ACSC/STAT 4703 - Fall 2016

Actuarial Models II

This is the page where I post material related to the ACSC/STAT 4703 course I am teaching in FALL 2016.

 

  • Office hours: Monday 10:30-11:30, Wednesday 10:30-11:30, Thursday 13:00-14:00
  • Office: 102 Chase building
  • If you want to come to my office at a different time please email me:tkenney@mathstat.dal.ca
  • Midterm Exam: Monday 26th October, in class.
  • Here are some practice questions for the Midterm exam. Here are the model solutions.
  • Here is the formula sheet for the midterm exam.
  • Here are the midterm model solutions.
  • Textbook: Loss Models: From Data to Decisions (Fourth Edition) by S. A. Klugman, H. J. Panjer and G. E. Wilmot, published by Wiley, 2012
  • Final Exam: Monday 12th December, 9:00-12:00 Seminar room (227) Chase Building. Here are some Practice questions and model solutions. Here is the formula sheet for the final. You will also be provided with any necessary tables. No notes are permitted in the examination. Scientific calculators are permitted, but not graphical calculators.

    • Handouts

    Course Handout

    Class Questions

    Answers to Class Questions

    (These are partly for my reference, so are not totally complete.)

    R code for some of the class questions

    Planned material

    Lecture time is limited, so I plan to use it explaining concepts and giving examples, rather than reading the textbook. Therefore, to get the most out of each lecture, you should read the relevant material before the lecture. Here is the list of what I expect to cover in each lecture. This is subject to change - make sure to check regularly for changes.

    Week beginning Monday Wednesday Friday
    5th September LABOUR DAY Introduction and Preliminaries
    9 Aggregate Loss Models:
  • 9.1 Introduction
    •
  • 9.2 Model choices
  • 9.3 The compound model for aggregate claims
    •
    12th September
  • 9.4 Analytic results
  • 9.5 Computing the aggregate claims distribution
    •
  • 9.6 the recursive method
    •
  • 9.6.1 Applications to compound frequency models
  • 9.6.2 Overflow/Underflow problems
    		•
    19th September
  • 9.6.3 Numerical stability
  • 9.6.4 Continuous severity
  • 9.6.5 Constructing arithmetic distributions
    		•
  • 9.7 The impact of individual policy modifications on aggregate payments
    •
  • 9.8 The individual risk model
    •
    26th September
  • 9.8 The individual risk model (cont.)
    • 11 Estimation for complete data:
  • 11.2 The empirical distribution for complete, individual data
  • 11.2 The empirical distribution for complete, individual data (cont.)
  • 11.3 Empirical distributions for grouped data
    • 12 Estimation for modified data:
  • 12.1 Point estimation
  • 12.2 Means, variances and interval estimation
    •
    3rd October
  • 12.2 Means, variances and interval estimation (cont.)
  • 12.3 Kernel density models
    •
  • 12.3 Kernel density models (cont.)
  • 12.4 Approximations for large data sets
    •
  • 12.4 Approximations for large data sets (cont.)
    • 15 Bayesian estimation
  • 15.2 Inference and prediction
    •
    10th October THANKSGIVING
  • 15.2 Inference and prediction (cont.)
  • 15.3 Conjugate priors and the linear exponential distribution
    •  Revision chapters 9, 11, 12, 15     		Revision chapters 9, 11, 12, 15
    17th October Revision chapters 9, 11, 12, 15

    MIDTERM

    EXAMINATION

    		 16 Model selection
  • 16.3 Graphical comparison of density and distribution functions
    •
    24th October
  • 16.4 Hypothesis tests
    •
  • 16.4 Hypothesis tests (cont.)
  • 16.5 Model Selection
    •      		17 Introduction and limited fluctuation credibility
  • 17.2 Limited fluctuation credibility theory
  • 17.3 Full credibility
    •
    31st October
  • 17.4 Partial credibility
  • 17.5 Problems with this approach
    •      		18 Greatest accuracy credibility
  • 18.2 Conditional distributions and expectation
  • 18.3 Bayesian methodology
    •
  • 18.4 The credibility premium
  • 18.5 The Buhlmann model
    •
    7th November STUDY BREAK
    14th November
  • 18.5 The Buhlmann model (cont.)
  • 18.6 The Buhlmann-Straub model
  • 18.7 exact credibility
    •
  • 18.7 exact credibility (cont.)
    • 19 Empirical Bayes parameter estimation
  • 19.2 Nonparametric estimation
    •
  • 19.2 Nonparametric estimation (cont.)
  • 19.3 Semiparametric estimation
    •
    21st November 20 Simulation
  • 20.1 Basics of Simulation
  • 20.2 Simulation for specific distributions
    •
  • 20.2 Simulation for specific distributions (cont.)
    •
  • 20.3 Determining the sample size
    •
    28th November
  • 20.4 Examples of simulation in actuarial modelling
    •      		Revision Revision
    5th December Revision (Also on Tuesday 6th December)

    Homework

    Assignment 1 Due Friday 30th September. Model Solutions
    Assignment 2 Due Friday 7th October. Model Solutions
    Assignment 3 Due Friday 14th October. Model Solutions
    Assignment 4 Due Wednesday 26th October. Model Solutions
    Assignment 5 Due Friday 4th November. Model Solutions
    Assignment 6 Due Friday 18th November. Model Solutions
    Assignment 7 Due Friday 25th November. Model Solutions
    Assignment 8 Due Friday 2nd December. Model Solutions