Systemic Risk Modeling: How Theory Can Meet Statistics

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

March 13, 2020

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Format: Chicago

Raphael A Espinoza, Miguel A. Segoviano, and Ji Yan. Systemic Risk Modeling: How Theory Can Meet Statistics, (USA: International Monetary Fund, 2020) accessed December 22, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/054
Stock No:

WPIEA2020054
ISBN:

9781513536170
ISSN:

1018-5941

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