CMStatistics 2020: Start Registration
View Submission - CFE
Title: CoVaR with volatility clustering, heavy tails and non-linear dependence Authors:  Giorgia Rivieccio - Parthenope University (Italy) [presenting]
Giovanni De Luca - University of Naples Parthenope (Italy)
Michele Leonardo Bianchi - Bank of Italy (Italy)
Abstract: The conditional value-at-risk is estimated 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.