Course info
SMS 3363: RISK THEORY FOR ACTUARIAL SCIENCE
PRE-REQUISITES: SMS 3200: Probability & Statistics II
Purpose of the course
This course provides the aspects of statistics relevant to risk measurement in Actuarial work.
Expected outcomes of the course
At the end of the course, students should be able to;
i) Understand the individual risk model and the collective risk model, evaluate the distribution and expectation of the total claim amounts.
ii) Have some fundamental concepts of decision theory.
iii) Estimate the premium of a policyholder and the total claim amounts using the information of the claim amounts made in previous years.
iv) Calculate some commonly used risk measures and explain their use and limitation.
Course content:
Economics of insurance, individual risk models for a short term, collective risk models for single and extended periods. Ruin theory and applications. Analysis of risk/uncertainty: risk/ uncertainty concepts, situations measurement of risk in precise terms, incorporating risk in investment decisions, RAD (risk - adjusted discount rate), certainty - equivalent (Hillier models), probability distribution, decision trees, and simulation (Hertz's model) approaches. Statistical inference for loss distributions: point and interval estimation, hypothesis testing, simulation, model building and testing. Sensitivity analysis of loss distributions: binomial, Poisson, normal, gamma, log-normal, log-gamma, Pareto, generalized Pareto, negative binomial, beta, and compound Poisson.
Recommended Text Books:
1. Daykin, C &Pentikainen, Practical Risk Theory for Actuaries. Chapman and Hall, 1994.
2. Kluggman S A, and Panjer, H H, et al. Loss Models: From Data to Decisions.John-Wiley and Sons, 1998.
PRE-REQUISITES: SMS 3200: Probability & Statistics II
Purpose of the course
This course provides the aspects of statistics relevant to risk measurement in Actuarial work.
Expected outcomes of the course
At the end of the course, students should be able to;
i) Understand the individual risk model and the collective risk model, evaluate the distribution and expectation of the total claim amounts.
ii) Have some fundamental concepts of decision theory.
iii) Estimate the premium of a policyholder and the total claim amounts using the information of the claim amounts made in previous years.
iv) Calculate some commonly used risk measures and explain their use and limitation.
Course content:
Economics of insurance, individual risk models for a short term, collective risk models for single and extended periods. Ruin theory and applications. Analysis of risk/uncertainty: risk/ uncertainty concepts, situations measurement of risk in precise terms, incorporating risk in investment decisions, RAD (risk - adjusted discount rate), certainty - equivalent (Hillier models), probability distribution, decision trees, and simulation (Hertz's model) approaches. Statistical inference for loss distributions: point and interval estimation, hypothesis testing, simulation, model building and testing. Sensitivity analysis of loss distributions: binomial, Poisson, normal, gamma, log-normal, log-gamma, Pareto, generalized Pareto, negative binomial, beta, and compound Poisson.
Recommended Text Books:
1. Daykin, C &Pentikainen, Practical Risk Theory for Actuaries. Chapman and Hall, 1994.
2. Kluggman S A, and Panjer, H H, et al. Loss Models: From Data to Decisions.John-Wiley and Sons, 1998.
- Lecturer: Gathoni Nyambura Shalyne