Ph.D. and MSQ in Quantitative Finance
The Ph.D. Program in Finance at GSEFM is designed to ensure that students acquire a thorough knowledge of the theory of finance, of econometric and computational methods, as well as the structure of modern financial markets, before beginning their own research under faculty supervision.
In the first year of the program, students attend core courses in financial economics, econometrics, and mathematical methods. Furthermore, students attend courses in microeconomic or macroeconomic theory. At the end of the first year of studies, students must pass qualifying examinations in finance, econometrics, and microeconomics or macroeconomics. In the second year of the program students complete field courses in two to three fields of their choosing and begin to work on their own research. All students are required to have a faculty advisor by the end of their second year in the program. The role of the faculty advisor is to help the student to make the transition from coursework to research and to identify suitable dissertation topics. By the end of their third year in the program students will need to have completed their first research paper. The dissertation is completed in the fourth year of the program. The dissertation must be a major piece of research, and its chapters must have the potential for publication in an international scientific journal.
First-year students in the Master Program in Quantitative Finance at GSEFM enroll in the same set of courses as the first-year Ph.D. Program in Finance students. If completing their first year of studies with strong success, the Master Program in Quantitative Finance students are then eligible and strongly encouraged to join the Ph.D. Program in Finance from their second year of studies onwards. Other students in the Master Program in Quantitative Finance in their second year of studies have the option to enroll in concentration courses with an emphasis on policy issues and/or on issues of implementation. The Master Program in Quantitative Finance is completed with a Master thesis written in the final months of the second year of studies.
Ph.D. Program Structure
| Pre-Semester: Mathematics and Statistics |
| Winter Term: Advanced Econometrics 1 (8 CP), Advanced Macroeconomic Theory 1 (8 CP), Advanced Microeconomic Theory 1 (8 CP), Mathematical Methods (8 CP) |
| Summer Term: Advanced Econometrics 2 (8 CP), Advanced Macroeconomic Theory 2 (8 CP), Advanced Microeconomic Theory 2 (8 CP), Historical and Normative Foundations (8 CP) |
| Qualifying Examinations |
| Winter Term: Field Courses, Workshop Attendance | ||||||||||||||
| Summer Term: Field Courses, Seminar, Workshop Attendance | ||||||||||||||
| Fields Offered: | ||||||||||||||
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| Seminar |
| Workshop Attendance |
| Independent Studies Course (Teaching Skills) |
| Third-Year Research Paper |
| Workshop Attendance |
| Job Market Course |
| Thesis Defense |
During either the third or fourth year in the program, students may spend one or two semesters abroad for a research stay at an internationally top ranked Ph.D. program. Such stays are facilitated by the faculty advisor.
First Year Courses
The details of the course offerings will differ somewhat from year to year, depending on the faculty member teaching the course in question. The following list summarizes typical first-year course contents. For more details on these courses in any given year as well as the field courses, it is best to consult the course syllabi typically retrievable on individual faculty members’ websites.
| Finance: mean-variance portfolio selection, CAPM, APT, derivatives, interest rates, overview of asset pricing field |
| Mathematics and Statistics: real analysis, advanced calculus, linear algebra, static optimization, probability theory, estimation and hypothesis testing, linear regression model. |
| Advanced Econometrics 1: fundamentals of linear regression (OLS, SUR, 2SLS, 3SLS, GMM, QML), cross-section regression models with limited dependent variables, static panel data models. |
| Advanced Financial Economics 1: corporate finance, credit constraints, moral hazard, adverse selection, asymmetric information, Diamond/Dybvig model, market for corporate control |
| Mathematical Methods: probability theory, measure theory, stochastic processes, topology, difference and differential equations, dynamic optimization, numerical methods |
| Advanced Macroeconomic Theory 1: dynamic optimization in models with representative and with heterogeneous agents, consumption, investment, saving and financial markets. or |
| Advanced Microeconomic Theory 1: theory of the household, theory of the firm, decisions under uncertainty, market equilibrium, static and dynamic games under alternative information structures. |
| Advanced Econometrics 2: integration and cointegration, single and multiple equation time-series models (ARMA, ARDL, VAR, VECM), spectral analysis, conditional heteroskedasticity. |
| Advanced Financial Economics 2: basic equilibrium asset pricing, models with heterogeneous agents or non-standard preferences, introduction to stochastic calculus and continuous-time modeling, option pricing, asset allocation, equilibrium asset pricing in continuous time, asset pricing in production economy models. |
| Historical and Normative Foundations: history of economic thought (illustrated in the context of modeling economic growth and/or the modeling of financial crises), normative foundations. |
| Advanced Macroeconomic Theory 2: structure of DSGE models, monopolistic competition and pricing, strategic complementarities, optimal monetary and fiscal policy, learning. or |
| Advanced Microeconomic Theory 2: contract theory (moral hazard, adverse selection, mechanism design, incomplete contracts), general equilibrium theory, welfare economics, externalities. |