Please consult the Directory of Classes for authoritative up-to-date information for Fall 2020 courses. Information like this changes frequently, and the present page is not necessarily up to date.
Fall 2020 Mandatory MAFN Courses
Math GR 5010 Introduction to the Mathematics of Finance – Hybrid
Directory of Classes
Time: Monday and Wednesday 7:40pm-8:55pm
Call Number: 12050
Instructor: Mikhail Smirnov
Stat GR 5263 Statistical Inference / Time-Series Modelling
MAFN students should NOT register for the other version of this course, Stat GU 4263
Two alternative sections:
Stat GR 5264 Stochastic Processes – Applications I
Two alternative sections:
In the Fall semester of 2020, the MAFN program offers the following finance electives.
These courses are not mandatory. MAFN students may choose their electives from across the university, subject to the constraints of the MAFN degree requirements and the constraints imposed by the schools and departments offering the courses.
See Elective Course Examples for examples of elective courses that MAFN students have taken in the past.
Surveys the field of quantitative investment strategies from a “buy side” perspective, through the eyes of portfolio managers, analysts and investors. Financial modeling there often involves avoiding complexity in favor of simplicity and practical compromise. All necessary material scattered in finance, computer science and statistics is combined into a project-based curriculum, which give students hands-on experience to solve real world problems in portfolio management. Students will work with market and historical data to develop and test trading and risk management strategies. Programming projects are required to complete this course.
Risk/return tradeoff, diversification and their role in the modern portfolio theory, their consequences for asset allocation, portfolio optimization. Capital Asset Pricing Model, Modern Portfolio Theory, Factor Models, Equities Valuation, definition and treatment of futures, options and fixed income securities will be covered.
Many business school finance courses have a Capital Markets and Investments prerequisite, and Math GR5280 satisfies this prerequisite. However, even if you satisfy the prerequisite, there is no guarantee that you can cross register into any particular business school course.
The hedge fund industry has continued to grow after the financial crisis, and hedge funds are increasingly important as an investable asset class for institutional investors as well as wealthy individuals. This course will cover hedge funds from the point of view of portfolio managers and investors. We will analyze a number of hedge fund trading strategies, including fixed income arbitrage, global macro, and various equities strategies, with a strong focus on quantitative strategies. We distinguish hedge fund managers from other asset managers, and discuss issues such as fees and incentives, liquidity, performance evaluation, and risk management. We also discuss career development in the hedge fund context.
Note: Open only to MAFN students.
Prerequisites: The student is expected to be mathematically mature and to be familiar with probability and statistics, arbitrage pricing theory, and stochastic processes. The course will introduce the notions of financial risk management, review the structure of the markets and the contracts traded, introduce risk measures such as VaR, PFE and EE, overview regulation of financial markets, and study a number of risk management failures. After successfully completing the course, the student will understand the basics of computing parametric VaR, historical VaR, Monte Carlo VaR, cedit exposures and CVA and the issues and computations associated with managing market risk and credit risk. The student will be familiar with the different categories of financial risk, current regulatory practices, and the events of financial crises, especially the most recent one.
Prerequisites: Students should be comfortable with algebra, calculus, probability, statistics, and stochastic calculus. The course covers the fundamentals of fixed income portfolio management. Its goal is to help the students develop concepts and tools for valuation and hedging of fixed income securities within a fixed set of parameters. There will be an emphasis on understanding how an investment professional manages a portfolio given a budget and a set of limits.
Prerequisites: Math GR5010 Required: Math GR5010 Intro to the Math of Finance (or equivalent),Recommended: Stat GR5264 Stochastic Processes – Applications I (or equivalent) The objective of this course is to introduce students, from a practitioner’s perspective with formal derivations, to the advanced modeling, pricing and risk management techniques that are used on derivatives desks in the industry, which goes beyond the classical option pricing courses focusing solely on the theory. The course is divided into four parts: Differential discounting, advanced volatility modeling, managing a derivatives book, and contagion and systemic risk in financial networks.
Math GR 5510 MAFN Fieldwork
Directory of Classes
Call Number: 12058 Points: 1-3
Note: Open only to MAFN students. Permission of instructor required. Grading: Letter Grade
Prerequisites: All 6 MAFN core courses and at least 6 credits of approved electives. As a consequence, this course is not open to students in their first two semesters
Instructor: Lars Tyge Nielsen
This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree.
For course description, rules, and procedures, please see Fieldwork Course (CPT)
Suggested Electives from the Statistics Department
Stat GR 5293.004 Topics in Modern Statistics
Financial Technology & Data Driven Innovations
Directory of Classes-Sec. 004
Time: Tuesday and Thursday 10:10am-11:25am
Location: ONLINE ONLY
Call Number: 20773 Points: 3
Instructor: Margaret Holen
Recent years have seen rapid evolution in many areas of financial services. This course will examine drivers of those changes focusing on consumer lending, where innovations have been enabled by novel data sources and algorithms for automated decision making. The class will consider multiple perspectives: that of the industry innovators shaping new products, that of consumers encountering them, and that of regulators overseeing the markets. Students will study financial concepts and machine learning methods important in the sector, including fairness and explainability. The class will integrate critical explorations of business practices informed by readings, class discussion and outside speakers, and machine learning and financial mathematics material will be illustrated with industry data sets.
Prerequisites: STAT GU4205 or the equivalent. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.
Stat GR 5206 Statistical Computation and Intro Data Science
If there are remaining seats by the end of change of program period, they will be made open to MAFN students.
Prerequisites: STAT GU4205 and at least one statistics course numbered between GU4221 and GU4261. This is a course on getting the most out of data. The emphasis will be on hands-on experience, involving case studies with real data and using common statistical packages. The course covers, at a very high level, exploratory data analysis, model formulation, goodness of fit testing, and other standard and non-standard statistical procedures, including linear regression, analysis of variance, nonlinear regression, generalized linear models, survival analysis, time series analysis, and modern regression methods. Students will be expected to propose a data set of their choice for use as case study material.
For students who do not have a strong background in Linear Regression, we also propose:
Stat GU 4205 Linear Regression Models
Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.