On the modelling of multivariate counts with Cox processes and dependent shot noise intensities

Benjamin Avanzi, Greg Taylor, Bernard Wong and Xinda Yang

Insurance: Mathematics and Economics, 2021, vol. 99, issue C, 9-24

Abstract: In this paper, we develop a method to model and estimate several, dependent count processes, using granular data. Specifically, we develop a multivariate Cox process with shot noise intensities to jointly model the arrival process of counts (e.g. insurance claims). The dependency structure is introduced via multivariate shot noise intensity processes which are connected with the help of Lévy copulas. In aggregate, our approach allows for (i) over-dispersion and auto-correlation within each line of business; (ii) realistic features involving time-varying, known covariates; and (iii) parsimonious dependence between processes without requiring simultaneous primary (e.g. accidents) events.

Keywords: Dependency modelling; Cox process; Shot noise; Insurance claims counts; Micro-level model; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C51 C53 C55 G22 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:99:y:2021:i:c:p:9-24

DOI: 10.1016/j.insmatheco.2021.01.002

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