2 research outputs found
Making heads or tails of systemic risk measures
This paper shows that the CoVaR,-CoVaR,CoES,-CoES and MES
systemic risk measures can be represented in terms of the univariate risk
measure evaluated at a quantile determined by the copula. The result is applied
to derive empirically relevant properties of these measures concerning their
sensitivity to power-law tails, outliers and their properties under
aggregation. Furthermore, a novel empirical estimator for the CoES is proposed.
The power-law result is applied to derive a novel empirical estimator for the
power-law coefficient which depends on
. To show empirical performance
simulations and an application of the methods to a large dataset of financial
institutions are used. This paper finds that the MES is not suitable for
measuring extreme risks. Also, the ES-based measures are more sensitive to
power-law tails and large losses. This makes these measures more useful for
measuring network risk but less so for systemic risk. The robustness analysis
also shows that all measures can underestimate due to the occurrence
of intermediate losses. Lastly, it is found that the power-law tail coefficient
estimator can be used as an early-warning indicator of systemic risk.Comment: Revised version of the -CoES paper, now with a better
estimator and clear theoretical results. Main body: 22 pages. Appendix
contains: proofs, explanation of the data cleaning/pre-processing procedure,
supplementary figures, tables and details of the software/computer setu
New general dependence measures: construction, estimation and application to high-frequency stock returns
We propose a set of dependence measures that are non-linear, local, invariant
to a wide range of transformations on the marginals, can show tail and risk
asymmetries, are always well-defined, are easy to estimate and can be used on
any dataset. We propose a nonparametric estimator and prove its consistency and
asymptotic normality. Thereby we significantly improve on existing (extreme)
dependence measures used in asset pricing and statistics. To show practical
utility, we use these measures on high-frequency stock return data around
market distress events such as the 2010 Flash Crash and during the GFC.
Contrary to ubiquitously used correlations we find that our measures clearly
show tail asymmetry, non-linearity, lack of diversification and endogenous
buildup of risks present during these distress events. Additionally, our
measures anticipate large (joint) losses during the Flash Crash while also
anticipating the bounce back and flagging the subsequent market fragility. Our
findings have implications for risk management, portfolio construction and
hedging at any frequency