Generalized Autoregressive Conditional Heteroskedastic (GARCH) models for Julia.

garchFit(y::Vector)

Estimate parameters of the univariate normal GARCH process.

Arguments

• y::Vector: univariate time-series array

Examples

filename = Pkg.dir("GARCH", "test", "data", "price.csv")
price = Array{Float64}(readdlm(filename, ',')[:,2])
ret = diff(log.(price))
ret = ret - mean(ret)
fit = garchFit(ret)

predict(fit::GarchFit, n::Integer=1)

Make n-step prediction using fitted object returned by garchFit (default step=1).

Arguments

• fit::GarchFit : fitted model object returned by garchFit.
• n::Integer : the number of time-steps to be forecasted, by default 1 (returns scalar for n=1 and array for n>1).

Examples

fit = garchFit(ret)
predict(fit, n=2)

Fitted GARCH model object.

Estimate Hessian using central difference approximation.

Normal GARCH log likelihood function.

Simulate GARCH process.

Test the null of normality using the Jarque-Bera test statistic.