Model for risk and forecasting

In this paper, analyse a time series and modelled around several models of the GARCH family, in order to estimate the conditional volatility; also referred to the Realized Volatil- ity and estimated the main models on it (MEM, HAR and RGARCH). Then through the VaR and ES estimations via parametric (GARCH models), non-parametric (Historical Simulation) and semi-parametric (Quantile models) approaches and the relatives backtesting. Lastly, the Model Confidence Set procedure has been implemented to better catch whose model is the best to compute VaR and ES forecasting. Worked in r studio