cov_eval {dccmidas}R Documentation

Var-cov matrix evaluation

Description

Evaluates the estimated var-cov matrix H_t with respect to a covariance proxy, under different robust loss functions (Laurent et al. 2013). The losses considered are also used in Amendola et al. (2020).

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 tt

References

Amendola A, Braione M, Candila V, Storti G (2020). “A Model Confidence Set approach to the combination of multivariate volatility forecasts.” International Journal of Forecasting, 36(3), 873 - 891. doi:10.1016/j.ijforecast.2019.10.001.

Laurent S, Rombouts JV, Violante F (2013). “On loss functions and ranking forecasting performances of multivariate volatility models.” Journal of Econometrics, 173(1), 1–10. doi:10.1016/j.jeconom.2012.08.004.

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]]


[Package dccmidas version 0.1.2 Index]