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Application of Stochastic Optimal Control to Financial Market Debt Crises

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  • Jerome L. Stein

Abstract

This interdisciplinary paper explains how mathematical techniques of stochastic optimal control can be applied to the recent subprime mortgage crisis. Why did the financial markets fail to anticipate the recent debt crisis, despite the large literature in mathematical finance concerning optimal portfolio allocation and stopping rules? The uncertainty concerns the capital gain, the return on capital and the interest rate. An optimal debt ratio is derived where the drift is probabilistic but subject to economic constraints. The crises occurred because the market neglected to consider pertinent economic constraints in the dynamic stochastic optimization. The first constraint is that the firm should not be viewed in isolation. The optimizer should be the entire industry. The second economic constraint concerns the modeling of the drift of the price of the asset. The vulnerability of the borrowing firm to shocks from the capital gain, the return to capital or the interest rate, does not depend upon the actual debt/net worth per se. Instead it increases in proportion to the difference between the Actual and Optimal debt ratio, called the excess debt. A general measure of excess debt is derived and I show that it is an early warning signal of the recent crisis.

Suggested Citation

  • Jerome L. Stein, 2009. "Application of Stochastic Optimal Control to Financial Market Debt Crises," CESifo Working Paper Series 2539, CESifo.
    • Handle: RePEc:ces:ceswps:_2539
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    References listed on IDEAS

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    1. Jerome L. Stein, 2005. "Optimal Debt And Endogenous Growth In Models Of International Finance," Australian Economic Papers, Wiley Blackwell, vol. 44(4), pages 389-413, December.
    2. Blanchet-Scalliet, Christophette & Diop, Awa & Gibson, Rajna & Talay, Denis & Tanre, Etienne, 2007. "Technical analysis compared to mathematical models based methods under parameters mis-specification," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1351-1373, May.
    3. Stein, Jerome L., 2006. "Stochastic Optimal Control, International Finance, and Debt Crises," OUP Catalogue, Oxford University Press, number 9780199280575.
    4. Fleming, Wendell H. & Stein, Jerome L., 2004. "Stochastic optimal control, international finance and debt," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 979-996, May.
    5. Kristopher Gerardi & Andreas Lehnert & Shane M. Sherlund & Paul Willen, 2008. "Making Sense of the Subprime Crisis," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 39(2 (Fall)), pages 69-159.
    6. Yuliya Demyanyk & Otto van Hemert, 2007. "Understanding the subprime mortgage crisis," Supervisory Policy Analysis Working Papers 2007-05, Federal Reserve Bank of St. Louis.
    7. Jerome L. Stein, 2003. "Stochastic Optimal Control Modeling of Debt Crises," CESifo Working Paper Series 1043, CESifo.
    8. Robert Jarrow, 2017. "Derivatives," World Scientific Book Chapters, in: THE ECONOMIC FOUNDATIONS OF RISK MANAGEMENT Theory, Practice, and Applications, chapter 3, pages 19-28, World Scientific Publishing Co. Pte. Ltd..
    9. Eckhard Platen & Rolando Rebolledo, 1996. "Principles for modelling financial markets," Published Paper Series 1996-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
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    Cited by:

    1. Haluk Yener & Thanasis Stengos & M. Ege Yazgan, 2017. "Analysis of the seeds of the debt crisis in Europe," The European Journal of Finance, Taylor & Francis Journals, vol. 23(15), pages 1589-1610, December.
    2. Adil NAAMANE, 2012. "Peut-on prévenir les crises financières ?," Working Papers 2011-2012_7, CATT - UPPA - Université de Pau et des Pays de l'Adour, revised May 2012.
    3. Adil Naamane, 2012. "Peut-on prévenir les crises financières ?," Working Papers hal-01885154, HAL.

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    More about this item

    Keywords

    stochastic optimal control; dynamic optimization; mortgage crisis; Ito equation; risk aversion; debt management; warning signals;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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