loglik_cDCC: Loss function used in the cDCC robust estimation.

In ctruciosm/Robpvc: Robust Principal Volatility Components

Loss function used in the cDCC robust estimation.

Description

Loss function used in the cDCC robust estimation.

Usage

loglik_cDCC(par, Qb, s, sigma)

Arguments

par	Two-dimensional cDCC vector parameters
Qb	Qbar matrix (obtained from the Robust_cDCC function)
s	Devolatilized returns
sigma	Sigma parameter (which is computed inside the Robust_cDCC function). In a three-dimensional case this value is equal to 0.8309765.

Details

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

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 densities for dynamic conditional correlations: implications for portfolio selection and value-at-risk. Journal of Statistical Computation and Simulation 88.10 (2018): 1976-2000.

Examples

# Estimating the parameters of the cDCC model in a robust way.
cDCC = Robust_cDCC(toyexampledata[,1:3])
param = cDCC[[1]]
Qbar = cDCC[[2]]
vol1 = fitted_Vol(param[1:3],toyexampledata[,1])
vol2 = fitted_Vol(param[4:6],toyexampledata[,2])
vol3 = fitted_Vol(param[7:9],toyexampledata[,3])
e = matrix(c(toyexampledata[,1]/vol1[1:nrow(toyexampledata)],
             toyexampledata[,2]/vol2[1:nrow(toyexampledata)],
             toyexampledata[,3]/vol3[1:nrow(toyexampledata)]), ncol=3)
      
loglik_cDCC(param[10:11],Qbar,e, 0.8309765)      

ctruciosm/Robpvc documentation built on July 27, 2022, 10:22 p.m.