Bootstrap forecast densities for returns and volatilities using the robust residual-based bootstrap procedure of Truc<U+00ED>os et at. (2017). The package also includes the robust GARCH (Generalized Autoregressive Conditional Heteroskedastic) estimator of Boudt et al. (2013) with the modification introduced by Truc<U+00ED>os et at. (2017).
This package provides a robust bootstrap procedure to obtain forecast densities for both return and volatilities in a GARCH context. The forecast densities are useful to obtain forecast intervals as well as to estimate risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). We also provide the robust GARCH estimator of Boudt et al. (2013) with the modification introduced by Truc<U+00ED>os et at. (2017). This procedure has shown good finite sample properties in both Monte Carlo experiments and empirical data. See; Truc<U+00ED>os et al. (2017), Truc<U+00ED>os (2019) and Truc<U+00ED>os et al. (2020) for recent implementations.
Boudt, Kris, Jon Danielsson, and S<U+00E9>bastien Laurent. Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting 29.2 (2013): 244-257.
Truc<U+00ED>os, Carlos, Luiz K. Hotta, and Esther Ruiz. Robust bootstrap forecast densities for GARCH returns and volatilities. Journal of Statistical Computation and Simulation 87.16 (2017): 3152-3174.
Truc<U+00ED>os, Carlos. Forecasting Bitcoin risk measures: A robust approach. International Journal of Forecasting 35.3 (2019): 836-847.
Truc<U+00ED>os, Carlos, Aviral K. Tiwari, and Faisal Alqahtani. Value-at-risk and expected shortfall in cryptocurrencies' portfolio: a vine copula<U+2013>based approach. Applied Economics 52.24 (2020): 2580-2593.