Students will participate in weekly sessions on relevant topics, including (but not limited to): Ethics, Career development (career leader, mock interviews, professional designations), Certifications (Bloomberg, excel, or other industry related software), Ethics and leadership (public speaking, presentations, teamwork, etc.). Additionally, the modules will feature industry experts who will provide background on the frontiers of the industry and share advice on careers and what to expect as a quantitative analyst.

This course is a practical introduction to statistical techniques which are important to the study of quantitative techniques for portfolio management. In particular, we will cover such things as time series analysis, such as ARIMA, GARCH, regression, cointegration, MCMC, resampling, copula, PCA, Factor models etc, with a particular focus on models and techniques that are prevalent in modern quantitative investment management. While the course is intended to be technical in nature, the focus will generally be on applications and implementations of models in the R language as opposed to theoretical considerations and derivations.

This course is a practical to introduction financial derivatives. In particular, we will cover such things as Introduction to derivatives, forwards/futures vs. options; basic option strategies; put-call parity, futures & forwards pricing by arbitrage, options  the binomial model (European & American/early exercise), basic stochastic processes, Brownian motion, Geometric Brownian motion, Ito calculus, The Black Scholes PDE & the Black Scholes formula  derivations & use for pricing, hedging & the Greeks in the Black Scholes model, Numerical methods for option pricing & hedging, Implied volatility, advanced options, Bermudan option, Asian options, lookback options, binary options, barrier options, stochastic volatility & volatility derivatives, hedging errors when volatility is stochastic, preview of advanced models. The focus will generally be on applications and implementations of models in the R language as opposed to theoretical considerations and derivations.

This course is a practical introduction to quantitative techniques for institutional portfolio management. In particular, we will cover such things as portfolio construction and optimization, active portfolio management and forecasting, as well as risk measurement and the budgeting of risk in portfolio management, with a particular focus on models and techniques that are prevalent in modern institutional investment management. While the course is intended to be technical in nature, the focus will generally be on applications and implementations of models in the R language as opposed to theoretical considerations and derivations.

This course will build on the 1^st professional development module by continuing to work on leadership skills, career development, and the practical application of quantitative techniques in investment management.  You will travel to some of the major Canadian financial centers to meet practitioners and begin building your professional networks.  You will also continue developing your professional speaking and presentation skills through the Toastmasters International Speechcraft program and applying thse skills by participating in quantitative university competitions (i.e., algo trading).

This course is a practical introduction to fixed income securities and interest rate derivatives. In particular, we will cover such things as yields & discount factors; spot & forward rates, curve fitting (bootstrapping & Nelson Siegel), bond pricing, pricing of certain cash flows, duration, convexity, key rate duration, hedging & immunization, interest rate forwards, futures & swaps, models of the short rate (Vasicek, Hull White), interest rate tree methods (Ho Lee, BDT), pricing interest rate derivatives (swaptions), credit risk, credit spreads, Black-Scholes-Merton/KMV style default risk etc. The focus will generally be on applications and implementations of models in the R language as opposed to theoretical considerations and derivations.

This course is a practical introduction financial and portfolio risk management. In particular, we will cover such things as loss distributions, risk measures, and risk aggregation and allocation principles, credit risk. The focus will generally be on applications and implementations of models in the R language as opposed to theoretical considerations and derivations.

This course is a practical introduction to algorithmic trading (AT) in the financial market. We will consider both algorithmic order execution and automated trading (such as high frequency trading (HFT)). In particular, we will cover such topics as financial market microstructure, order types, order-driven vs quote-driven markets, low-frequency, regularly spaced data vs high-frequency (or tick-by-tick) irregularly spaced data, limit order books, order execution algorithms and strategies, and high frequency trading. We will introduce models and techniques that are prevalent in modern institutional and retail investment management. The focus will be on applications and implementations of models in the R language as opposed to theoretical considerations and derivations. Quantitative analysis and computer programming will be emphasized.

This course presents experiential learning of actual quantitative portfolio management as it is conducted in the financial industry. Under the guidance of faculty advisors, students will manage a multi-million-dollar portfolio within the investment policies and procedures of the fund. Requires detailed analysis of macroeconomic, industry, and company fundamentals, using industry standard quantitative techniques in forecasting both return and risk as well as constructing portfolios. Entails preparation, on a regular basis, of up-to-date reports and presentations of portfolio analysis, selection, and management.

Requires students to demonstrate their ability by applying and integrating the knowledge gained in the MQIM program by completing either a research paper or an internship under the supervision of a faculty member.