GM_2M_long_run_vol {rumidas}R Documentation

GARCH-MIDAS-2M long-run volatility (with skewness)

Description

Obtains the long-run volatility of the GARCH-MIDAS with two low-frequency variables, with an asymmetric term linked to past negative returns. For details, see Engle et al. (2013) and Conrad and Loch (2015).

Usage

GM_2M_long_run_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_into_mat function.

mv_m_2

second MIDAS variable already transformed into a matrix, through mv_into_mat function.

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 the long-run volatility for each i,t.

References

Conrad C, Loch K (2015). “Anticipating Long-Term Stock Market Volatility.” Journal of Applied Econometrics, 30(7), 1090–1114. doi:10.1002/jae.2404.

Engle RF, Ghysels E, Sohn B (2013). “Stock market volatility and macroeconomic fundamentals.” Review of Economics and Statistics, 95(3), 776–797. doi:10.1162/REST_a_00300.

See Also

mv_into_mat.

Examples


# conditional density of the innovations: normal
est_val<-c(alpha=0.01,beta=0.8,gamma=0.05,m=0,theta_1=0.1,w2_1=2,theta_2=0.1,w2_2=2)
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(GM_2M_long_run_vol(est_val,r_t,mv_m_1,mv_m_2,K_1=12,K_2=24))


[Package rumidas version 0.1.3 Index]