IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v60y2012i6p1373-1388.html
   My bibliography  Save this article

Computing Optimal Recovery Policies for Financial Markets

Author

Listed:
  • Fred E. Benth

    (Department of Mathematics, Centre of Mathematics for Applications, University of Oslo, N-0316 Oslo, Norway)

  • Geir Dahl

    (Department of Mathematics and Department of Informatics, Centre of Mathematics for Applications, University of Oslo, N-0316 Oslo, Norway)

  • Carlo Mannino

    (Department of Computer Science, University of Rome “La Sapienza,” Rome, Italy; and Centre of Mathematics for Applications, N-0316 Oslo, Norway)

Abstract

The current financial crisis motivates the study of correlated defaults in financial systems. In this paper we focus on such a model, which is based on Markov random fields. This is a probabilistic model in which uncertainty in default probabilities incorporates experts' opinions on the default risk (based on various credit ratings). We consider a bilevel optimization model for finding an optimal recovery policy: which companies should be supported given a fixed budget. This is closely linked to the problem of finding a maximum likelihood estimator of the defaulting set of agents, and we show how to compute this solution efficiently using combinatorial methods. We also prove properties of such optimal solutions and give a practical procedure for estimation of model parameters. Computational examples are presented, and experiments indicate that our methods can find optimal recovery policies for up to about 100 companies. The overall approach is evaluated on a real-world problem concerning the major banks in Scandinavia and public loans. To our knowledge, this is a first attempt to apply combinatorial optimization techniques to this important and expanding area of default risk analysis.

Suggested Citation

  • Fred E. Benth & Geir Dahl & Carlo Mannino, 2012. "Computing Optimal Recovery Policies for Financial Markets," Operations Research, INFORMS, vol. 60(6), pages 1373-1388, December.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:6:p:1373-1388
    DOI: 10.1287/opre.1120.1112
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.1120.1112
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.1120.1112?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Martine Labbé & Patrice Marcotte & Gilles Savard, 1998. "A Bilevel Model of Taxation and Its Application to Optimal Highway Pricing," Management Science, INFORMS, vol. 44(12-Part-1), pages 1608-1622, December.
    2. Giesecke, Kay & Weber, Stefan, 2006. "Credit contagion and aggregate losses," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 741-767, May.
    3. Robert A. Jarrow & Stuart M. Turnbull, 2008. "Pricing Derivatives on Financial Securities Subject to Credit Risk," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 17, pages 377-409, World Scientific Publishing Co. Pte. Ltd..
    4. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    7. Shaojie Deng & Kay Giesecke & Tze Leung Lai, 2012. "Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk," Operations Research, INFORMS, vol. 60(1), pages 78-91, February.
    8. René Carmona & Jean-Pierre Fouque & Douglas Vestal, 2009. "Interacting particle systems for the computation of rare credit portfolio losses," Finance and Stochastics, Springer, vol. 13(4), pages 613-633, September.
    9. Geir Dahl & Geir Storvik & Alice Fadnes, 2002. "Large-Scale Integer Programs in Image Analysis," Operations Research, INFORMS, vol. 50(3), pages 490-500, June.
    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. Qiu, Rui & Xu, Jiuping & Ke, Ruimin & Zeng, Ziqiang & Wang, Yinhai, 2020. "Carbon pricing initiatives-based bi-level pollution routing problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 203-217.

    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. Fred E. Benth & Geir Dahl & Carlo Mannino, 2010. "Computing optimal recovery policies for financial markets," DIS Technical Reports 2010-20, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    2. Xu, Ruxing & Li, Shenghong, 2010. "Belief updating, debt pricing and financial decisions under asymmetric information," Research in International Business and Finance, Elsevier, vol. 24(2), pages 123-137, June.
    3. André Lucas & Siem Jan Koopman, 2005. "Business and default cycles for credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 311-323.
    4. Andreas Mühlbacher & Thomas Guhr, 2018. "Extreme Portfolio Loss Correlations in Credit Risk," Risks, MDPI, vol. 6(3), pages 1-25, July.
    5. Tao Peng, 2010. "Portfolio Credit Risk Modelling and CDO Pricing - Analytics and Implied Trees from CDO Tranches," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 8, July-Dece.
    6. Konstantinos Spiliopoulos, 2014. "Systemic Risk and Default Clustering for Large Financial Systems," Papers 1402.5352, arXiv.org, revised Feb 2015.
    7. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    8. Hong-Ming Yin & Jin Liang & Yuan Wu, 2018. "On a New Corporate Bond Pricing Model with Potential Credit Rating Change and Stochastic Interest Rate," JRFM, MDPI, vol. 11(4), pages 1-12, December.
    9. Rafal M. Wojakowski & M. Shahid Ebrahim & Aziz Jaafar & Murizah Osman Salleh, 2019. "Can Loan Valuation Adjustment (LVA) approach immunize collateralized debt from defaults?," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 28(2), pages 141-158, May.
    10. Augusto Castillo, 2004. "Firm and Corporate Bond Valuation: A Simulation Dynamic Programming Approach," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 41(124), pages 345-360.
    11. John Y. Campbell & Glen B. Taksler, 2003. "Equity Volatility and Corporate Bond Yields," Journal of Finance, American Finance Association, vol. 58(6), pages 2321-2350, December.
    12. Ephraim Clark & Geeta Lakshmi, 2003. "Controlling the risk: a case study of the Indian liquidity crisis 1990-92," Journal of International Development, John Wiley & Sons, Ltd., vol. 15(3), pages 285-298.
    13. David Lee, 2018. "Pricing Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment," Working Papers hal-01758922, HAL.
    14. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    15. Kraft, Holger & Steffensen, Mogens, 2008. "How to invest optimally in corporate bonds: A reduced-form approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(2), pages 348-385, February.
    16. Hamerle, Alfred & Knapp, Michael & Wildenauer, Nicole, 2005. "Auswirkungen unterschiedlicher Assetkorrelationen in Mehr-Sektoren-Kreditportfoliomodellen," University of Regensburg Working Papers in Business, Economics and Management Information Systems 409, University of Regensburg, Department of Economics.
    17. Jobst, Norbert J. & Zenios, Stavros A., 2005. "On the simulation of portfolios of interest rate and credit risk sensitive securities," European Journal of Operational Research, Elsevier, vol. 161(2), pages 298-324, March.
    18. Di Bu & Simone Kelly & Yin Liao & Qing Zhou, 2018. "A hybrid information approach to predict corporate credit risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1062-1078, September.
    19. Samuel Chege Maina, 2011. "Credit Risk Modelling in Markovian HJM Term Structure Class of Models with Stochastic Volatility," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2011, January-A.
    20. Chia-Chien Chang & Chou-Wen Wang & Szu-Lang Liao, 2009. "The valuation of special purpose vehicles by issuing structured credit-linked notes," Applied Financial Economics, Taylor & Francis Journals, vol. 19(3), pages 227-256.

    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:inm:oropre:v:60:y:2012:i:6:p:1373-1388. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.