IMF Working Papers

Systemic Risk Modeling: How Theory Can Meet Statistics

By Raphael A Espinoza, Miguel A. Segoviano, Ji Yan

March 13, 2020

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Raphael A Espinoza, Miguel A. Segoviano, and Ji Yan. Systemic Risk Modeling: How Theory Can Meet Statistics, (USA: International Monetary Fund, 2020) accessed November 14, 2024

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Summary

We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.

Subject: Banking, Consumer loans, Interbank markets, Loans, Systemic risk

Keywords: Central bank, WP

Publication Details

  • Pages:

    39

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

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  • Series:

    Working Paper No. 2020/054

  • Stock No:

    WPIEA2020054

  • ISBN:

    9781513536170

  • ISSN:

    1018-5941