ROBUSTGARCHloss_RCPP: Loss function used in GARCH robust estimation.

In RobGARCHBoot: Robust Bootstrap Forecast Densities for GARCH Models

Description

Loss function used in GARCH (Generalized Autoregressive Conditional Heteroskedastic) robust estimation.

Usage

ROBUSTGARCHloss_RCPP(theta, r, sigma2)

Arguments

theta	Vector of robust estimated (or initial values) parameters obtained from ROBUSTGARCH function.
r	Vector of time series returns.
sigma2	robust squared volatility estimation (or initial value of squared volatility)

Details

This function is used in the robust estimation. We can use it to evaluate the value of the loss function using several values of the vector parameters (theta)

Value

Returns the value of the loss function

Author(s)

Carlos Trucíos

References

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.

Examples

# Using the Bitcoin daily returns, we estimate the parameter of the GARCH model in a robust way
param = ROBUSTGARCH(returnsexample)
# We can evaluate the loss function using the estimated parameters
ROBUSTGARCHloss_RCPP(param[2:3], returnsexample, param[1]/(1-param[2]-param[3]))

RobGARCHBoot documentation built on Dec. 17, 2020, 5:07 p.m.