SDST3908 Credibility theory and loss modelling (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof M Hofert, SCDS (Department of Statistics and Actuarial Science) < mhofert@hku.hk >
Teachers Involved (Prof M Hofert,Statistics & Actuarial Science)
Course Objectives Credibility theory concerns the construction of statistical estimators in order to obtain more reliable ones, for example for premium calculations.  Different approaches to credibility theory are introduced (limited fluctuation, Bayesian, greatest accuracy). Loss modelling is at the core of actuarial science and parametric and nonparametric methods for point and interval estimation, as well as model assessment and selection are covered.
Course Contents & Topics Limited fluctuation approach (full credibility, partial credibility); Bayesian methodology and credibility (conditioning, Bayesian modelling, pure and Bayesian premium); greatest accuracy credibility (Buhlmann model, Buhlmann--Straub model); mathematical statistics (point estimation, interval estimation); parametric estimation (method of moment, quantile matching, maximum likelihood); nonparametric estimation (empirical distribution, survival and hazard rate function, kernel density); modified data (grouped, truncated, censored); model assessment and selection.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand limited fluctuation credibility (full and partial credibility) and greatest accuracy credibility (Buhlmann and Buhlmann-Straub)
CLO 2 learn about Bayesian methodology
CLO 3 understand main concepts of mathematical statistics
CLO 4 learn about parametric and nonparametric estimation
CLO 5 understand data modifications (grouping, truncation, censoring)
CLO 6 learn about model assessment and selection
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST2602 or SDST3902 or SDST3906
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 BSc in Actuarial Science ( Core/Compulsory )
2024 Minor in Actuarial Studies ( Disciplinary Elective )
2023 BSc in Actuarial Science ( Core/Compulsory )
2023 Minor in Actuarial Studies ( Disciplinary Elective )
2022 BSc in Actuarial Science ( Core/Compulsory )
2022 Minor in Actuarial Studies ( Disciplinary Elective )
2021 BSc in Actuarial Science ( Core/Compulsory )
2021 Minor in Actuarial Studies ( Disciplinary Elective )
Course to PLO Mapping 2024 BSc in Actuarial Science < PLO 1,2,3,4,5 >
2023 BSc in Actuarial Science < PLO 1,2,3,4,5 >
2022 BSc in Actuarial Science < PLO 1,2,3,4,5 >
2021 BSc in Actuarial Science < PLO 1,2,3,4,5 >
Offer in 2025 - 2026 Y        2nd sem    Examination May     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 40.0 1,2,3,4,5,6
Examination One 2-hour written examination 60.0 1,2,3,4,5,6
Required/recommended reading
and online materials		 Klugman S. A., Panjer H. H., & Willmot G. E.: Loss Models: From Data to Decisions (John Wiley & Sons, 2019, 5th edition).
Course Website http://moodle.hku.hk
Additional Course Information NIL