36 graduate courses in Waterloo’s course catalog for Spring 2021, from UW Open Data. uwloo can’t read that term’s Graduate Calendar, so this stands in for it; the calendar itself is in Waterloo’s archive.
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.
Discrete and continuous random variables; generating functions; dependence; maximum likelihood estimation, functions of random variables; confidence intervals, hypothesis tests; Bayesian estimation, simple linear regression.
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 Mathematics
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.
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.
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.
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 Analytics
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.
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.
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.
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.
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.