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Topics include distributional quantities, tail-weight measures, risk measures such as VaR and TVaR, empirical modeling, survival estimators, decrement probability estimation, parametric model selection with statistical tests, and credibility 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Evaluation of Actuarial Models Exam  \nThe syllabus for this exam is defined in the form of learning objectives that set forth, usually in broad terms, what the candidate should be able to do in actual practice.  \nPlease check the “Syllabus Update” for this exam for any changes to this syllabus or to options for obtaining credit for this exam.  \nThe syllabus for this examination provides an introduction to modeling and covers important actuarial methods that are useful in modeling. A thorough knowledge of calculus, probability, and mathematical statistics is assumed.  \nThe candidate will be introduced to useful frequency and severity models beyond those covered in Exams 3Fand ST. The candidate will be required to understand the steps involved in the modeling process and how to carry out these steps in solving business problems. The candidate should be able to: (1) analyze data from an application in a business context; (2) determine a suitable model including parameter values; and (3) provide measures of confidence for decisions based upon the model. The candidate will be introduced to a variety of tools for the calibration and evaluation of the models.  \nThe candidate is expected to be familiar with survival, severity, frequency and aggregate models, and use statistical methods to estimate parameters of such models given sample data. The candidate is further expected to identify steps in the modeling process, understand the underlying assumptions implicit in each family of models, recognize which assumptions are applicable in a given business application, and appropriately adjust the models for impact of insurance coverage modifications.  \nSpecifically, the candidate is expected to be able to perform the tasks listed below.  \nA. Severity Models  \n\n| LEARNING OBJECTIVES |\n| --- |\n| 1. Calculate the basic distributional quantities:\u003Cbr>a. Moments\u003Cbr>b. Percentiles\u003Cbr>c. Generating functions\u003Cbr>2. Describe how changes in parameters affect the distribution.\u003Cbr>3. Recognize classes of distributions and their relationships.\u003Cbr>4. Apply the following techniques for creating new families of distributions:\u003Cbr>a. Multiplication by a constant\u003Cbr>b. Raising to a power\u003Cbr>c. Exponentiation\u003Cbr>d. Mixing\u003Cbr>5. Identify the applications in which each distribution is used and reasons why.\u003Cbr>6. Apply the distribution to an application, given the parameters.\u003Cbr>7. Calculate various measures of tail weight and interpret the results to compare the tail weights.\u003Cbr>8. Identify and describe two extreme value distributions. |\n\nB. Frequency Models  \n\n| LEARNING OBJECTIVES |\n| --- |\n| For the Poisson, Mixed Poisson, Binomial, Negative Binomial, Geometric distribution and mixtures thereof:\u003Cbr>1. Describe how changes in parameters affect the distribution.\u003Cbr>2. Calculate moments.\u003Cbr>3. Identify the applications for which each distribution is used and reasons why.\u003Cbr>4. Apply the distribution to an application given the parameters.\u003Cbr>5. Apply the zero-truncated or zero-modified distribution to an application given the parameters. |\n\nC. Aggregate Models  \n\n| LEARNING OBJECTIVES |\n| --- |\n| 1. Compute relevant parameters and statistics for collective risk models.\u003Cbr>2. Evaluate compound models for aggregate claims.\u003Cbr>3. Compute aggregate claims distributions. |\n\nD. For Severity, Frequency and Aggregate Models  \n\n| LEARNING OBJECTIVES |\n| --- |\n| 1. Evaluate the impacts of coverage modifications:\u003Cbr>a. Deductibles\u003Cbr>b. Limits\u003Cbr>c. Coinsurance\u003Cbr>2. Calculate Loss Elimination Ratios.\u003Cbr>3. Evaluate effects of inflation on losses. |\n\nE. Risk Measures  \n\n| LEARNING OBJECTIVES |\n| --- |\n| 1. Calculate VaR, and TVaR and explain their use and limitations. |\n\nF. Construction of Empirical Models  \n\n| LEARNING OBJECTIVES |\n| --- |\n| 1. Estimate failure time and loss distributions using:\u003Cbr>a. Kaplan-Meier estimator, including approximations for large data sets\u003Cbr>b. Nelson-Åalen estimator\u003Cbr>c. Kernel density estimator\u003Cbr>2","cbCaicrDiM5snJTh","https://ap.wps.com/l/cbCaicrDiM5snJTh","pdf",34091,"English","# Severity Models\n# Frequency Models\n# Aggregate Models\n# For Severity, Frequency and Aggregate Models\n# Risk Measures\n# Construction of Empirical Models\n# Estimation of Decrement Probabilities from Large Samples\n# Construction and Selection of Parametric Models\n# Credibility","[{\"question\":\"What core skills are assumed for this exam syllabus?\",\"answer\":\"A thorough knowledge of calculus, probability, and mathematical statistics is assumed, along with familiarity with survival, severity, frequency, and aggregate models.\"},{\"question\":\"What are the required outcomes for applying modeling in business problems?\",\"answer\":\"The candidate must understand modeling steps, analyze application data in a business context, determine a suitable model including parameter values, and provide confidence measures for model-based decisions.\"},{\"question\":\"Which topics are included under risk measures?\",\"answer\":\"The syllabus requires calculating VaR and TVaR and explaining their use and limitations.\"}]","Construction and Evaluation of Actuarial Models Exam - Syllabus | PDF",1790492102]