Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Generalised AutoRegressive Conditional Heteroskedasticity   We propose Neural GARCH, a class of methods to model conditional
heteroskedasticity in financial time series. Neural GARCH is a neural network
adaptation of the GARCH 1,1 model in the univariate case, and the diagonal BEKK
1,1 model in the multivariate case. We allow the coefficients of a GARCH model
to be time varying in order to reflect the constantly changing dynamics of
financial markets. The time varying coefficients are parameterised by a
recurrent neural network that is trained with stochastic gradient variational
Bayes. We propose two variants of our model, one with normal innovations and
the other with Students t innovations. We test our models on a wide range of
univariate and multivariate financial time series, and we find that the Neural
Students t model consistently outperforms the others.

6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess