Simulate from a first order Beta-Skew-t-EGARCH model

Description

Simulate the y series (typically interpreted as a financial return or the error in a regression) from a first order Beta-Skew-t-EGARCH model. Optionally, the conditional scale (sigma), log-scale (lambda), conditional standard deviation (stdev), dynamic components (lambdadagger in the 1-component specification, lambda1dagger and lambda2dagger in the 2-component specification), score (u) and centred innovations (epsilon) are also returned.

Usage

1
2
tegarchSim(n, omega = 0, phi1 = 0.95, phi2 = 0, kappa1 = 0.01, kappa2 = 0,
  kappastar = 0, df = 10, skew = 1, 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

Details

Empty

Value

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

Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

References

Fernandez and Steel (1998), 'On Bayesian Modeling of Fat Tails and Skewness', Journal of the American Statistical Association 93, pp. 359-371.

Harvey and Sucarrat (2014), 'EGARCH models with fat tails, skewness and leverage'. Computational Statistics and Data Analysis 76, pp. 320-338.

Sucarrat (2013), 'betategarch: Simulation, Estimation and Forecasting of First-Order Beta-Skew-t-EGARCH models'. The R Journal (Volume 5/2), pp. 137-147.

See Also

tegarch, zoo

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
##1-component specification: simulate series with 500 observations:
set.seed(123)
y <- tegarchSim(500, omega=0.01, phi1=0.9, kappa1=0.1, kappastar=0.05,
  df=10, skew=0.8)

##simulate the same series, but with more output (volatility, log-volatility or
##lambda, lambdadagger, u and epsilon)
set.seed(123)
y <- tegarchSim(500, omega=0.01, phi1=0.9, kappa1=0.1, kappastar=0.05, df=10, skew=0.8,
  verbose=TRUE)
  
##plot the simulated values:
plot(y)

##2-component specification: simulate series with 500 observations:
set.seed(123)
y <- tegarchSim(500, omega=0.01, phi1=0.95, phi2=0.9, kappa1=0.01, kappa2=0.05,
  kappastar=0.03, df=10, skew=0.8)