illustrate how a practitioner may identify the relevant abstract concepts and put them to work.

Suggested Citation

  • Zuzana Krajcovicova & Pedro Pablo Perez-Velasco & Carlos Vazquez, 2017. "A Novel Approach to Quantification of Model Risk for Practitioners," Papers 1705.05572, arXiv.org.
    • Handle: RePEc:arx:papers:1705.05572
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1705.05572
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rama Cont, 2006. "Model Uncertainty And Its Impact On The Pricing Of Derivative Instruments," Mathematical Finance, Wiley Blackwell, vol. 16(3), pages 519-547, July.
    2. Boucher, Christophe M. & Daníelsson, Jón & Kouontchou, Patrick S. & Maillet, Bertrand B., 2014. "Risk models-at-risk," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 72-92.
    3. Hull, John & Suo, Wulin, 2002. "A Methodology for Assessing Model Risk and its Application to the Implied Volatility Function Model," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(2), pages 297-318, June.
    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. Farkas, Walter & Fringuellotti, Fulvia & Tunaru, Radu, 2020. "A cost-benefit analysis of capital requirements adjusted for model risk," Journal of Corporate Finance, Elsevier, vol. 65(C).
    2. Kamila Sommer, 2014. "Fertility Choice in a Life Cycle Model with Idiosyncratic Uninsurable Earnings Risk," Finance and Economics Discussion Series 2014-32, Board of Governors of the Federal Reserve System (U.S.).
    3. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    4. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," Journal of Financial Stability, Elsevier, vol. 23(C), pages 79-91.
    5. Yu Feng, 2019. "Theory and Application of Model Risk Quantification," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2019, January-A.
    6. Yesol Huh, 2014. "Machines vs. Machines: High Frequency Trading and Hard Information," Finance and Economics Discussion Series 2014-33, Board of Governors of the Federal Reserve System (U.S.).
    7. Carol Alexander & José María Sarabia, 2012. "Quantile Uncertainty and Value‐at‐Risk Model Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1293-1308, August.
    8. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    9. Christophe Boucher & Gregory Jannin & Patrick Kouontchou & Bertrand Maillet, 2013. "An Economic Evaluation of Model Risk in Long-term Asset Allocations," Review of International Economics, Wiley Blackwell, vol. 21(3), pages 475-491, August.
    10. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, December.
    11. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    12. Baule, Rainer & Shkel, David, 2021. "Model risk and model choice in the case of barrier options and bonus certificates," Journal of Banking & Finance, Elsevier, vol. 133(C).
    13. Detering, Nils & Packham, Natalie, 2018. "Model risk of contingent claims," IRTG 1792 Discussion Papers 2018-036, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Lazar, Emese & Qi, Shuyuan, 2022. "Model risk in the over-the-counter market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 769-784.
    15. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    16. Yu Feng, 2019. "Non-Parametric Robust Model Risk Measurement with Path-Dependent Loss Functions," Papers 1903.00590, arXiv.org.
    17. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    18. Coqueret, Guillaume & Tavin, Bertrand, 2016. "An investigation of model risk in a market with jumps and stochastic volatility," European Journal of Operational Research, Elsevier, vol. 253(3), pages 648-658.
    19. Pelster, Matthias & Vilsmeier, Johannes, 2016. "The determinants of CDS spreads: Evidence from the model space," Discussion Papers 43/2016, Deutsche Bundesbank.
    20. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

    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:1705.05572. 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.