@misc{10498/30386,
year = {2021},
url = {http://hdl.handle.net/10498/30386},
abstract = {In this article we consider an aggregate loss model with dependent losses. The losses occurrence process is governed by a two-state Markovian arrival process (MAP2), a Markov renewal process process that allows for (1) correlated inter-losses times,  (2) non-exponentially  distributed inter-losses times and, (3) overdisperse losses counts. Some quantities of interest to measure persistence in the loss occurrence process are obtained. Given a real operational risk database, the aggregate loss model is estimated by fitting separately the inter-losses times and severities. The MAP2 is estimated via direct maximization of the likelihood function, and severities are modeled by the heavy-tailed, double-Pareto Lognormal distribution. In comparison with the fit provided by the Poisson process, the results point out that taking into account the dependence and overdispersion in the inter-losses times distribution leads to higher capital charges.},
publisher = {Elsevier},
keywords = {Loss modeling},
keywords = {Dependent losses times},
keywords = {{Markov renewal theory; Overdispersion}},
keywords = {Batch Markovian arrival process},
keywords = {PH distribution},
keywords = {double-Pareto Lognormal distribution},
keywords = {MLE estimation},
keywords = {Operational risk},
keywords = {Value-at-Risk},
title = {Analysis of an aggregate loss model in a Markov renewal regime},
doi = {10.1016/J.AMC.2020.125869},
author = {Ramírez Cobo, Josefa and Carrizosa, Emilio and Lillo, Rosa E.},
}