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Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach

Author

Listed:
  • Xisong Jin

    ()

  • Francisco Nadal De Simone

    ()

Abstract

This study measures investment funds? systemic credit risk in three forms: (1) credit risk common to all funds within each of the seven categories National Central Banks report to the ECB; (2) credit risk in each category of investment fund conditional on distress on another category of investment fund and; (3) the build-up of investment funds? vulnerabilities which may lead to a disorderly unraveling. The paper uses a novel framework which combines marginal probabilities of distress estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM). The framework models investment funds? distress dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. In addition, the estimates of the common components of the investment funds? distress measures may contain some early warning features, and identifying the macro and financial variables most closely associated with them may serve to guide macro-prudential policy. The relative importance of these variables differs from those associated with the common components of marginal measures of distress. Thus this framework can contribute to the formulation of macro-prudential policy.

Suggested Citation

  • Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
  • Handle: RePEc:bcl:bclwop:bclwp095
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    File URL: http://www.bcl.lu/fr/Recherche/publications/cahiers_etudes/95/BCLWP095.pdf
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    References listed on IDEAS

    as
    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. Tobias Adrian & Hyun Song Shin, 2014. "Procyclical Leverage and Value-at-Risk," Review of Financial Studies, Society for Financial Studies, vol. 27(2), pages 373-403.
    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. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
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    Citations

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    Cited by:

    1. Molitor, Philippe & Doyle, Nicola & Hermans, Lieven & Weistroffer, Christian, 2016. "Shadow banking in the euro area: risks and vulnerabilities in the investment fund sector," Occasional Paper Series 174, European Central Bank.
    2. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.

    More about this item

    Keywords

    financial stability; investment funds; procyclicality; macro-prudential policy; structural credit risk models; probability of distress; non-linearities; generalized dynamic factor model; dynamic copulas;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • F3 - International Economics - - International Finance
    • G1 - Financial Economics - - General Financial Markets

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