DAGM_loglik_no_skew: DAGM log-likelihood (without skewness)

Description

Obtains the log-likelihood of the DAGM, without the asymmetric term linked to past negative returns, according to two errors' conditional distributions: Normal and Student-t. For details, see amendola_candila_gallo:2019;textualrumidas.

Usage

DAGM_loglik_no_skew(param, daily_ret, mv_m, K, distribution, lag_fun = "Beta")

Value

The resulting vector is the log-likelihood value for each \(i,t\).

Arguments

param: Vector of starting values.
daily_ret: Daily returns, which must be an "xts" object.
mv_m: MIDAS variable already transformed into a matrix, through mv_into_mat function.
K: Number of (lagged) realizations of the MIDAS variable to consider.
distribution: The conditional density to use for the innovations. At the moment, valid choices are "norm" and "std", for the Normal and Student-t distributions.
lag_fun: optional. Lag function to use. Valid choices are "Beta" (by default) and "Almon", for the Beta and Exponential Almon lag functions, respectively.

References

Examples

Run this code

# \donttest{
# conditional density of the innovations: normal
start_val<-c(alpha=0.01,beta=0.80,gamma_1=0.05,m=0,theta_pos=0,w2_pos=1.1,theta_neg=0,w2_neg=1.1)
r_t<-sp500['2005/2010']
mv_m<-mv_into_mat(r_t,diff(indpro),K=12,"monthly")
sum(DAGM_loglik(start_val,r_t,mv_m,K=12,distribution="norm"))

# conditional density of the innovations: Student-t
start_val<-c(0.01,0.80,0.05,0,0,1.1,0,1.1,5)
r_t<-sp500['2005/2010']
mv_m<-mv_into_mat(r_t,diff(indpro),K=12,"monthly")
sum(DAGM_loglik(start_val,r_t,mv_m,K=12,distribution="std"))
# }