SemiDMFMC: [S]emi-parametirc [D]ynamic [M]ulti-[F]actor...

In GuanglinHuang/SemiDMFMC: Semi Dynamic Multi-Factor Multi-Cumulants Estimation

[S]emi-parametirc [D]ynamic [M]ulti-[F]actor [M]ulti-[C]umulant estimation

Description

[S]emi-parametirc [D]ynamic [M]ulti-[F]actor [M]ulti-[C]umulant estimation

Usage

SemiDMFMC(
  X,
  ff,
  Z = NULL,
  SemiFL = F,
  sel.bw = "uni",
  con_residual = T,
  best_model = NULL,
  Penalty = c("NONE", "LASSO", "MCP", "SCAD"),
  factor.control = list(var.model = "sGARCH", var.targeting = F, var.distribution =
    "sged", Corr.Struture = c("ica", "dcc", "copula"), dcc.model = "DCC", copula.model =
    list(copula = "mvt", method = "ML", time.varying = FALSE, transformation = "spd"),
    tgc.type = "leverage", tgc.targeting = F, mean.model = list(armaOrder = c(1, 1)),
    CTGC = FALSE, rep_sim_f = 10),
  eps.control = list(var.model = "sGARCH", var.targeting = F, var.distribution =
    "sged", tgc.type = "leverage", tgc.targeting = F, mean.model = list(armaOrder = c(0,
    0)), rep_sim_e = 10),
  ...
)

Arguments

X	A matrix or data frame with t rows (samples) and n columns (variables).
ff	The factors drive the data X.
Z	The variable drive factor loadings.
SemiFL	Logical. If TRUE, the factor loadings will be semi-parametric function.
sel.bw	Bandwith selection method.
con_residual	Logical, whether the moments of the residuals are time-varying or not.
best_model	A vector. It gives which factor loadings are semi-parametric.
Penalty	Only used for multi-factor model. Penalty regression to estimate factor loadings.
factor.control	The parameters of factors' moments estimation.
eps.control	The parameters of errors' moments estimation.
...	Any other passthru parameters.

Value

Estimated covariance, co-skewness, co-exkurtosis, co-kurtosis and the results of regression.

GuanglinHuang/SemiDMFMC documentation built on July 2, 2022, 7:25 p.m.