## Systemic Risk Modeling: How Theory Can Meet Statistics

_IMF Working Papers, March 13, 2020_

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## Bibliographic details
- Authors: Raphael A Espinoza, Miguel A. Segoviano, Ji Yan
- Published: March 13, 2020
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781513536170.001

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### Main contribution
- Proposes a framework to link empirical models of systemic risk to theoretical network/general equilibrium models used to understand channels of transmission of systemic risk.
- Uses empirical “cross-sectional" moments to re-calibrate a theoretical model and estimate the importance of a “Minsky" cycle of optimism in driving systemic risk.

### Theoretical model components
- Allows for systemic risk arising from:
  - interbank counterparty risk,
  - common asset exposures / fire sales,
  - a “Minsky" cycle of optimism.

### Empirical model and methodology
- Uses stock market and CDS spreads data to:
  - estimate a multivariate density of equity returns,
  - compute the expected equity return for each bank, conditional on a bad macro-outcome.
- The empirical “cross-sectional" moments derived from these estimates are used to re-calibrate the theoretical model.

### Key findings and focal estimation objective
- The framework is designed to estimate the importance of the Minsky cycle of optimism in driving systemic risk by reconciling empirical cross-sectional moments with theoretical model implications.

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_Source: https://www.imf.org/en/publications/wp/issues/2020/03/13/systemic-risk-modeling-how-theory-can-meet-statistics-49244_