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Estimating U.S. Pollution Liabilities by Simulation

Estimating U.S. Pollution Liabilities by Simulation. Christopher Diamantoukos, FCAS, MAAA. Introduction. Approach to solving the problems encountered in the estimation of Environmental Pollution costs or liabilities

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Estimating U.S. Pollution Liabilities by Simulation

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  1. Estimating U.S. Pollution Liabilities by Simulation Christopher Diamantoukos, FCAS, MAAA

  2. Introduction • Approach to solving the problems encountered in the estimation of Environmental Pollution costs or liabilities • Review the process and the structure of a pollution cost simulation model (PCSM) • Relevance to actuaries and past studies

  3. Overview of Process • Analysis, research, and the Scientific Method • Estimating parameters • Specialized analyses or subsystems • Modeling techniques

  4. Estimating Parameters • Site Costs, number of sites: fundamental frequency and severity approach tends to be empirical • Legal issues relating to coverage for transfer of liability (triggers, conditions, exclusions, contribution over time) tend to be subjective

  5. Specialized Subsystems • Site cost variability • Coverage Defense Module (CDM) • PRP and ultimately responsible parties’ shares • Estimating costs and frequencies among states

  6. Modeling Techniques • Beta distribution and shares • Credibility used for cost differentiation among states • Team approach: RODS, CDM, Aliases

  7. Exposure Model • Sites and costs: the proximate exposure base • Translation to entities (PRPs) • Allocation of liability over “time” • Liability transfer through insurance or other “risk transfer agreements” • Insurance losses never used

  8. Linear Flow of the PCSM • Site cleanup costs at CERCLA (EPA) level • PRP shares • Coverage Defense Module (CDM) • Loss (Cost) Adjustment Expenses and distribution of costs across years • Either one of: • State costs to get countrywide estimate, or • Insurance portfolio attachment

  9. The Observations List • Magnitude • CDM Effects • Underlying Limits: the double-edged sword • Complements aggregate level exposure analysis • State variability tends to be overstated

  10. Beyond the Paper • State sites: the future? • Distributions, characterization, and classification • Stochastic modeling: cooperative processing • Samples and biases

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