Testing the Systemic Risk Differences in Banks

Jokivuolle, Esa; Tunaru, Radu; Vioto, Davide

doi:10.2139/ssrn.3189313

Cited by 2 publications

(1 citation statement)

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“…In Table 4 we report the average indicators and scores over the years from 2013 to 2018. There is a large difference between the GSIBs score and the score based on the ∆CoVaR, particularly for some banks (see also Jokivuolle et al (2018)). However, there are not remarkable differences in the ∆CoVaR scores computed under different distributional assumptions.…”

Section: Gsibs Indicatorsmentioning

confidence: 99%

CoVaR with volatility clustering, heavy tails and non-linear dependence

Bianchi,

De Luca,

Rivieccio

2020

Preprint

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In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR ≤ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR = based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample.

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Section: Gsibs Indicatorsmentioning

confidence: 99%