DAGM_2M_cond_vol {rumidas} | R Documentation |
DAGM-2M conditional volatility (with skewness)
Description
Obtains the conditional volatility of the DAGM with two MIDAS variables. For details, see Amendola et al. (2019).
Usage
DAGM_2M_cond_vol(param, daily_ret, mv_m_1, mv_m_2, K_1, K_2, lag_fun = "Beta")
Arguments
param |
Vector of starting values. |
daily_ret |
Daily returns, which must be an "xts" object. |
mv_m_1 |
first MIDAS variable already transformed into a matrix, through |
mv_m_2 |
second MIDAS variable already transformed into a matrix, through |
K_1 |
Number of (lagged) realizations of the first MIDAS variable to consider. |
K_2 |
Number of (lagged) realizations of the second MIDAS variable to consider. |
lag_fun |
optional. Lag function to use. Valid choices are "Beta" (by default) and "Almon", for the Beta and Exponential Almon lag functions, respectively. |
Value
The resulting vector is an "xts" object representing the conditional volatility.
References
Amendola A, Candila V, Gallo GM (2019). “On the asymmetric impact of macro–variables on volatility.” Economic Modelling, 76, 135–152. doi:10.1016/j.econmod.2018.07.025.
See Also
Examples
start_val<-c(0.01,0.80,0.05,0.2,0.1,1.1,0.4,1.1,0.5,1.1,0,1.1)
r_t<-sp500['2005/2010']
mv_m_1<-mv_into_mat(r_t,diff(indpro),K=12,"monthly")
mv_m_2<-mv_into_mat(r_t,diff(indpro),K=24,"monthly")
head(DAGM_2M_cond_vol(start_val,r_t,mv_m_1,mv_m_2,K_1=12,K_2=24))