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Students will go through sessions on but not limited to: Career development (career leader, mock interviews, professional designations), Certifications (Bloomberg, Excel, etc.), Ethics, seminars by invited speakers providing background on what to expect as a quantitative analyst and what are the frontiers of the industry. Students will be required to review a quantitative reading and present on it in one of the semesters following Toastmasters approach.
Time series analysis, such as ARIMA, GARCH, regression, co-integration, MCMC resampling, copula, PCA, Factor models.
Basic theory of derivatives with an emphasis on computational implementation via R language.
The course will cover the theory, analytic methods, and computational techniques currently employed in the business of investment management. Intended to be highly quantitative, the content will include topics drawn from the fields of utility theory, asset pricing, portfolio optimization, active portfolio management, and risk modeling. The course is intended to be applied in nature, and will have a significant computational component using the R language for statistical computing and various financial industry add-on packages. A particular focus will be on the implementation and interpretation of models that are used throughout the investment industry in the management of large pools of institutional capital.
The second semester students will be focused on the application of quantitative techniques in investment management. Students will go to the major Canadian financial centres to meet practitioners. They will then prepare for quantitative university competitions (i.e., algo. trading).
Topics to be covered: 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 introductions), Interest rate tree methods (Ho Lee, BDT), Pricing interest rate derivatives (swaptions), Credit risk understanding credit spreads, Black-Scholes-Merton/KMV style default risk models.
Loss distributions, risk measures, and risk aggregation and allocation principles, credit risk, VaR.
The case-based course intends to be applied in nature and covers the implementation, backtesting, evaluation, and interpretation of automated trading models and strategies employed by both retail and institutional traders via the R language. According to Wikipedia: As of 2014, more than 75 percent of the stock shares traded on United States exchanges (including the New York Stock Exchange and NASDAQ) originate from automated trading system orders. 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.
The Quantitative Investment Management program allows you the flexibility to complete two approved electives. These may include courses offered in our MBA program, or courses with other UNB faculties.
Here are some courses with other faculties that you may want to take as electives:
CS 6705 Foundations of Artificial Intelligence
CS 6735 Machine Learning and Data Mining
ECON 4673 Introduction to Game theory
ECON 6645 Applied Econometrics
MATH 6503 Numerical Methods for Differential Equation
MATH 6615 or CS6375 Linear Programming
MATH 6853 Mathematics of Financial Derivatives:
STAT 6053 Regression Analysis
STAT 6073 Non-Parametric Statistics I
STAT 6083 Introduction to Multivariate Statistics
STAT 625 Stochastic Process II
STAT 6323 Dynamic Programming
STAT 6372 Non-Parametric Statistics II
STAT 6383 Introduction to Stochastic Processes
STAT 6402 Multivariate Statistical Analysis
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