Generalized information criteria for high-dimensional sparse statistical jump models
Cortese, Federico P. ; Kolm, Petter N. and Lindström, Erik LU
(2026)
In AStA Advances in Statistical Analysis
- Abstract
We extend the generalized information criteria framework for model selection to high-dimensional sparse statistical jump models, a recent class of statistically robust and computationally efficient alternatives to hidden Markov models. Specifically, we derive expressions for the model fit and complexity to construct suitable information criteria for hyperparameter selection. In extensive simulation studies, we demonstrate that our approach selects the correct hyperparameters with high probability. Finally, providing an empirical application, we infer the key features that drive the return dynamics of the world equity market. We find that a three-state model best describes the dynamics of MSCI developed and emerging markets indexes.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/64f7f7da-9636-4d58-b862-6145fb30ece6
- author
- Cortese, Federico P.
; Kolm, Petter N.
and Lindström, Erik
LU
- organization
- publishing date
- 2026
- type
- Contribution to journal
- publication status
- in press
- subject
- keywords
- Clustering, Feature selection, Financial markets, Information criteria, Model selection, Regime switching, Unsupervised learning
- in
- AStA Advances in Statistical Analysis
- publisher
- Springer
- external identifiers
-
- scopus:105034924654
- ISSN
- 1863-8171
- DOI
- 10.1007/s10182-026-00554-9
- language
- English
- LU publication?
- yes
- id
- 64f7f7da-9636-4d58-b862-6145fb30ece6
- date added to LUP
- 2026-05-21 14:26:27
- date last changed
- 2026-05-21 14:27:03
@article{64f7f7da-9636-4d58-b862-6145fb30ece6,
abstract = {{<p>We extend the generalized information criteria framework for model selection to high-dimensional sparse statistical jump models, a recent class of statistically robust and computationally efficient alternatives to hidden Markov models. Specifically, we derive expressions for the model fit and complexity to construct suitable information criteria for hyperparameter selection. In extensive simulation studies, we demonstrate that our approach selects the correct hyperparameters with high probability. Finally, providing an empirical application, we infer the key features that drive the return dynamics of the world equity market. We find that a three-state model best describes the dynamics of MSCI developed and emerging markets indexes.</p>}},
author = {{Cortese, Federico P. and Kolm, Petter N. and Lindström, Erik}},
issn = {{1863-8171}},
keywords = {{Clustering; Feature selection; Financial markets; Information criteria; Model selection; Regime switching; Unsupervised learning}},
language = {{eng}},
publisher = {{Springer}},
series = {{AStA Advances in Statistical Analysis}},
title = {{Generalized information criteria for high-dimensional sparse statistical jump models}},
url = {{http://dx.doi.org/10.1007/s10182-026-00554-9}},
doi = {{10.1007/s10182-026-00554-9}},
year = {{2026}},
}