Multivariate Cox Hidden Markov models with an application to operational risk

Tsz Chai Fung, Andrei L. Badescu and X. Sheldon Lin

Scandinavian Actuarial Journal, 2019, vol. 2019, issue 8, 686-710

Abstract: Modeling multivariate time-series aggregate losses is an important actuarial topic that is very challenging due to the fact that losses can be serially dependent with heterogeneous dependence structures across loss types and business lines. In this paper, we investigate a flexible class of multivariate Cox Hidden Markov Models for the joint arrival process of loss events. Some of the nice properties possessed by this class of models, such as closed-form expressions, thinning properties and model versatility are discussed in details. We provide the expectation-maximization (EM) algorithm for efficient model calibration. Applying the proposed model to an operational risk dataset, we demonstrate that the model offers sufficient flexibility to capture most characteristics of the observed loss frequencies. By modeling the log-transformed loss severities through mixture of Erlang distributions, we can model the aggregate losses. Finally, out-of-sample testing shows that the proposed model is adequate to predict short-term future operational risk losses.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03461238.2019.1598482 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:sactxx:v:2019:y:2019:i:8:p:686-710

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/sact20

DOI: 10.1080/03461238.2019.1598482

Access Statistics for this article

Scandinavian Actuarial Journal is currently edited by Boualem Djehiche

More articles in Scandinavian Actuarial Journal from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().