24 undergraduate courses in the 2026–27 Undergraduate Calendar, and 39 graduate courses in the Fall 2026 Graduate Calendar.
Undergraduate
ACTSC 127Introduction to Global Capital Markets and Financial Analytics0.50 units
This course introduces financial markets and institutions, examining the role of finance in the global economy while introducing foundational principles of financial decision making. The course utilizes analytic and computational approaches to the topics, enabling students to develop data management and analysis competencies.
ACTSC 231Introductory Financial Mathematics0.50 units
The theory of rates of interest and discount including the theoretical continuous case of forces of interest and discount. Annuities and sinking funds, including the continuous case. Practical and theoretical applications primarily to mortgages and bonds. Yield rates.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
MATH137 - Calculus 1 for Honours Mathematics (0.50)
The future lifetime random variable: probability and survival functions; force of mortality; complete and curtate expectation of life; Makeham and Gompertz mortality laws. Life tables: characteristics of population and insurance life tables; selection; fractional age assumptions. Life insurance payments and annuity payments: present value random variables; expected present values; higher moments; actuarial notation. Annual, 1/mthly, and continuous cases. Relationships between insurance and annuity functions. Premiums: expense loadings. Present value of future loss random variables and distribution, net and gross cases. Equivalence principle. Portfolio percentile principle. Extra risks.
ACTSC 291Global Capital Markets and Financial Analytics0.50 units
This course offers an overview of global capital markets and asset valuation. Topics may include an overview of financial markets and instruments, time value of money, valuation of financial assets, and financial risk and portfolio management. The course utilizes an analytic and computational approach to the topics, enabling students to develop data management and analysis competencies.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
ACTSC127 - Introduction to Global Capital Markets and Financial Analytics (0.50)
AFM127 - Introduction to Global Capital Markets and Financial Analytics (0.50)
CFM101 - Introduction to Financial Markets and Data Analytics (0.50)
ACTSC 362Introduction to Property and Casualty Practice0.50 units
Introduction to property and casualty coverages. Claim payment process, development triangles, and diagnostic testing. Estimating ultimate claims using development, frequency-severity, expected and Bornhuetter Ferguson methods. Estimating unpaid claim and unallocated loss adjustment expense liabilities. Ratemaking process, including expense provisions, profit and contingencies, and adjustments for catastrophes and large losses. Pricing for deductibles and increased limits. Introduction to reinsurance pricing and reserving. Commercial lines pricing.
Prerequisites
Complete all of the following
Earned a minimum grade of 60% in each of the following:
ACTSC 363Casualty and Health Insurance Mathematics 10.50 units
Introduction to the collective risk model; models for loss frequency: (a, b, 0) and (a, b, 1) classes of distributions, compound distributions and mixtures; models for loss severity: exponential, gamma, lognormal, Pareto, Weibull, and mixtures; measures of distribution tails; impact of policy adjustments on loss frequency and severity; estimation of frequency and severity models.
ACTSC 372Investment Science and Corporate Finance0.50 units
Introduction to financial markets. Different return and risk measures. Investment rules and capital budgeting. Rigorous derivations of Markowitz portfolio optimization and its application in investment decisions. Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT). Weighted average cost of capital (WACC) and efficient market hypothesis (EMH). Long-term financing, capital structure (MM propositions), and dividend policies. Introduction to options, forwards, and swaps.
This is the first in a two-course sequence that deals with corporate financial decision making. Topics may include capital budgeting, cost of capital, security issuance, capital structure, payout policy and dividends, and short-term finance. Where suitable, topics are treated from a mathematical and quantitative perspective.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
ACTSC291 - Global Capital Markets and Financial Analytics (0.50)
AFM272 - Global Capital Markets and Financial Analytics (0.50)
ACTSC 423Topics in Financial Econometrics0.50 units
This course introduces the use of advanced econometric/statistical methods in studying financial market data, and in quantitatively assessing risks associated with financial investments. The methods presented in this course are tailored to address specific issues of interest in finance, such as the quantification of the risk-return tradeoff, the modelling of time-varying stock/bond market volatility and, possibly, also the statistical analysis of financial derivatives such as options.
Prerequisites
Must have completed at least 1 of the following:
AFM323 - Quantitative Foundations for Finance (0.50)
Credibility theory: American credibility, Bayesian, Bühlmann, Bühlmann-Straub, and empirical Bayes parameter estimation. Risk theory: claim arrival dynamics, surplus models, first-passage times, applications to solvency, and their analytical and numerical analyses.
Prerequisites
Complete all of the following
Must have completed the following:
ACTSC363 - Casualty and Health Insurance Mathematics 1 (0.50)
ACTSC 445Quantitative Enterprise Risk Management0.50 units
This course introduces enterprise risk management, with a focus on quantitative analysis and economic capital. Risk classification is first discussed with an emphasis on the types of risk most suited to quantitative methods. Risk measures, such as Value-at-Risk (VaR) and Conditional Tail Expectation (CTE or TVaR), are then introduced and their use by firms and regulators to determine risk capital requirements is further highlighted. Different approaches are considered for developing loss distributions, including frequency/severity analysis and extreme value theory. Copulas and economic scenario generators are used to aggregate dependent risks. Different strategies for mitigating or transferring risk are reviewed. Additional topics that may be covered include credit risk, capital allocation, and regulation of financial institutions.
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed the following:
ACTSC372 - Investment Science and Corporate Finance (0.50)
ACTSC 446Mathematics of Financial Markets0.50 units
This course covers mathematical techniques for no-arbitrage pricing and hedging financial derivatives. Topics to be covered can be classified into three broad areas: derivatives markets (options; forwards and futures; other derivatives; put-call parity), discrete-time financial models (binomial models; general multi-period models; Fundamental Theorems of Asset Pricing; risk-neutral probability), and continuous-time financial models (basic stochastic calculus and Itô's lemma; Black-Scholes model; interest rate models and bond pricing).
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed the following:
ACTSC372 - Investment Science and Corporate Finance (0.50)
ACTSC 447Numerical Computation for Financial Modelling0.50 units
The interaction of financial models, numerical methods, and computing environments. Basic computational aspects of option pricing and hedging. Numerical methods for stochastic differential equations, strong and weak convergence. Generating correlated random numbers. Time-stepping methods. Finite difference methods for the Black-Scholes equation. Discretization, stability, convergence. Methods for portfolio optimization, effect of data errors on portfolio weights.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
AMATH242 - Introduction to Computational Mathematics (0.50)
Theory and practice of pension plan funding. Assumptions, basic actuarial functions, and population theory applied to private pensions. Concepts of normal costs, supplemental liability, unfunded liability arising from individual accrued benefit, and projected benefit cost methods.
ACTSC 454Longevity and Mortality Using Predictive Analytics0.50 units
Kaplan-Meier and Nelson-Aalen estimators for survival functions. Kernel density models. Validation of mortality tables. Estimators for Markov multiple state transition intensities. Longevity models, including deterministic and stochastic models such as Lee-Carter and Cairns-Blake-Dowd.
Profit testing for traditional and non-traditional life insurance. Pricing and valuation of embedded options in life insurance products. Defined benefit and defined contribution pension plan design. Theory and practice of unit credit methods for pension plan funding and valuation for final average salary, career average earnings, and career average revalued earnings pension plans; post-retirement health benefits.
Prerequisites
Complete all of the following
Earned a minimum grade of 60% in each of the following:
ACTSC 456Statistical Learning in Actuarial Science0.50 units
This course explores a broad range of statistical learning models used to analyze data in actuarial and financial contexts. It covers techniques from both supervised and unsupervised learning, with a focus on practical applications. Specific topics covered include modelling principles and practice, advanced and regularized regression models, cross-validation, traditional classification models (logistic regression, LDA, QDA, KNN), decision trees (bagging, boosting, random forests), dimensionality reduction (PCA), hierarchical and K-means clustering, and an introduction to neural networks with applications to supervised and unsupervised learning problems. Some extensions to the above models may also be considered.
ACTSC 457Predictive Modelling in Property and Casualty Insurance0.50 units
This course introduces the fundamentals of predictive modelling with a focus on applications in loss modelling and pricing in the property and casualty insurance industry. The course centres on a case study approach to develop comprehensive pricing models using realistic, large-scale insurance data. The course also emphasizes practical skills in data cleansing, exploratory analysis, model development and validation.
Prerequisites
Complete all of the following
Must have completed the following:
ACTSC363 - Casualty and Health Insurance Mathematics 1 (0.50)
Time value of money; simple and compound interest and discount; real returns; equations of value; loan schedules; valuation of fixed coupon bonds; valuation of real return bonds; term structure of interest rates; no arbitrage pricing; valuation of forward contracts; valuation of interest rate swaps. Duration and Immunization
Models for future lifetime; insurance and annuity functions; life tables and their use; future loss random variable for a contract; calculations of premiums and reserves; standard international actuarial notation.
ACTSC 613Statistics for Actuarial Science0.50 units
Discrete and continuous random variables; generating functions; dependence; maximum likelihood estimation, functions of random variables; confidence intervals, hypothesis tests; Bayesian estimation, simple linear regression.
ACTSC 614Corporate Finance and Accounting0.50 units
Principles of financial accounting; construction and analysis of accounts. Sustainability reporting. Financial planning; capital budgeting, assessment of capital projects; long term financing; short term finance and planning
Micro: Supply and demand; utility theory and risk aversion; production choices; competition; Macro: Fiscal and monetary policy; exchange rates; factors affecting inflation, unemployment, exchange rates and economic growth; introductory game theory; introduction to insurance economics.
Multiple state models; premiums and reserves for stat dependent policies, including joint life and last survivor benefits; cashflow projection methods; post retirement benefits: valuation and funding. Estimation and inference for survival models. Introduction to longevity models.
Generalized linear models: multiple linear regression and normal linear model; exponential family; link functions; linear predicators; estimation; testing. Time series: Univariate ARIMA; multivariate AR; ARCH and other non-linear models.
ACTSC 625Casualty and Health Insurance Mathematics0.50 units
Frequency and severity models; compound distributions, calculation of moments and probabilities using recursion; Bayesian estimation and credibility; claims reserving for non-life insurance using run-off triangle methods, introductory ruin theory.
ACTSC 632Data Science with Actuarial Applications0.50 units
This course provides a comprehensive treatment of various techniques from statistics, predictive analytics and machine learning that can be used to analyze data sets relevant for actuarial applications. Specific topics covered include: modeling principles and practice, analysis and estimation of survival models, insurance pricing using generalized linear models, classification and tree-based methods.
This course considers actuarial risk management in the context of practical applications in life and non-life insurance, and in pensions. Topics covered include the role of the actuary; methods for pricing and reserving in life and non-life insurance; regulatory capital; risk transfer through reinsurance or securitization; product development; model risk and governance; pension risk management; embedded options in life insurance.
This course combines enterprise and quantitative risk management to provide a comprehensive coverage of risk management science with applications in finance, insurance and corporate governance. Topics covered include: risk taxonomy; risk measures; extreme value theory; copulas; stress and scenario testing; interest rate risk management, credit risk management; regulation, including Basel, Basel 3 and Solvency 2; risk adjusted return; capital allocation.
ACTSC 845Quantitative Enterprise Risk Management0.50 units
This course introduces enterprise risk management, with a focus on quantitative analysis and economic capital. Risk classification is first discussed with an emphasis on the types of risk most suited to quantitative methods. Risk measures, such as Value-at-Risk (VaR) and Conditional Tail Expectation (CTE or TVaR), are then introduced, and their use by firms and regulators to determine risk capital requirements is further highlighted. Different approaches are considered for developing loss distributions, including frequency/severity analysis and extreme value theory. Copulas and economic scenario generators are used to aggregate dependent risks. Different strategies for mitigating or transferring risk are reviewed. Additional topics that may be covered include credit risk, capital allocation and regulation of financial institutions.
ACTSC 846Mathematics of Financial Markets0.50 units
This course covers mathematical techniques for no-arbitrage pricing and hedging financial derivatives. Topics to be covered can be classified into three broad ares: derivatives markets (options; forwards and futures; other derivatives; put-call parity), discrete-time financial models (binomial models; general multi-period models; fundamental theorems of asset pricing; risk-neutral probability), and continuous-time financial models (basic stochastic calculus and Ito's lemma; Black-Scholes model; interest rate models and bond pricing).
ACTSC 854Longevity and Mortality Using Predictive Analytics0.50 units
Kaplan-Meier and Nelson-Aalen estimators for survival functions. Kernel density models. Validation of mortality tables. Estimators for Markov multiple state transition intensities. Longevity models including deterministic and stochastic models such as Lee-Carter and Cairns-Blake-Dowd.
Profit testing for traditional and non-traditional life insurance. Pricing and valuation of embedded options in life insurance products. Defined benefit and defined contribution pension plan design. Theory and practice of unit credit methods for pension plan funding and valuation for final average salary, career average earnings, and career average revalued earnings pension plans; post-retirement health benefits.
ACTSC 856Statistical Learning in Actuarial Science0.50 units
This course explores a broad range of statistical learning models used to analyze data in actuarial and financial contexts. It covers techniques from both supervised and unsupervised learning, with a focus on practical applications. Specific topics covered include: modeling principles and practice, advanced and regularized regression models, cross-validation, traditional classification models (logistic regression, LDA, QDA, KNN), decision trees (bagging, boosting, random forests), dimensionality reduction (PCA), hierarchical and K-means clustering, and an introduction to neural networks with applications to supervised and unsupervised learning problems. Some extensions to the above models may also be considered.
ACTSC 895Communications in Actuarial Science0.50 units
This course is designed to develop students' oral and written communication skills, using examples from actuarial risk management. It includes elements of writing, and also reading and summarizing leading edge work in actuarial theory and practice. Presentations and collaborative work are used to develop essential communication skills in both academic and professional contexts.
This course acts as a capstone on the coursework based part of the PhD program and as a stepping stone to the PhD proposal and to research. It is aimed at developing research skills: critically reading published research, summarizing and synthesizing areas of research, writing and orally presenting summaries of research problems, data sets and theoretical and applied results. The course is designed to be integrative across core areas of the student discipline.
ACTSC 961Mathematical Methods of Loss Reserving0.50 units
Macro methods of runoff analysis: chain-ladder, least squares, separation, payment per claim incurred. Stochastic methods: Reid's method, see-saw, payment per unit of risk, autoregressive models, Kalman filter.
This course presents advanced mathematical and computational tools useful to study loss frequency and severity models, and also aggregate claims models. Analytic and recursive evaluation of compound distributions are discussed, as well as generalized linear models for frequency and severity models. Other topics covered include pricing and insurance risk processes.
The analysis of the development over time of the surplus on a portfolio of insurance business is considered. The classical Poisson and Sparre Andersen risk models are studied. Unified treatment of moments and distributions of quantities related to the event of ruin is done through a discounted penalty function approach. Random variables of interest include the time of ruin itself, the deficit immediately after ruin occurs, and the surplus immediately prior to ruin. Defective renewal equations and Laplace transforms are utilized extensively. Prerequisites are familiarity with aggregate loss models at the level of ActSc 431/831 or equivalent.
ACTSC 964Foundations of Quantitative Risk Management0.50 units
Fundamental concepts in quantitative risk management. Topics typically include: risk measures, extreme value theory, multivariate distributions and copulas. This course has a focus on mathematical and statistical techniques. Other topics may be covered at the discretion of the instructor.
Ruin Theory for heavy-tailed distributions. Fluctuation of maxima and upper order statistics. Extreme value distributions: Weibull, Frechet, Gumbel and generalized Pareto. Mean excess function. Statistical methods for external events. Estimation of parameters of extreme value and excess distributions. Applications in finance and insurance.
Mixed Poisson and nonhomogeneous birth processes for claim counts; analytic, recursive, asymptotic and approximate evaluation of compound distributions for aggregate claims; reliability concepts and analysis of stop-loss moments; applications for inflation, incurred but not reported claims, and infinite server queues. Prerequisites are familiarity with aggregate loss models at the level of ActSc 431/831 or equivalent.
ACTSC 967Foundations of Insurance Economics0.50 units
This course deals with the foundations of insurance economics and the theory of risk sharing in both centralized and decentralized insurance markets. The topics covered include the basics of the theory of choice under uncertainty and preference representations, risk aversion and measures of risk, Pareto optimality in insurance contracting under expected-utility and non-expected-utility models of preferences, as well as Pareto-optimal risk sharing in peer-to-peer insurance. Additional advanced topics, such as moral hazard and adverse selection in insurance contracting, may also be included.
ACTSC 969Stochastic Calculus for Quantitative Finance0.50 units
The course provides an introduction to Itô calculus and stochastic processes in the context of quantitative finance. Topics include: quadratic variation, Itô formula, Itô differential equations, Brownian motion, geometric Brownian motion, martingales, connection between diffusion processes and partial differential equations, Girsanov transform.
The course introduces options and other derivative securities in different asset classes. The main focus is on methods of pricing in a multi-period setting, but continuous-time models are also discussed. Topics may include no-arbitrage pricing theory, the fundamental theory of asset pricing, complete and incomplete markets,and pricing of complex financial instruments.
The course discusses methods and tools for modeling of financial derivatives in the continuous-time setting. Both theory and practical applications are discussed. The first part covers methods of pricing and hedging of derivatives under different assumptions about the dynamics of the underlying economic factors. Topics normally include currency derivatives, American and exotic options, futures contracts, stochastic volatility models and mean-variance hedging. The second part deals with modeling and pricing of interest-rate products. Topics may include short interest rate models, the Heath-Jarrow-Morton Framework, and Libor and swap market models.
The course will cover selected and advanced topics in quantitative finance and risk management, with a particular focus on current developments. Topics may include robust and Bayesian portfolio optimization, limits to arbitrage, derivatives pricing under model uncertainty, credit risk models, and models of systematic risk.
Basic optimization: quadratic minimization subject to leanear equality constraints. Effecient portfolios: the efficient frontier, the capital market line, Sharpe ratios and threshold returns. Practical portfolio optimization: short sales restrictions target portfolios, transactions costs. Quadratic programming theory. Special purpose quadratic programming algorithms for portfolio optimization: today's large investment firms expect to solve problems with at least 1000 assets, transactions costs and various side constraints in just a few minutes of computation time. This requires very specialized QP algorithms. An overview of such algorithms will be presented with computational results from commercial problems. The efficient frontier, the capital market line, Sharpe ratios and threshold returns in practice.
The focus of this course is on the statistical modelling, estimation and inference and forecasting of nonlinear financial time series, with a special emphasis on volatility and correlation of asset prices and returns. Topics to be covered normally include: review on distribution and dynamic behaviour of financial time series, univariate and multivariate GARCH processes, long-memory time-series processes, stochastic volatility models, modelling of extreme values, copulas, realized volatility and correlation modelling for ultra high frequency data and continuous time models.