# arch.univariate.SkewStudent.loglikelihood¶

SkewStudent.loglikelihood(parameters, resids, sigma2, individual=False)[source]

Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student’s t distribution, conditional on the variance.

Parameters
parametersndarray

Shape parameter of the skew-t distribution

residsndarray

The residuals to use in the log-likelihood calculation

sigma2ndarray

Conditional variances of resids

individualbool, optional

Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)

Returns
llfloat

The log-likelihood

Notes

The log-likelihood of a single data point x is

$\ln\left[\frac{bc}{\sigma}\left(1+\frac{1}{\eta-2} \left(\frac{a+bx/\sigma} {1+sgn(x/\sigma+a/b)\lambda}\right)^{2}\right) ^{-\left(\eta+1\right)/2}\right],$

where $$2<\eta<\infty$$, and $$-1<\lambda<1$$. The constants $$a$$, $$b$$, and $$c$$ are given by

$a=4\lambda c\frac{\eta-2}{\eta-1}, \quad b^{2}=1+3\lambda^{2}-a^{2}, \quad c=\frac{\Gamma\left(\frac{\eta+1}{2}\right)} {\sqrt{\pi\left(\eta-2\right)} \Gamma\left(\frac{\eta}{2}\right)},$

and $$\Gamma$$ is the gamma function.

Return type

ndarray