dccFit

The T-by-k data matrix of k-dimensional standardized asset returns. Typically, they are the 
standardized residuals of the command dccPre.

A logical switch to specify the type of DCC model. 
Type="TseTsui" for Tse and Tsui's DCC model. Type = "Engle" for Engle's DCC model. Default is Tse-Tsui model.

type

The initial parameter values for theta1 and theta2

theta

Conditional innovation distribution with std for multivariate Student-t innovations.

cond.dist

degrees of freedom of the multivariate Student-t innovations.

For Tse and Tsui method only, m denotes the number of returns 
used in local correlation matrix estimation

Fits a DCC model using either multivariate Gaussian or 
multivariate Student-t innovations. Two types of DCC models are available. The first type is proposed by Engle 
and the other is by Tse and Tsui. Both models appear in the Journal of Business and 
Economic Statistics, 2002.

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

dccFit: Dynamic Cross-Correlation Model Fitting

Description

Usage

Arguments

Value

References

See Also