cov_eval: Var-cov matrix evaluation

In dccmidas: DCC Models with GARCH and GARCH-MIDAS Specifications in the Univariate Step, RiskMetrics, Moving Covariance and Scalar and Diagonal BEKK Models

Var-cov matrix evaluation

Description

Evaluates the estimated var-cov matrix H_t with respect to a covariance proxy, under different robust loss functions \insertCitelaurent2013lossdccmidas. The losses considered are also used in \insertCiteamendola_2020;textualdccmidas.

Usage

cov_eval(H_t, cov_proxy = NULL, r_t = NULL, loss = "FROB")

Arguments

H_t	Estimated covariance matrix, formatted as array
cov_proxy	optional Covariance matrix, formatted as array
r_t	optional List of daily returns used to calculate H_t. If parameter 'cov_proxy' is not provided, then r_t must be included. In this case, a (noise) proxy will be automatically used
loss	Robust loss function to use. Valid choices are: "FROB" for Frobenius (by default), "SFROB" for Squared Frobenius, "EUCL" for Euclidean, "QLIKE" for QLIKE and "RMSE" for Root Mean Squared Errors

Value

The value of the loss for each t

References

\insertAllCited

Examples

require(xts)
# close to close daily log-returns
r_t_s<-diff(log(sp500['2010/2019'][,3]))
r_t_s[1]<-0
r_t_n<-diff(log(nasdaq['2010/2019'][,3]))
r_t_n[1]<-0
r_t_f<-diff(log(ftse100['2010/2019'][,3]))
r_t_f[1]<-0
db_m<-merge.xts(r_t_s,r_t_n,r_t_f)
db_m<-db_m[complete.cases(db_m),]
colnames(db_m)<-c("S&P500","NASDAQ","FTSE100")
# list of returns
r_t<-list(db_m[,1],db_m[,2],db_m[,3])
# estimation
K_c<-144
N_c<-36
cdcc_est<-dcc_fit(r_t,univ_model="sGARCH",distribution="norm",
corr_model="DCCMIDAS",N_c=N_c,K_c=K_c)
cov_eval(cdcc_est$H_t,r_t=r_t)[(K_c+1):dim(cdcc_est$H_t)[3]]

dccmidas documentation built on May 29, 2024, 5:26 a.m.