Dr. Yvonne Chueh, ASA, Ph.D., is a Professor of Actuarial Science at Central Washington University with more than 20 years of experience in preparing students to pass their SOA exams. She has served as a Council Member and Chair of the SOA Education & Research Special Interest Section. Dr. Chueh’s knowledge of STAM material is unparalleled and during this five-week intensive program will use her tremendous STAM experience to provide a personalized, hands-on approach in leading students to be in a dominant position to pass Exam STAM in October Learn more
The Exam STAM Pass Preparation Bootcamp Provides:
- Five comprehensive seminars integrating required syllabus conceptual understanding with problem solving review including the teaching of many valuable strategies, techniques, and methods for problem solving advancement
- Q&A email correspondence available with Dr. Chueh throughout the entire five week bootcamp up until the end of the exam sitting
- Online office hours available with Dr. Chueh throughout the entire five week bootcamp up until the end of the exam sitting
- Open Forum following each weekly seminar in which students can ask question regarding any syllabus topic/problem solving
- All students will be connected to Dr. Chueh’s Google Drive in which she shares new STAM content and problem solving solutions throughout the program
- Students receive recording of live seminar following its completion each Sunday
Schedule
Live Classes & Subjects
Seminar 1: 9/6
10:00 AM - 2:00 PM EST
10:00 AM - 2:00 PM EST
Severity, Frequency, and Cost Per Loss v.s. Per Payment Variables
• Severity Models: study the random variable of loss amount, with deductible or limit or both.
• Frequency Models: study the random variable of loss count, with deductible or limit or both. The class of (a,b,0) and (a,b,1) are the main model to master.
• Cost per loss and Cost per payment variables and their probability distributions, expected values, variance.
• Severity Models: study the random variable of loss amount, with deductible or limit or both.
• Frequency Models: study the random variable of loss count, with deductible or limit or both. The class of (a,b,0) and (a,b,1) are the main model to master.
• Cost per loss and Cost per payment variables and their probability distributions, expected values, variance.
Seminar 2: 9/13
10:00 AM - 2:00 PM EST
10:00 AM - 2:00 PM EST
Aggregate Claims, Compound Variables with Modified Distributions
• Aggregate Models: Express an aggregate claim model using primary and secondary distributions. Relate to their parameters or statistics for collective risk models. Recognize and define the distribution of an aggregate claim variable.
• Relations of severity, frequency and aggregate models: Include deductibles and policy limits to modify probability distributions of loss variables to claim variables under inflation.
• Aggregate Models: Express an aggregate claim model using primary and secondary distributions. Relate to their parameters or statistics for collective risk models. Recognize and define the distribution of an aggregate claim variable.
• Relations of severity, frequency and aggregate models: Include deductibles and policy limits to modify probability distributions of loss variables to claim variables under inflation.
Seminar 3: 9/20
10:00 AM - 2:00 PM EST
10:00 AM - 2:00 PM EST
Tail Distributions and Parametric Model Fitting and Testing
• Risk Measures: Define and calculate VaR, and TVaR. Solve related problems in risk measures.
• Risk Measures: Define and calculate VaR, and TVaR. Solve related problems in risk measures.
• Parametric Models: Estimate the parameters of failure time and loss distributions using:
a) Maximum likelihood
b) Method of moments
c) Percentile matching
d) Bayesian procedures
• Accept or reject a fitted model and/or compare models: by Graphical procedures or one of the following: Kolmogorov-Smirnov test, Chi-square goodness-of-fit test, Likelihood ratio test, Schwarz Bayesian Criterion, Akaike Information Criterion
Seminar 4: 9/27
10:00 AM - 2:00 PM EST
10:00 AM - 2:00 PM EST
Credibility Theory and Applications
• Credibility Theory: Apply limited fluctuation (classical) credibility including criteria for both full and partial credibility. Perform Bayesian analysis using both discrete and continuous models.
• Credibility Theory: Apply Bühlmann and Bühlmann-Straub models and understand the relationship of these to the Bayesian model. Apply conjugate priors in Bayesian analysis and in particular the Poisson-gamma model. Apply empirical Bayesian methods in the nonparametric and semiparametric cases.
• Credibility Theory: Apply limited fluctuation (classical) credibility including criteria for both full and partial credibility. Perform Bayesian analysis using both discrete and continuous models.
• Credibility Theory: Apply Bühlmann and Bühlmann-Straub models and understand the relationship of these to the Bayesian model. Apply conjugate priors in Bayesian analysis and in particular the Poisson-gamma model. Apply empirical Bayesian methods in the nonparametric and semiparametric cases.
Seminar 5: 10/4
10:00 AM - 2:00 PM EST
10:00 AM - 2:00 PM EST
Rate Making, Reserving, and Insurance Coverages
• Casualty Insurance Topics
• Medical and Dental Coverage, Property and Casualty Coverage
• Pricing: Pure Premium and Loss Ratio
• Reserving: Estimating unpaid losses from a run-off triangle, using • Chain ladder • Average cost per claim • Bornhuetter Ferguson
• Casualty Insurance Topics
• Medical and Dental Coverage, Property and Casualty Coverage
• Pricing: Pure Premium and Loss Ratio
• Reserving: Estimating unpaid losses from a run-off triangle, using • Chain ladder • Average cost per claim • Bornhuetter Ferguson
Join Our Group for The Exam STAM 5-Week Pass Preparation Program and Prepare to Pass STAM in October
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