ACTL3162 General Insurance Techniques
Assignment
Due time: Monday 9th October 2017 5 pm
1 Learning outcomes
The assignment aims particularly at developing the course learning outcomes
associated with Aim A and Aim B. It specifically assesses the program goals
“Knowledge”, “Problem solving and critical thinking”, as well as “Communi-
cation”. You are expected to demonstrate your ability to analyse an actuarial
problem, apply appropriate theories and logic to interpret the problem, and
develop solutions and conclusions. The communication of those will also be
assessed.
2 Assignment task
Task 1
You are an actuarial analyst for a general insurer who introduced a new
motor insurance product to the market just over one year ago. During this
time, the company has received over 1,000 claims and you now believe the
claims experience is significant enough for you to investigate the form of the
accident severity distribution. Some policy details are given below:
1. Every policy has a standard excess of $700. (A policyholder may have
multiple accidents in the year and may have reported more than one
claim. The excess is applied to each accident (or loss), not the total
loss in the year.)
2. In addition, policyholders can choose to add on an additional excess
in order to lower their premium. There are multiple levels for the
policyholder to choose from.
The claims data are stored in data.csv. This file contains claim amounts paid
by the insurer, along with the amount of any additional deductible elected
by the policyholder.
Your task is to use Maximum Likelihood Estimation (MLE) to fit an appro-
priate accident severity distribution (i.e. the total loss before deducting the
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excess) for individual claims. You are required to fit the Pareto, Log-normal
and Gamma distributions to the claims data and use appropriate goodness-
of-fit tests to decide and subsequently justify which of the three distributions
is the most appropriate to use for modelling the claim severity distribution.
In addition, you must briefly describe your methodology in reaching your
MLE estimates of your parameters. However, providing detailed mathemati-
cal formulas and code snippets is not necessary (but the entire R code (or the
code of other software if you are not using R) must be provided separately).
(Hint: The MLE estimates for parameters may not be analytically tractable
in this case! Consider using a numerical package in R to find the parameters.
Also, try various initial conditions for the optimisation in case you’re getting
errors.)
Task 2
Let Ct be the surplus of an insurer at time t, measured in months. Recall
that Ct is defined as
Ct = c0 +
t∑
i=1
(pii − Si), t = 0, 1, 2, . . . (1)
where Si ∼ Gamma(α, β) is the total loss for month i and pii = pi is the
monthly premium income, equal to the expected loss plus a loading of 20%.
This implies that the premium income is fixed month to month. Let ψt(c0)
denote the probability that ruin occurs within time t given initial surplus c0.
a) Derive an expression for ψ1(c0) in terms of the Gamma distribution func-
tion G.
b) Provide an explanation for the expression
ψ2(c0) =
∫ c0+pi
0
ψ1(c0 + pi − y)g(y;α, β)dy + 1−G(c0 + pi;α, β) (2)
where g and G are the Gamma density and distribution functions, respec-
tively.
In addition, evaluate this expression for α = 20 and β = 0.2 where c0 =
0, 10, 20, 50. (Hint: You may try to use the ‘integrate’ function in R!)
c) Explain the difficulty involved in calculating ψt(c0) for larger values of t.
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d) Suppose Si is replaced by a discrete distribution over the non-negative
integers 0, 1, 2, . . . . We call this new distribution S∗i with probability
mass function g∗ and distribution function G∗. We can now calculate the
surplus at time t as
C∗t = c0 +
t∑
i=1
(pii − S∗i ), t = 0, 1, 2, . . . (3)
Let ψ∗t (c0) denote the probability that ruin occurs within time t given
initial surplus c0 in the above model.
• Give an expression for ψ∗1(c0).
• Provide a recursive expression for ψ∗t (c0).
• Comment on the implementation of this expression in comparison to
the original case when Si was continuous.
e) Your task is now to approximate ψ2(c0) by ψ
∗
2(c0) by replacing S with S
∗
with various methods of discretisation discussed in the lecture slides of
Module 4. Use the same values of c0 as in part b).
(A) Method of rounding
(1) h = 1,m = 150
(2) h = 1,m = 300
(3) h = 5,m = 30
(4) h = 5,m = 60
(B) Method of upper and lower bounds
(1) d = 1
(2) d = 5
Above, d and h refers to the span while m refers to the number of discrete
intervals. For (B), you are required to calculate S∗ based on both the
method of upper and lower bounds.
Compare and comment on differences in your results relative to part b).
(Hint: You may try and find suitable R packages to help you do the
discretisation.)
Task 3
Let Si ∼ Gamma(α, β) where α = 5 and β = 0.8.
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a) • What is the distribution of Y5 =
∑5
i=1 Si?
• Compute the distribution function FY5(x) at x = 10, 20, 30, 40, 50.
b) Now, you will need to discretise Si and perform convolution using Panjer
recursion to approximate the distribution function of Y5.
• Evaluate this at the same values of x as in part a).
• Explain your methodology and justify any assumptions/decisions
you have made in the process of finding an adequate approximation.
(Hint: Try using the R package ‘actuar’.)
c) Compare the above results in a) and b) and comment on any differences.
Additional Instructions
• Answers are to be provided in Word or pdf format.
• Intermediate steps for questions involving any form of derivation are
required. Your comments and conclusions should be well justified and
charts should be used to support your conclusions where applicable.
• You are strongly recommended to use the software R for pro-
gramming, although the use of other software will also be accepted.
Some sample R codes for fitting are available on the course web site
which may be of use. In addition, feel free find packages online to
perform your computations (but always check that your answer is sen-
sible!).
• When making a comment or conclusion based on R outputs (or other
software outputs), you should include the relevant outputs in the main
body of your report. You should make sure that the marker can read
and understand your arguments and statements without referring to
the appendix.
• Your R codes (or codes of other software) should be included in the
appendix. The marker will choose a substantial portion of the reports
to check the code. He/she will need to copy the code, run it and check
whether it is correct, implementable and consistent with the output
presented in your answer. Students will risk failing the assign-
ment if the code cannot be run or the output provided in
the answer is not consistent with the output generated by the
code.
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• There is no page limit. However, you should think of a clear and
effective structure for your responses. Responses provided of excessive
lengths are explicitly penalised in the communications criteria.
• You should not
– Include programming codes in the main body of your report
– Have figures or tables that are not referred to or analysed in the
main body of your report
– Include materials that are not highly relevant in the main body
of your report
2.1 Communication skills
We recommend students to seek feedback from the EDU (although you need
to give them time to read your report) - connect to the EDU website on
Moodle “Write well; Learn deeply”. The student enrolment key is “ASB_LTP”.
2.2 Assignment submission procedure
Your assignment must be uploaded as a unique document (either pdf or
Word document) and all parts must be in portrait format. As long as the
due date is still future, you can resubmit your work; the previous version of
your assignment will be replaced by the new version.
Assignments must be submitted via the Turnitin submission box that is
available on the course Moodle website. Turnitin reports on any similarities
between their own cohort’s assignments, and also with regard to other sources
(such as the internet or all assignments submitted all around the world via
Turnitin). More information is available at: [click]. Please read this page, as
we will assume that you are familiar with its content.
Please note that the School of Risk and Actuarial Studies will apply the
following policy on late assignments. A penalty of 25% of the mark the
student would otherwise have obtained, for each full (or part) day of lateness
(e.g., 0 day 1 minute = 25% penalty, 2 days 21 hours = 75% penalty).
Students who are late must submit their assessment item to the LIC via e-
mail. The LIC will then upload documents to the relevant submission boxes.
The date and time of reception of the e-mail determines the submission time
for the purposes of calculating the penalty.
You need to check your document once it is submitted (check it on-screen).
We will not mark assignments that cannot be read on screen.
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Students are reminded of the risk that technical issues may delay or
even prevent their submission (such as internet connection and/or computer
breakdowns). Students should then consider either submitting their assign-
ment from the university computer rooms or allow enough time (at least
24 hours is recommended) between their submission and the due
time. The Turnitin module will not let you submit a late report. No paper
copy will be either accepted or graded.
In case of a technical problem, the full document must be submitted to
the LIC before the due time by e-mail, with explanations about why the
student was not able to submit on time. In principle, this assignment will
not be marked. It is only in exceptional circumstances where the assignment
was submitted before the due time by e-mail that it may be marked—and
this only if a valid reason is established (and the LIC has the discretion in
deciding whether a given reason is valid).
2.3 Plagiarism awareness
Students are reminded that the work they submit must be their own. While
we have no problem with students discussing assignment problems if they
wish, the material students submit for assessment must be their own. In
particular, this means that any code you present are from your own computer,
which you yourself developed, without any reference to any other student’s
work.
While some small elements of code are likely to be similar, big patches
of identical code (even with different variable names, layout, or comments—
Turnitin picks this up) will be considered as plagiarism. The best strategy to
avoid any problem is not to share bits and pieces of code with other student
outside your group.
Note however that you are allowed to use any R code that was made
available during the course (either with the lectures or developed in the
tutorial exercises). You don’t need to reference them formally, and this will
not be considered as plagiarism.
Students should make sure they understand what plagiarism is—cases of
plagiarism have a very high probability of being discovered. For issues of
collective work, having different persons marking the assignment does not
decrease this probability.
Students should consult the “Write well; Learn deeply” website and con-
sult the resources provided there. In particular, all students should do the
quiz about plagiarism to make sure they know how to avoid any issue. For in-
stance, did you know that sharing any part of your work with other students
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(outside your group) before the deadline is already considered as plagiarism?1
3 Assessment criteria
Please see the file, “Rubric”.
1Yes, that’s right, just sending it, even if the third party promises not to copy, is already
plagiarism in the UNSW policy!
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