ACSC/STAT 4703 - Fall 2015

Actuarial Models II

This is the page where I post material related to the ACSC/STAT 4703 course I am teaching in FALL 2015.

 

  • Office hours: Monday 10:30-11:30, Wednesday 10:30-11:30, Thursday 13:00-14:00
  • Office: 202 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.
  • 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: TBA. 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
    7th September Introduction and Preliminaries
    9 Aggregate Loss Models:
  • 9.1 Introduction
    •
    14th September
  • 9.2 Model choices
  • 9.3 The compound model for aggregate claims
    •
  • 9.4 Analytic results
  • 9.5 Computing the aggregate claims distribution
    •
  • 9.6 the recursive method
    •
    21st September
  • 9.6.1 Applications to compound frequency models
  • 9.6.2 Overflow/Underflow problems
    		•
  • 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
    •
    28th September
  • 9.8 The individual risk model
    •
  • 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
    • 5th October
  • 11.3 Empirical distributions for grouped data
    • 12 Estimation for modified data:
  • 12.1 Point estimation
  • 12.2 Means, variances and interval estimation
    •
  • 12.2 Means, variances and interval estimation (cont.)
  • 12.3 Kernel density models
    •
    12th October THANKSGIVING
  • 12.3 Kernel density models (cont.)
  • 12.4 Approximations for large data sets
    • 15 Bayesian estimation
  • 15.2 Inference and prediction
    •
  • 15.3 Conjugate priors and the linear exponential distribution
    •
    19th October Revision chapters 9, 11, 12, 15 Revision chapters 9, 11, 12, 15 Revision chapters 9, 11, 12, 15
    26th October

    MIDTERM

    EXAMINATION

    		 16 Model selection
  • 16.3 Graphical comparison of density and distribution functions
  • 16.4 Hypothesis tests
    •
  • 16.4 Hypothesis tests (cont.)
    •
    2nd November 17 Introduction and limited fluctuation credibility
  • 17.2 Limited fluctuation credibility theory
  • 17.3 Full credibility
    •
  • 17.4 Partial credibility
  • 17.5 Problems with this approach
    •      		18 Greatest accuracy credibility
  • 18.2 Conditional distributions and expectation
  • 18.3 Bayesian methodology
    •
    9th November
  • 18.4 The credibility premium
  • 18.5 The Buhlmann model
    •      		REMEMBRANCE DAY
  • 18.5 The Buhlmann model (cont.)
  • 18.6 The Buhlmann-Straub model
  • 18.7 exact credibility
    •
    16th November
  • 18.7 exact credibility (cont.)
    • 19 Empirical Bayes parameter estimation
  • 19.2 Nonparametric estimation
    •      		19 Empirical Bayes parameter estimation
  • 19.2 Nonparametric estimation (cont.)
  • 19.3 Semiparametric estimation
    •      		20 Simulation
  • 20.1 Basics of Simulation
  • 20.2 Simulation for specific distributions
    •
    23rd November
  • 20.2 Simulation for specific distributions (cont.)
    •
  • 20.3 Determining the sample size
    •
  • 20.4 Examples of simulation in actuarial modelling
    •
    30th November Revision Revision Revision
    7th December Revision

    Homework

    Assignment 1 Due Friday 2nd October. Model Solutions
    Assignment 2 Due Friday 9th October. Model Solutions
    Assignment 3 Due Friday 16th October. Model Solutions
    Assignment 4 Due Wednesday 28th October. Model Solutions
    Assignment 5 Due Friday 13th November. Model Solutions
    Assignment 6 Due Friday 20th November. Model Solutions
    Assignment 7 Due Friday 27th November. Model Solutions
    Assignment 8 Due Friday 4th December. Model Solutions