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Risk endogeneity at the lender/investor-of-last-resort

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  • Diego Caballero
  • André Lucas
  • Bernd Schwaab
  • Xin Zhang

Abstract

We address to what extent a central bank can de-risk its balance sheet by unconventional monetary policy operations. To that end, we propose a novel risk measurement framework to empirically study the time variation in central bank portfolio credit risks associated with such operations. The framework accommodates a large number of bank and sovereign counterparties, joint tail dependence, skewness, and time-varying dependence parameters. In an application to selected items from the consolidated Eurosystem's weekly balance sheet between 2009 and 2015, we find that unconventional monetary policy operations generated beneficial risk spillovers across monetary policy operations, causing overall risk to be non-linear in exposures. Some policy operations reduced rather than increased overall risk.

Suggested Citation

  • Diego Caballero & André Lucas & Bernd Schwaab & Xin Zhang, 2019. "Risk endogeneity at the lender/investor-of-last-resort," BIS Working Papers 766, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:766
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    3. V. A. Mau, 2022. "Trends in Economic Science: Discussions of the Paths of Russian Modernization in the 19th–20th Centuries," Studies on Russian Economic Development, Springer, vol. 33(5), pages 506-512, October.
    4. Chavleishvili, Sulkhan & Fahr, Stephan & Kremer, Manfred & Manganelli, Simone & Schwaab, Bernd, 2021. "A risk management perspective on macroprudential policy," Working Paper Series 2556, European Central Bank.

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    More about this item

    Keywords

    credit risk; risk measurement; central bank; lender-of-last-resort; unconventional monetary policy;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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