The master of science (MSc) in actuarial science program provides advanced education and research training that prepares students for a career in industry or to continue on to PhD studies. The program offers exposure to current applied and theoretical topics generally not covered by the professional exam syllabus. Students have the opportunity to gain work experience through co-operative education.
Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar.
This program consists of required courses, elective courses, and a project for a minimum of 36 units (at least 24 units of course work must be at the graduate level). Undergraduate courses used to meet the program requirements, if any, will not be included in the program cumulative grade point average (CGPA). Students who have completed the undergraduate actuarial science major or honours program at SFU, or have received approval of the graduate program chair based on an equivalent program, are required to complete 24 graduate course units plus 6 project units for 30 units in total.
The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Prerequisite: STAT 450 or permission of the instructor. Students with credit for STAT 801 may not take this course for further credit.
and two of
Life insurance models. Interest rate models for life insurance: time series, stochastic differential equations, estimation. Portfolios of identical policies. Diversified portfolios. Prerequisite: ACMA 320.
Advanced non-life insurance mathematics. Individual risk models, collective risk models, ruin models. Actuarial reserve models: Bonus-malus system, IBNR techniques. Generalized linear models in Actuarial Science. Prerequisite: ACMA 335.
Actuarial risks. Insurance premium calculation principles and properties. Risk measures and ordering. Applications. Prerequisite: ACMA 335.
and two of
A modern approach to normal theory for general linear models including models with random effects and "messy" data. Topics include experimental units, blocking, theory of quadratic forms, linear contrasts, analysis of covariance, heterogeneous variances, factorial treatment structures, means comparisons, missing data, multi-unit designs, pseudoreplication, repeated measures mixed model formulation and estimation and inference. Prerequisite: STAT 350 or equivalent.
The theory and application of statistical methodology for analyzing non-normal responses. Special emphasis on contingency tables, logistic regression, and log-linear models. Other topics can include mixed-effects models and models for overdispersed data. Prerequisite: STAT 830 and STAT 850 or permission of instructor.
An advanced treatment of modern methods of multivariate statistics and non-parametric regression. Topics may include: (1) dimension reduction techniques such as principal component analysis, multidimensional scaling and related extensions; (2) classification and clustering methods; (3) modern regression techniques such as generalized additive models, Gaussian process regression and splines. Prerequisite: STAT 830 and STAT 853 or permission of instructor.
An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed. Prerequisite: STAT 830 or equivalent or permission of instructor.
Statistical methodology used in analysing failure time data. Likelihoods under various censoring patterns. Inference using parametric regression models including the exponential, Weibull, lognormal, generalized gamma distributions. Goodness-of-fit tests. The proportional hazards family, and inference under the proportional hazards model. Stratification and blocking in proportional hazards models. Time dependent covariates. Regression methods for grouped data. Prerequisite: STAT 450. Students with credit for STAT 806 may not take this course for further credit.
Methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including unbalanced and incomplete data sets, characteristic of biomedical research are covered. Topics include covariance pattern models, random or mixed-effects models, multilevel models, generalized estimating equations, inference for multistate processes and counting processes, and methods for handling missing data. Prerequisite: STAT 450 or permission of the instructor.
The theory and application of statistical approaches for the analysis of spatial and time dependent data. Topics will include: point pattern analysis, spatial autocorrelation analysis, geostatistics, lattice processes, modeling spatial count and binary data, spatio-temporal models and time series analysis. Prerequisite: STAT 830 or permission of the instructor.
and four additional graduate units
Other courses may be substituted for these courses with senior supervisor and graduate program chair approval.
and a project
Students are required to submit and successfully defend a project based on a statistical analysis problem or on the development of new statistical methodology. The project is examined as a thesis and must be submitted to the library.
All students are required to submit and successfully defend a project based on an actuarial science problem. The project is examined as a thesis and must be submitted to the library. See the Graduate General Regulations Section 1.10 and 1.11 for further information.
Students are expected to complete the program requirements in five terms. The course work typically takes three terms, and the project, including the defence, usually takes two terms.
Diploma in Financial Engineering
The Diploma in Financial Engineering is designed for students in the MSc program who would like to develop applied skills in the field of finance.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.