fitted_Vol: Estimated Volatility
In RobGARCHBoot: Robust Bootstrap Forecast Densities for GARCH Models

Description Usage Arguments Details Value Author(s) References Examples

View source: R/ROBUSTGARCH.R

Using the robust estimated parameters of Boudt et al. (2013) with the modification introduced by Trucíos et at. (2017), we obtain the estimated volatility.

1	fitted_Vol(theta,r)

`theta`	Vector of robust estimated parameters obtained from ROBUSTGARCH function.
`r`	Vector of time series returns.

More details can be found in Boudt et al. (2013) and Trucíos et at. (2017).

The function returns the estimated volatility from 1 to T+1.

Carlos Trucíos

Boudt, Kris, Jon Danielsson, and Sébastien Laurent. Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting 29.2 (2013): 244-257.

Trucíos, 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.

# Using the Bitcoin daily returns, we estimate the parameter of the GARCH model in a robust way
param = ROBUSTGARCH(returnsexample)
# With the estimated parameters, we estimate the volatiltiy in a robust way
vol = fitted_Vol(param, returnsexample)