fitted_cDCC: Conditional covariance matrix using the robust cDCC Estimator

In ctruciosm/Robpvc: Robust Principal Volatility Components

Conditional covariance matrix using the robust cDCC Estimator

Description

Conditional covariance matrix using the robust cDCC estimator of Boud et al. (2013) with the modification introduced by Trucíos et at. (2018).

Usage

fitted_cDCC(r, Qbar, params)

Arguments

r	Matrix of time series returns.
Qbar	Qbar matrix obtained from Robust_cDCC function
params	Estimated parameters obtained from Robust_cDCC function

Details

More details can be found in Boudt et al. (2013) and Trucíos et at. (2018).

Value

The function returns the estimated conditional covariance matrix from t = 1, ..., T+1, where T is the length of the sample size.

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])
# Estimated conditional covariance matrix
H = fitted_cDCC(toyexampledata[,1:3], cDCC[[2]], cDCC[[1]])
# One-step-ahead conditional covariance matrix
H[[nrow(toyexampledata[,1:3]) + 1]]

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