Parameter estimates for two-state MS-GJR-GARCH(1,1) model with skewed Student’s-<i>t</i> distribution.

<p>Parameter estimates for two-state MS-GJR-GARCH(1,1) model with skewed Student’s-<i>t</i> distribution.</p

Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian GJR-GARCH(1,1) Frank copula EVT VaR model.

<p>Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian GJR-GARCH(1,1) Frank copula EVT VaR model.</p

Back-testing results following sGARCH(1,1) and GJR-GARCH(1,1) models with skewed student’s-<i>t</i> errors.

<p>Back-testing results following sGARCH(1,1) and GJR-GARCH(1,1) models with skewed student’s-<i>t</i> errors.</p

Back-testing results following Bayesian MS-GJR-GARCH(1,1) Student’s-<i>t</i> and Frank copula-EVT VaR models.

<p>Back-testing results following Bayesian MS-GJR-GARCH(1,1) Student’s-<i>t</i> and Frank copula-EVT VaR models.</p

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Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian MS-GJR-GARCH(1,1) Student’s-<i>t</i> copula EVT VaR model.

<p>Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian MS-GJR-GARCH(1,1) Student’s-<i>t</i> copula EVT VaR model.</p

POT parameter estimates, <i>VaR</i>_<i>q</i>(<i>Z</i>) and following Bayesian MS-GJR-GARCH(1,1) Frank and Student’s-<i>t</i> copula-EVT models.

<p>POT parameter estimates, <i>VaR</i>_<i>q</i>(<i>Z</i>) and following Bayesian MS-GJR-GARCH(1,1) Frank and Student’s-<i>t</i> copula-EVT models.</p

Mean excess function plot demonstrating the <i>hybrid</i> method for threshold selection.

<p>Mean excess function plot demonstrating the <i>hybrid</i> method for threshold selection.</p

Expected versus observed number of exceptions following Bayesian MS-GJR-GARCH(1,1) and GJR-GARCH(1,1) copula-EVT VaR model.

<p>Expected versus observed number of exceptions following Bayesian MS-GJR-GARCH(1,1) and GJR-GARCH(1,1) copula-EVT VaR model.</p

Kendall’s <i>τ</i>; <i>ρ</i>_<i>τ</i>(<i>ρ</i>_<i>SE</i>) for Gaussian and Student’s-<i>t</i> copula parameter estimates.

<p>Kendall’s <i>τ</i>; <i>ρ</i>_<i>τ</i>(<i>ρ</i>_<i>SE</i>) for Gaussian and Student’s-<i>t</i> copula parameter estimates.</p