Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free
  Regularization   We introduce a regularization approach to arbitrage-free factor-model
selection. The considered model selection problem seeks to learn the closest
arbitrage-free HJM-type model to any prespecified factor-model. An asymptotic
solution to this, a priori computationally intractable, problem is represented
as the limit of a 1-parameter family of optimizers to computationally tractable
model selection tasks. Each of these simplified model-selection tasks seeks to
learn the most similar model, to the prescribed factor-model, subject to a
penalty detecting when the reference measure is a local martingale-measure for
the entire underlying financial market. A simple expression for the penalty
terms is obtained in the bond market withing the affine-term structure setting,
and it is used to formulate a deep-learning approach to arbitrage-free affine
term-structure modelling. Numerical implementations are also performed to
evaluate the performance in the bond market.

33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th