Learn R Programming

betategarch (version 2.0)

tegarch.sim2: Simulate from a first order two-component Beta-skew-t-EGARCH model

Description

Simulate the y series (typically understood as a financial return or the error in a regression) from a 1st. order 2 component Beta-skew-t-EGARCH model. Optionally, the sigma, lambda, lambdadagger, u and epsilon are also returned.

Usage

tegarch.sim2(n, omega = 0, phi1 = 0.95, phi2 = 0.9, kappa1 = 0.001,
  kappa2 = 0.01, kappastar = 0.005, df = 10, skew = 0.98,
  lambda.initial = NULL, verbose = FALSE)

Arguments

n
integer, length of y (i.e. no of observations)
omega
numeric, the value of omega
phi1
numeric, the value of phi1
phi2
numeric, the value of phi2
kappa1
numeric, the value of kappa1
kappa2
numeric, the value of kappa2
kappastar
numeric, the value of kappastar
df
numeric, the value of df (degrees of freedom)
skew
numeric, the value of skew (skewness parameter
lambda.initial
NULL (default) or initial value(s) of the recursion for lambda or log-volatility. If NULL then the values are chosen automatically.
verbose
logical, TRUE or FALSE (default). If TRUE then a matrix with n rows containing y, sigma, lambda, lambdadagger, u and epsilon is returned. If FALSE then only y is returned.

Value

  • A numeric vector of length n or a matrix with n rows, depending on the value of verbose.

Details

Empty

References

Harvey and Sucarrat (2012), 'EGARCH models with fat tails, skewness and leverage', Cambridge Working Papers in Economics 1236, Faculty of Economics, University of Cambridge

See Also

tegarch.est

Examples

Run this code
##simulate series with 500 observations:
set.seed(123)
y <- tegarch.sim2(500, omega=0.01, phi1=0.95, phi2=0.9, kappa1=0.01, kappa2=0.05,
  kappastar=0.03, df=10, skew=0.8)

##simulate the same series, but with more output (volatility, log-volatility or
##lambda, lambda1dagger, lambda2dagger, u and epsilon)
set.seed(123)
y <- tegarch.sim2(500, omega=0.01, phi1=0.95, phi2=0.9, kappa1=0.01, kappa2=0.05,
  kappastar=0.03, df=10, skew=0.8, verbose=TRUE)

Run the code above in your browser using DataLab