Advanced Short-Term Actuarial Mathematics (ASTAM) Practice Exam
Build your confidence for Advanced Short-Term Actuarial Mathematics (ASTAM). Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
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Practice with the Advanced Short-Term Actuarial Mathematics ASTAM Practice Exam to strengthen your ability to model and reason about short-term insurance risks. Working through severity models, aggregate models, and coverage modifications helps you connect distributional thinking to realistic loss scenarios. You can also build skills in parametric model construction, credibility, and reserving or pricing concepts. Take a manageable next step by using this practice to identify a few topics to review more deeply.
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Sample Questions
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A candidate is completing the registration form for the actuarial exam. What action must they take to register for the exam?
An actuarial candidate is planning their path to the ASA designation and is unsure about the sequence in which they must complete the exams, e-Learning courses, VEE credits, and the professionalism seminar. What does the SOA allow regarding the order of these requirements?
A candidate is mailing their exam application and considers using an overnight courier. Why must the courier package be addressed to the SOA street address rather than a post office box?
Which of the following best describes the scope of the Fundamentals of Actuarial Mathematics (FAM) Exam?
An actuarial student is preparing for the ASTAM exam and wants to know the structure of the exam. Which of the following correctly describes the ASTAM exam?
Exam insights and study advice
Short-term insurance work depends on sound estimates of losses, prices, and reserves. Severity and aggregate models support loss forecasting, while coverage modifications, credibility, and reserving concepts connect analysis to real pricing and capital decisions. Practicing these areas can help you contribute more confidently to actuarial work in property, casualty, and other short-term lines.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.
01Reserving and Pricing for Short-Term Insurance Coverages
Topics
- Understand, interpret, and apply techniques for estimating outstanding claims, using the following methods: Expected Loss Ratio; Chain-Ladder; Bornhuetter-Ferguson; Bayesian; Frequency and Severity.
- Understand, interpret, and apply the following statistical models and assumptions used for outstanding claims reserves: Mack’s model; Poisson model; Overdispersed Poisson model.
- Calculate projected losses using trend analysis.
- Calculate overall average rates and rate changes using the loss cost and loss ratio methods.
- Calculate risk classification differential changes, including balancing back.
Learning objectives
- Understand, interpret, and apply techniques for estimating outstanding claims, using the following methods: Expected Loss Ratio; Chain-Ladder; Bornhuetter-Ferguson; Bayesian; Frequency and Severity.
- Understand, interpret, and apply the following statistical models and assumptions used for outstanding claims reserves: Mack’s model; Poisson model; Overdispersed Poisson model.
- Calculate projected losses using trend analysis.
- Calculate overall average rates and rate changes using the loss cost and loss ratio methods.
- Calculate risk classification differential changes, including balancing back.
02Construction and Selection of Parametric Models
Topics
- Estimate the parameters for frequency and severity distributions by maximum likelihood.
- Estimate the variance of the estimators and construct normal and non-normal confidence intervals.
- Use the delta method to estimate the variance of the maximum likelihood estimator of a function of the parameter(s).
- Estimate the parameters for severity, frequency, and aggregate distributions using Bayesian Estimation.
- Perform model selection using: Graphical procedures; Hypothesis tests, including Kolmogorov-Smirnov, Chi-square goodness-of-fit, and Likelihood ratio (LRT) tests; Score-based approaches, including Schwarz Bayesian Criterion (SBC), Bayesian Information Criterion (BIC), and Akaike Information Criterio
Learning objectives
- Estimate the parameters for frequency and severity distributions by maximum likelihood.
- Estimate the variance of the estimators and construct normal and non-normal confidence intervals.
- Use the delta method to estimate the variance of the maximum likelihood estimator of a function of the parameter(s).
- Estimate the parameters for severity, frequency, and aggregate distributions using Bayesian Estimation.
- Perform model selection using: Graphical procedures; Hypothesis tests, including Kolmogorov-Smirnov, Chi-square goodness-of-fit, and Likelihood ratio (LRT) tests; Score-based approaches, including Schwarz Bayesian Criterion (SBC), Bayesian Information Criterion (BIC), and Akaike Information Criterio
03Aggregate Models
Topics
- Use convolution and recursive formulas to derive probability and distribution functions for aggregate claims distributions with (a,b,0) or (a,b,1) frequency, and with discrete severity distributions.
- Derive the discretized version of a continuous distribution using the method of rounding and local moment matching.
- Perform calculations for sums of compound Poisson models.
Learning objectives
- Use convolution and recursive formulas to derive probability and distribution functions for aggregate claims distributions with (a,b,0) or (a,b,1) frequency, and with discrete severity distributions.
- Derive the discretized version of a continuous distribution using the method of rounding and local moment matching.
- Perform calculations for sums of compound Poisson models.
04Credibility
Topics
- Explain and apply Bayesian (greatest accuracy) credibility.
- Apply Bühlmann and Bühlmann-Straub models and understand their relationship to Bayesian models.
- Explain and apply empirical Bayesian estimation in the nonparametric and semiparametric cases.
Learning objectives
- Explain and apply Bayesian (greatest accuracy) credibility.
- Apply Bühlmann and Bühlmann-Straub models and understand their relationship to Bayesian models.
- Explain and apply empirical Bayesian estimation in the nonparametric and semiparametric cases.
05Coverage Modifications
Topics
- Evaluate the effects of the following coverage modifications: deductibles, policy limits, maximum covered loss, coinsurance, and stop loss reinsurance.
- Calculate and interpret loss elimination ratios, increased limits factors, and deductible factors.
- Evaluate and interpret the effects of inflation on losses.
Learning objectives
- Evaluate the effects of the following coverage modifications: deductibles, policy limits, maximum covered loss, coinsurance, and stop loss reinsurance.
- Calculate and interpret loss elimination ratios, increased limits factors, and deductible factors.
- Evaluate and interpret the effects of inflation on losses.
06Severity Models
Topics
- Describe how changes in the parameters affect the distributions.
- Create new distributions by multiplication by a constant, raising to a power, exponentiation, mixing and splicing.
- Understand and interpret the characteristics of severity distributions.
- Compare two distributions based on various characteristics of their tails, including moments, ratios of moments, limiting tail behavior, hazard rate functions, and mean excess functions.
- Understand the derivation and characteristics of the Generalized Extreme Value and the Generalized Pareto distributions.
- Apply the Generalized Extreme Value and the Generalized Pareto distributions to the estimation of tail risk measures and probabilities.
Learning objectives
- Describe how changes in the parameters affect the distributions.
- Create new distributions by multiplication by a constant, raising to a power, exponentiation, mixing and splicing.
- Understand and interpret the characteristics of severity distributions.
- Compare two distributions based on various characteristics of their tails, including moments, ratios of moments, limiting tail behavior, hazard rate functions, and mean excess functions.
- Understand the derivation and characteristics of the Generalized Extreme Value and the Generalized Pareto distributions.
- Apply the Generalized Extreme Value and the Generalized Pareto distributions to the estimation of tail risk measures and probabilities.