The graduate diploma in financial engineering is designed for graduate students in the Department of Statistics and Actuarial Science who would like to develop applied skills in the field of finance, and for students in the MSc finance program seeking to deepen their theoretical understanding of relevant statistical and mathematical concepts so as to prepare students for careers in quantitative finance.
Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar.
The program consists of required courses and elective courses for a minimum of 22 units. For those students with a limited background in finance/economics, preparatory courses offered by the Beedie School of Business may also be required.
Students must complete all of
An introductory course in derivative securities that includes pricing as well as the use of derivative securities in portfolio management and structured transactions.
and one of
An introduction to stochastic models for the rate of return. Time series. Stochastic differential equations. Covariance equivalence principle. Applications. Prerequisite: Permission of the Department. Students with credit for ACMA 820 may not take this course for further credit.
Life insurance models. Interest rate models for life insurance: time series, stochastic differential equations, estimation. Portfolios of identical policies. Diversified portfolios. Prerequisite: ACMA 320.
and two of
Study the distribution of aggregate claims and introduce stochastic claims reserving methods in insurance. Individual versus collective models. Standard distribution-free methods. Other models. Prerequisite: Permission of the Department. Students with credit for ACMA 821 may not take this course for further credit.
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.
Tu, Th 11:30 AM – 1:20 PM
AQ 5008, Burnaby
Advanced mathematical statistics for PhD students. Topics in probability theory including densities, expectation and random vectors and matrices are covered. The theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theoretical framework of hypothesis testing is covered. Additional topics that may be covered include modes of convergence, central limit theorems for averages and medians, large sample theory and empirical processes. Prerequisite: STAT 830 or permission from the instructor.
Application of stochastic processes: queues, inventories, counters, etc. Reliability and life testing. Point processes. Simulation. Students with credit for STAT 870 may not take this course for further credit.
An introduction to smoothing and modelling of functional data. Basis expansion methods, functional regression models and derivative estimation are covered. Prerequisite: STAT 830 or permission of the 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.
and one of
A survey of asset pricing models including linear factor models, CAPM, and arbitrage models. Multi-period consumption, portfolio choice, and asset pricing models; continuous-time consumption and portfolio choice; behavioral finance and asset pricing; asset pricing with differential information.
The term structure of interest rates, fixed income returns, yield-spread analysis, sources of risk in fixed income securities, and embedded options.
Computational tools for financial analysis, financial engineering and risk management.
Credit risk management with emphasis on portfolio models, including probability of default and loss given default models, credit capital allocation, active portfolio management, credit derivatives, and structured transactions.
Value at risk, advanced market risk models, statistical models, stress testing, scenario analysis, and risk-adjusted performance measurement.
and one or more elective courses from the above lists to meet the overall minimum required units
Students may apply some courses completed for one credential towards this credential as outlined in graduate regulation 1.7.6. Normally students must complete a minimum of four additional courses beyond their MSc program to be awarded this diploma.
Students are expected to complete the program requirements in one to two terms past the expected time to complete the master's program.
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.