@misc{10498/30594,
year = {2021},
url = {http://hdl.handle.net/10498/30594},
abstract = {In this article we consider an aggregate loss model with dependent losses. The loss occurrence process is governed by a two-state Markovian arrival process ( M A P 2 ), a Markov renewal process that allows for (1) correlated inter-loss times, (2) non-exponentially distributed inter-loss times and, (3) overdisperse loss counts. Some quantities of interest to measure persistence in the loss occurrence process are obtained. Given a real OpRisk database, the aggregate loss model is estimated by fitting separately the inter-loss times and severities. The M A P 2 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-loss times distribution leads to higher capital charges.},
publisher = {Elsevier},
keywords = {Loss modeling},
keywords = {Dependent loss times},
keywords = {Markov renewal theory},
keywords = {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.},
}