Comparison of Value at Risk (VaR) Multivariate Forecast Models.

We investigate the performance of VaR (Value at Risk) forecasts, considering different multivariate models: HS (Historical Simulation), DCC-GARCH (Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity) with normal and Student's distribution, GO-GARCH (Generalized Orthogonal-Generalized Autoregressive Conditional Heteroskedasticity), and copulas Vine (C-Vine, D-Vine, and R-Vine). For copula models, we consider that marginal distribution follow normal, Student's and skewed Student's distribution. We assessed the performance of the models using stocks belonging to the Ibovespa index during the period from January 2012 to April 2022.

Liquidity spillover in international stock markets through distinct time scales.

This paper identifies liquidity spillovers through different time scales based on a wavelet multiscaling method. We decompose daily data from U.S.