naginterfaces.library.rand.times_​garch_​asym1

naginterfaces.library.rand.times_garch_asym1(dist, num, ip, iq, theta, gamma, df, fcall, comm, statecomm)

times_garch_asym1 generates a given number of terms of a type I $\text{AGARCH}(p,q)$ process (see Engle and Ng (1993)).

For full information please refer to the NAG Library document for g05pd

https://support.nag.com/numeric/nl/nagdoc_30.3/flhtml/g05/g05pdf.html

Parameters

diststr, length 1

The type of distribution to use for ${ϵ}_t$.

$dist=\text{'N'}$

A Normal distribution is used.

$dist=\text{'T'}$

A Student’s $t$-distribution is used.

numint

$T$, the number of terms in the sequence.

ipint

The number of coefficients, ${\beta }_{\text{i}}$, for $\text{i}=1,2,\dots ,p$.

iqint

The number of coefficients, ${\alpha }_{\text{i}}$, for $\text{i}=1,2,\dots ,q$.

thetafloat, array-like, shape $(iq+ip+1)$

The first element must contain the coefficient ${\alpha }_o$, the next $iq$ elements must contain the coefficients ${\alpha }_{\text{i}}$, for $\text{i}=1,2,\dots ,q$. The remaining $ip$ elements must contain the coefficients ${\beta }_{\text{j}}$, for $\text{j}=1,2,\dots ,p$.

gammafloat

The asymmetry parameter $\gamma$ for the $\text{GARCH}(p,q)$ sequence.

dfint

The number of degrees of freedom for the Student’s $t$-distribution.

If $dist=\text{'N'}$, $df$ is not referenced.

fcallbool

If $fcall=True$, a new sequence is to be generated, otherwise a given sequence is to be continued using the information in $comm$[‘r’].

commdict, communication object, modified in place

Communication structure for the reference vector.

If $fcall=False$, this argument must have been initialized by a prior call to times_garch_asym1.

statecommdict, RNG communication object, modified in place

RNG communication structure.

This argument must have been initialized by a prior call to init_repeat() or init_nonrepeat().

Returns

htfloat, ndarray, shape $\left(num\right)$

The conditional variances $h_{\text{t}}$, for $\text{t}=1,2,\dots ,T$, for the $\text{GARCH}(p,q)$ sequence.

etfloat, ndarray, shape $\left(num\right)$

The observations ${ϵ}_{\text{t}}$, for $\text{t}=1,2,\dots ,T$, for the $\text{GARCH}(p,q)$ sequence.

Raises

NagValueError

(errno $1$)

On entry, $dist$ is not valid: $dist=⟨value⟩$.

(errno $2$)

On entry, $num=⟨value⟩$.

Constraint: $num\ge 0$.

(errno $3$)

On entry, $ip=⟨value⟩$.

Constraint: $ip\ge 0$.

(errno $4$)

On entry, $iq=⟨value⟩$.

Constraint: $iq\ge 1$.

(errno $5$)

On entry, $theta[⟨value⟩]=⟨value⟩$.

Constraint: $theta[i-1]\ge 0.0$.

(errno $7$)

On entry, $df=⟨value⟩$.

Constraint: $df\ge 3$.

(errno $11$)

$ip$ or $iq$ is not the same as when $comm$[‘r’] was set up in a previous call.

Previous value of $ip=⟨value⟩$ and $ip=⟨value⟩$.

Previous value of $iq=⟨value⟩$ and $iq=⟨value⟩$.

(errno $13$)

On entry, $statecomm$[‘state’] vector has been corrupted or not initialized.

(errno $51$)

On entry, sum of $theta[\text{i}-1]$, for $\text{i}=2,3,\dots ,ip+iq+1$ is $\ge 1.0$: $sum=⟨value⟩$.

Notes

A type I $\text{AGARCH}(p,q)$ process can be represented by:

$$h_t={\alpha }_0+\sum _{i=1}^q{\alpha }_i{({ϵ}_{t-i}+\gamma )}^2+\sum _{i=1}^p{\beta }_i{h}_{t-i}\text{,}t=1,2,\dots ,T\text{;}$$

where ${ϵ}_t|{\psi }_{t-1}=N(0,h_t)$ or ${ϵ}_t|{\psi }_{t-1}=S_t(\text{df},h_t)$. Here $S_t$ is a standardized Student’s $t$-distribution with $\text{df}$ degrees of freedom and variance $h_t$, $T$ is the number of observations in the sequence, ${ϵ}_t$ is the observed value of the $\text{GARCH}(p,q)$ process at time $t$, $h_t$ is the conditional variance at time $t$, and ${\psi }_t$ the set of all information up to time $t$. Symmetric GARCH sequences are generated when $\gamma$ is zero, otherwise asymmetric GARCH sequences are generated with $\gamma$ specifying the amount by which positive (or negative) shocks are to be enhanced.

One of the initialization functions init_repeat() (for a repeatable sequence if computed sequentially) or init_nonrepeat() (for a non-repeatable sequence) must be called prior to the first call to times_garch_asym1.

References

Bollerslev, T, 1986, Generalised autoregressive conditional heteroskedasticity, Journal of Econometrics (31), 307–327

Engle, R, 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica (50), 987–1008

Engle, R and Ng, V, 1993, Measuring and testing the impact of news on volatility, Journal of Finance (48), 1749–1777

Hamilton, J, 1994, Time Series Analysis, Princeton University Press