Traditionally, the random variable total annual losses has been treated as if it were generated by univariate frequency and severity processes. This approach either reflects the explicit assumption that only a single loss process is present or represents the aggregation of losses from multiple sources as in collective risk theory. This article presents a model that utilizes multivariate frequency and severity distributions to analyze total annual losses when multiple loss processes are present. Unlike the traditional approach, the multivariate technique allows explicit recognition of dependencies among the components of the overall process. Multivariate frequency and severity distributions that might be appropriate in insurance and risk management applications are discussed. Finally, the model is applied using the bodily injury and property damage liability claims experience of two fleets of vehicles. The central problem in risk theory is modeling the probability distribution of total claims. The total claims distribution and its components, the frequency and severity distributions, then are used to evaluate reinsurance and deductible programs, to compute ruin probabilities, and to provide other information of interest to decision makers. For most organizations, total claims arise from exposure to multiple perils, each of which typically can produce more than one type of claim. The most general case involves a three-stage process: a multivariate accident process where each accident can lead to multivariate claims frequency and severity processes. Statistical dependencies could be present at a number of points, but the authors consider the following the most important: (1) dependencies among the accident frequency processes, (2) dependencies among claims frequencies of the same or different types for a given accident type, and (3) dependencies among severities arising out of a given accident of a particular type. For example, weather conditions could affect the frequency of both fires and automobile accidents. Ignoring such dependencies can lead to serious underestimates in J. David Cummins is the Harry J. Loman Professor of Property-Liability Insurance at the Wharton School of the University of Pennsylvania. He has a Ph.D. from the same institution. He is Associate Director of the S. S. Huebner Foundation for Insurance Education, an academic member of the ARIA Board of Directors, and a Past Chairman of the ARIA Risk Theory Seminar. He is the author or co-author of numerous books and professional journal articles. Laurel J. Wiltbank is an Instructor in Economics at Colgate University. She holds a Ph.D. degree from the University of Pennsylvania and is a member of the ARIA Risk Theory Seminar.
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