Curriculum

This course features studies in derivative pricing theory and focuses on building basic financial theory and their applications to various derivative products. A working knowledge of probability theory, stochastic calculus, knowledge of ordinary and partial differential equations and familiarity with the basic financial instruments is assumed. The topics covered in this course include but are not limited to binomial pricing models; continuous time limits; the Black-Scholes model; the Greeks and hedging; European, American, Asian, barrier and other path-dependent options; short rate models and interest rate derivatives; convertible bonds; stochastic volatility and volatility derivatives; currency and commodity derivative.

This course introduces the principles and practices of applied data science and machine learning in the context of finance and insurance. Topics include data extraction from structured and unstructured sources (text, images, audio, and geospatial data), data cleaning, integration, and transformation, feature engineering, and exploratory analysis. Students will learn frameworks for model development and monitoring, including handling missing data, categorical encoding, variable and model selection, and lifecycle management.
The course also includes an introduction to Generalized Linear Models (GLMs). The course places emphasis on data preparation and the overall model lifecycle, providing the foundation for model-building techniques covered in Data Science and Machine Learning II. Reproducible, code-driven workflows using Python and SQL are emphasized, supported by case studies from financial analytics and insurance applications.

An overview of methods and problems in the analysis of time series data related to finance and insurance. The course will focus on both theory and application with real datasets using R and Python and will require writing reports. Topics include stationary processes, linear processes; elements of inference in time and frequency domains with applications; ARMA, ARIMA, SARIMA, ARCH, GARCH; filtering and smoothing time-series; and State-space models.

This course provides an overview of the key statistical tools, methodologies, and algorithms used in life insurance and property and casualty insurance with a focus on numerical implementation and data applications. The life insurance component covers topics including stochastic mortality modelling and evaluation of (joint) life products via Markov Chains. The property and casualty component focuses on data driven rate making methods and stochastic pricing and reserving techniques.

This course focuses on data science techniques for risk modelling stemming from finance and insurance, including maximum likelihood estimation, expectation maximization, generalized linear and additive models, mixture models, hidden Markov models, artificial neural networks, and reinforcement learning.

This course features studies in the risks, and how to quantify and manage those risk, in financial and mortality linked insurance products. Topics include hedging of guarantees embedded in equity-linked insurance and annuity products, asset-liability management, determination of regulatory and economic capitals, insurance securitization (life & P/C), longevity bonds and derivatives, reinsurance, catastrophe bonds and derivatives.

This course extends over the fall/winter semesters and will feature invited guest speakers delivering both academic and practical seminars on current aspects of finance and insurance modeling, pensions, valuation risk management, regulation, and accounting.

This course takes cases from a variety of problems in the financial and insurance worlds and students will work in groups to develop both the theory and implementation of cases, draft reports, and deliver presentations on their findings. The course will be led by industry practitioners. Sample topics include Solvency II, Pension Benefits Act, valuing and managing complex annuity riders.

This course provides a rigorous and hands-on exploration of numerical techniques used to model complex systems in finance and insurance. Students will learn how to simulate and analyze stochastic processes that underlie asset prices, risk dynamics, and actuarial models. Key topics include the generation of random variables, simulation of stochastic differential equations, and advanced Monte Carlo methods such as variance reduction, multilevel and importance sampling, least-squares Monte Carlo, and Markov chain Monte Carlo for Bayesian inference. Emphasis is placed on both the theoretical foundations and practical implementation of these methods in real-world financial and insurance contexts.

As part of their required courses, students will select two out of three course options: STA 2503, STA 2535, and a STA 2000-level course. Selection of the 2000-level course requires MFI Program Director approval.