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THE ESTIMATION RISK IN EXTREME SYSTEMIC RISK FORECASTS

Published online by Cambridge University Press:  22 August 2023

Yannick Hoga
Yannick Hoga*
Affiliation:
University of Duisburg-Essen
*
Address correspondence to Yannick Hoga, Faculty of Economics and Business Administration, University of Duisburg-Essen, Essen, Germany; e-mail: [email protected]
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Abstract

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Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the residuals using an estimator based on extreme value theory. We show the validity of the method by establishing the asymptotic normality of the MES forecasts. The good finite-sample coverage of the implied MES forecast intervals is confirmed in simulations. An empirical application to major U.S. banks illustrates the significant time variation in the precision of MES forecasts, and explores the implications of this fact from a regulatory perspective.

Type
ARTICLES
Information
Econometric Theory , First View , pp. 1 - 50
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Footnotes

The author would like to thank the Co-Editor Eric Renault and the three anonymous referees for their insightful comments that significantly improved the quality of the paper. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through project 460479886.

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