A dominance approach for comparing the performance of VaR forecasting models

We examine the tail systemic risk between the global financial system and financial institutions that belong to different industry groups. Our main contribution is the development of a systemic risk measure Delta Quantile-Located Conditional Autoregressive Expected Shortfall, ΔQLMV−CoCARES. This new measure captures the extreme downside risk in terms of the ES of the system should both the financial system and the institution simultaneously be in distress. The evidence suggests that cross significant volatility and ES effects exist between the system and financial institutions. Furthermore, our measure presents better forecasting performance than standard or novel systemic risk measures based on VaR such as CoVaR or ΔQLMV−CoCAViaR and it is effective at predicting financial crises. We also develop a new systemic stress indicator SSIES based on ΔQLMV−CoCARES systemic risk measure which presents higher forecasting ability than other standard stress indicators.

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Systematic Bagging Model for Market Risk - Cross Pattern Learning Framework for Value at Risk and Expected Shortfall Estimation

anon,

Ślepaczuk,

Chlebus

2024

Preprint

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A dominance approach for comparing the performance of VaR forecasting models

Abstract: trabajo-del-icaeWorking papers are in draft form and are distributed for discussion. It may not be reproduced without permission of the author/s.

Cited by 3 publications

References 46 publications

The use of the tail dependence function for high quantile risk measure analysis: an application to portfolio optimization

The use of the tail dependence function for high quantile risk measure analysis: an application to portfolio optimization

Measuring Systemic Risk Using Multivariate Quantile-Located ES Models

Systematic Bagging Model for Market Risk - Cross Pattern Learning Framework for Value at Risk and Expected Shortfall Estimation

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