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xdcclarge (version 0.1.0)

Estimating a (c)DCC-GARCH Model in Large Dimensions

Description

Functions for Estimating a (c)DCC-GARCH Model in large dimensions based on a publication by Engle et,al (2017) and Nakagawa et,al (2018) . This estimation method is consist of composite likelihood method by Pakel et al. (2014) and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (, , ).

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Version

Install

install.packages('xdcclarge')

Monthly Downloads

109

Version

0.1.0

License

GPL (>= 2)

Maintainer

Kei Nakagawa

Last Published

July 12th, 2018

Functions in xdcclarge (0.1.0)

dcc_estimation

This function estimates the parameters(alpha,beta) and time-varying correlation matrices(Rt) of DCC-GARCH model.
dcc_gradient

This functions calculates numerical gradient of log-likelihood of DCC-GARCH model.
cdcc_correlations

This function get the correlation matrix (Rt) of estimated cDCC-GARCH model.
cdcc_optim

This function optimizes log-likelihood of cDCC-GARCH model.
us_stocks

the closing price data of us stocks in SP500 index from 2006-03-31 to 2014-03-31 from yahoo finance.
dcc_correlations

This function get the correlation matrix (Rt) of estimated DCC-GARCH model.
cdcc_estimation

This function estimates the parameters(alpha,beta) and time-varying correlation matrices(Rt) of cDCC-GARCH model.
dcc_loglikelihood

This function calculates log-likelihood of DCC-GARCH model.
xdcclarge

Package
cdcc_gradient

This functions calculates numerical gradient of log-likelihood of cDCC-GARCH model.
cdcc_loglikelihood

This function calculates log-likelihood of cDCC-GARCH model.
dcc_optim

This function optimizes log-likelihood of DCC-GARCH model.