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Market- and Book-Based Models of Probability of Default for Developing Macroprudential Policy Tools

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  • Xisong Jin
  • Francisco Nadal de Simone

Abstract

The recent financial crisis raised awareness of the need for a framework for conducting macroprudential policy. Identifying as early as possible and addressing the buildup of endogenous imbalances, exogenous shocks, and contagion from financial markets, market infrastructures, and financial institutions are key elements of a sound macroprudential framework. This paper contributes to this literature by estimating several models of default probability, two of which relax two key assumptions of the Merton model: the assumption of constant asset volatility and the assumption of a single debt maturity. The study uses market and banks? balance sheet data. It finds that systemic risk in Luxembourg banks, while mildly correlated with that of European banking groups, did not increase as dramatically as it did for the European banking groups during the heights of the financial crisis. In addition, it finds that systemic risk has declined during the second half of 2010, both for the banking groups as well as for the Luxembourg banks. Finally, this study illustrates how models of default probability can be used for event-study purposes, for simulation exercises, and for ranking default probabilities during a period of distress according to banks? business lines. As such, this study is a stepping stone toward developing an operational framework to produce quantitative judgments on systemic risk and financial stability in Luxembourg.

Suggested Citation

  • Xisong Jin & Francisco Nadal de Simone, 2011. "Market- and Book-Based Models of Probability of Default for Developing Macroprudential Policy Tools," BCL working papers 65, Central Bank of Luxembourg.
  • Handle: RePEc:bcl:bclwop:bclwp065
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    References listed on IDEAS

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    1. Thorsten Lehnert & Xisong Jin & Francisco Nadal de Simone, 2011. "Does the GARCH Structural Credit Risk Model Make a Difference?," LSF Research Working Paper Series 11-6, Luxembourg School of Finance, University of Luxembourg.
    2. Gabriele Galati & Richhild Moessner, 2013. "Macroprudential Policy – A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 846-878, December.
    3. Marcos Souto & Benjamin M. Tabak & Francisco Vazquez, 2009. "Linking Financial and Macroeconomic Factors to Credit Risk Indicators of Brazilian Banks," Working Papers Series 189, Central Bank of Brazil, Research Department.
    4. Geske, Robert, 1977. "The Valuation of Corporate Liabilities as Compound Options," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 541-552, November.
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    Cited by:

    1. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    2. Jin, Xisong & Nadal De Simone, Francisco, 2014. "A framework for tracking changes in the intensity of investment funds' systemic risk," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 343-368.
    3. Jin, Xisong & Nadal De Simone, Francisco de A., 2014. "Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 81-101.
    4. Gastón Andrés Giordana & Ingmar Schumacher, 2017. "An Empirical Study on the Impact of Basel III Standards on Banks’ Default Risk: The Case of Luxembourg," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 10(2), pages 1-21, April.
    5. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.

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    More about this item

    Keywords

    financial stability; credit risk; structured products; default probability; GARCH;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G1 - Financial Economics - - General Financial Markets

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