1d:T747,# Var-cov matrix evaluation Evaluates the estimated var-cov matrix H_t with respect to a covariance proxy, under different robust loss functions if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="laurent2013loss",package="dccmidas",cached_env=.Rdpack.currefs) . The losses considered are also used in if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="amendola_2020;textual",package="dccmidas",cached_env=.Rdpack.currefs) . ```r 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 ## Returns The value of the loss for each $t$ ## Examples ```r 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]] ``` ## References if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_all_ref(.Rdpack.currefs)

cov_eval function