Financial Toolbox    
ugarch

Univariate GARCH(P,Q) parameter estimation with Gaussian innovations

Syntax

Arguments

U
Single column vector of random disturbances, i.e., the residuals or innovations (t), of an econometric model representing a mean-zero, discrete-time stochastic process. The innovations time series U is assumed to follow a GARCH(P,Q) process.
P
Non-negative, scalar integer representing a model order of the GARCH process. P is the number of lags of the conditional variance. P can be zero; when P = 0, a GARCH(0,Q) process is actually an ARCH(Q) process.
Q
Positive, scalar integer representing a model order of the GARCH process. Q is the number of lags of the squared innovations.

Description

[Kappa, Alpha, Beta] = ugarch(U, P, Q) computes estimated univariate GARCH(P,Q) parameters with Gaussian innovations.

Kappa is the estimated scalar constant term () of the GARCH process.

Alpha is a P-by-1 vector of estimated coefficients, where P is the number of lags of the conditional variance included in the GARCH process.

Beta is a Q-by-1 vector of estimated coefficients, where Q is the number of lags of the squared innovations included in the GARCH process.

The time-conditional variance, t2, of a GARCH(P,Q) process is modeled as

where represents the argument Alpha, represents Beta, and the GARCH(P, Q) coefficients {, , } are subject to the following constraints.

Note that U is a vector of residuals or innovations (t) of an econometric model, representing a mean-zero, discrete-time stochastic process.

Although t2 is generated using the equation above, t and t2 are related as

where {vt} is an independent, identically distributed (i.i.d.) sequence ~ N(0,1).

Examples

See ugarchsim for an example of a GARCH(P,Q) process.

See Also
ugarchpred, ugarchsim, and the GARCH Toolbox function garchfit

References

James D. Hamilton, Time Series Analysis, Princeton University Press, 1994


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