The PhD program is rooted in research, grounded on a solid foundation of advanced academic coursework, and enhanced through a sequence of program experiences that will enable your future career.

One-on-one learning is our focus. You and your advisor will feel more like apprentice and mentor than student and faculty. Together you’ll have the freedom to concentrate on ideas and fields of study that YOU choose. You decide your fate by picking the department, but the range of areas that you can take your work are boundless. Through our rigorous curriculum you’ll work closely one-on-one with highly qualified, highly-dedicated faculty members and leave Questrom with university teaching experience, a publishable dissertation, an esteemed and supportive network, and a PhD!

PhD ProgramAcademics

Academic Disciplines

Your academic discipline will take you in limitless directions. Whichever area of study you choose, there are no bounds to the opportunity you’ll find here.

No matter which academic discipline you choose, you’ll develop relationships with Questrom’s talented PhD students and faculty, expanding your knowledge and sparking innovation. This is a place where you’ll work across disciplines, industries, and borders to understand the forces transforming our world, to be challenged by the brightest minds, and to make invaluable connections every day.

Academic disciplines allow you to specialize your focus to one area of interest. With your research interests in mind, you can choose from the disciplines we offer our PhD in Business students. Once you pick your discipline then, in a way, you become a part of the department itself. Learn more about academic disciplines and the departments to which they belong by visiting our individual department pages. Here you’ll find information about each department’s faculty, courses, events, and resources.

PhD ProgramAcademics


Course Requirements

The minimum course requirement for the PhD program is 48-64 credits depending on the chosen academic discipline. Below are the course requirements for the disciplines that we offer:


Over the summer between year 1 and year 2, students are expected to write a research paper. During the spring term of year one, students should find a faculty mentor with whom to work on the summer paper.  The mentor may be the faculty advisor assigned upon entry in the program or may be a different person. Students will lead this project, but it can also be co-authored with the faculty mentor.

Students will then present the results of that work in a departmental seminar during the Fall semester of year 2. The faculty readers will determine if the finished work fulfills the requirements of the summer paper.


After the completion of all course work and the curriculum paper, students are required to appear for a qualifying examination to demonstrate their advanced knowledge of literature and theory, research techniques and the ability to craft a research proposal.


The final phase of the program is the completion of your dissertation, which you will complete with the help of your advisor. You’ll form a dissertation committee that’s made up of a Committee Chairperson and a least two additional faculty members. With this guidance, you’ll develop a research proposal for investigating an area of significance for management theory and practice.


Our PhD program is for students who want to work at a university in a research capacity and who ultimately express a strong desire to be university professors. That’s why our program includes a teaching requirement where you’ll teach at least one 3 or 4 credit class independently as the lead instructor before earning your degree. This teaching must take place during your 3rd or 4th year in the program.


Our fellowship students must carry out teaching and/or research requirements as a part of their fellowship obligations. These students must carry out the necessary tasks, assigned by their department chairperson, in order to maintain their fellowship.

PhD in Business Administration Course Requirements

The following course requirements are for the Accounting, Finance, Information Systems, Marketing, Operations & Technology Management, Management & Organizations, Strategy & Innovation disciplines.

YEAR 1 & 2
  • Research Methodology: 5 courses (20 credits)
  • Major Area: 5 courses (20 credits)
  • Minor Area(s): 5 Courses (20 credits)

Students are also required to take the following in their first and second year:

  • DS906 Philosophy and Science of Research (4 credits)
  • DS907 Teaching, Publishing, and Dissemination of Knowledge (0 credit)

Fundamentals of Research and the Philosophies of Science (previously MK912) (QSTDS906)

This course introduces students to research. The class provides a brief introduction to the philosophy of science and debates about the nature of theory before diving thoroughly into different research methods. Students are exposed to research methods from their own and adjacent fields ranging from causal inference and experiments to qualitative research methods. The last part of the class introduces students to issues around diversity, ethics, and equity in research. As part of the class students will complete the introductory ethics modules that are required by the university. Students will be graded on their class participation, a research proposal which is due at the end of the class, and their feedback to other students on their research proposals.

Teaching, Publishing, and Dissemination of Knowledge (QSTDS907)

As scholars, doctoral students will be responsible for both conducting research in their chosen field and disseminating knowledge through publishing and teaching. This course prepares participants to be successful in converting research into publications and provides a foundation for designing and delivering educational offerings in a variety of settings.

Total Credits: 64

PhD in Mathematical Finance COurse Requirements

Year 1: Fall

Microeconomic Theory 1 (GRSEC701)

Neoclassical general equilibrium theory. Topics covered include consumption, production, existence of competitive equilibrium, fundamental welfare theorems, externalities, and uncertainty.

Probability Theory I (GRSMA779)

Introduction to probability with measure theoretic foundations. Fundamentals of measure theory. Probability space. Measurable functions and random variables. Expectation and conditional expectation. Zero-one laws and Borel-Cantelli lemmas. Chracteristic functions. Modes of convergence. Uniform integrability. Skorokhod representation theorem. Basic limit theorems.

Doctoral Seminar in Finance (QSTFE918)

This doctoral course, is designed to provide students with an introduction to financial economics. This lecture-based course will cover no arbitrage conditions, preferences and risk aversion, portfolio selection, the capital asset pricing model, asset pricing and dynamic asset pricing. In addition to lectures, this class will include readings and assignments. Open to MBA students with faculty member's permission. Must have strong quantitative background and several courses in finance or economics.

Real Analysis (GRSMA711)

Measure theory and integration on measure spaces, specialization to integration on locally compact spaces, and the Haar integral. Lp spaces, duality, and representation theorems. Introduction to Banach and Hilbert spaces, open mapping theorem, spectral theorem for Hermitian operators, and compact and Fredholm operators.

Year 1: Spring

Microeconomic Theory 2 (GRSEC703)

Noncooperative game theory. Economics of information: adverse selection, signaling, principal agent problem, moral hazard and introduction to mechanism design.

Advanced Stochastic Processes (GRSMA783)

Proof-based approach to stochastic processes. Brownian motion. Continuous martingales. Stochastic integration. Ito formula. Girsanov's Theorem. Stochastic differential equations. Feynman-Kac formula. Markov Processes. Local times. Levy processes. Semimartingales and the general stochastic integral. Stable processes. Fractional Brownian motion.

Advanced Capital Markets (QSTFE920)

This course provides a comprehensive and in-depth treatment of modern asset pricing theories. Extensive use is made of continuous time stochastic processes, stochastic calculus and optimal control. In particular, martingale methods are employed to address the following topics: (i) optimal consumption- portfolio policies and (ii) asset pricing in general equilibrium models. Advances involving non-separable preferences, incomplete information and agent diversity will be discussed.

Probability Theory II (GRSMA780)

Probability topics important in applications and research. Laws of large numbers. Three series theorem. Central limit theorems for independent and non-identically distributed random variables. Speed of convergence. Large deviations. Laws of the iterated logarithm. Stable and infinitely divisible distributions. Discrete time martingales and applications.

Year 2: Fall

Derivative Securities (QSTFE919)

This course provides a comprehensive and in-depth treatment of valuation methods for derivatives securities. Extensive use is made of continuous time stochastic processes, stochastic calculus and martingale methods. The main topics to be addressed include 1) European contingent claims valuation, 2) American claims, 3) valuation in the standard model, 4) barrier options, 5) numerical methods for ingle asset derivatives, and 6) multi-asset options. Additional topics may be covered depending on time constraints.

Partial Differential Equations (GRSMA776)

Hyperbolic, elliptic, and parabolic equations. Characteristics and separation of variables. Eigenvalue problems, Fourier techniques, Sobolev spaces, and potential theory. Introduction to pseudodifferential operators.

Advanced Mathematical Finance (QSTMF922)

This course provides a rigorous introduction to the modern theory of mathematical finance in incomplete markets. The first part of the course covers the fundamental theorem of asset pricing, as well as super-hedging of contingent claims, in full generality for discrete time models. The second half of the class focuses on optimal investment, pricing and hedging in continuous time incomplete markets. Here, lectures will cover optimal investment and duality for general semi-martingale models; and portfolio optimization and pricing in Markovian factor models, non-Markovian Brownian models, models with trading constraints, and models with transactions costs. Throughout this half, lectures will highlight connections to the dynamic programming principle, the Martingale optimality principle, semi-linear Cauchy partial differential equations, backward stochastic differential equations, and the theory of viscosity solutions.

Fall Electives (choose one)

Portfolio Theory (QSTMF730)

A concise introduction to recent results on optimal dynamic consumption- investment problems is provided. Lectures will cover standard mean-variance theory, dynamic asset allocation, asset-liability management, and lifecycle finance. The main focus of this course is to present a financial engineering approach to dynamic asset allocation problems of institutional investors such as pension funds, mutual funds, hedge funds, and sovereign wealth funds. Numerical methods for implementation of asset allocation models will also be presented. The course also covers empirical features and practical implementation of dynamic portfolio problems. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Advanced Derivatives (QSTMF770)

This course provides a comprehensive and in-depth treatment of valuation methods for derivative securities. Extensive use is made of continuous time stochastic processes, stochastic calculus and martingale methods. The main topics to be addressed include (i) European option valuation, (ii) Exotic options, (iii) Multiasset options, (iv) Stochastic interest rate, (v) Stochastic volatility, (vi) American options and (vii) Numerical methods. Additional topics may be covered depending on time constraints. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Credit Risk (QSTMF772)

The derivatives market has experienced tremendous growth during the past decade as credit risk has become a major factor fostering rapid financial innovation. This course will provide an in-depth approach to credit risk modelling for the specific purpose of pricing fixed income securities and credit-risk derivatives. The course will explore the nature of factors underlying credit risk and develop models incorporating default risk. Types and structures of credit-derivatives will be presented and discussed. Valuation formulas for popular credit-derivatives will be derived. Numerical methods, for applications involving credit derivative structures and default risks, will be presented. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Advanced Computational Methods (QSTMF850)

This course explores algorithmic and numerical schemes used in practice for the pricing and hedging of financial derivative products. The focus of this course lies on data analysis. It covers such topics as: stochastic models with jumps, advanced simulation methods, optimization routines, and tree-based approaches. It also introduces machine learning concepts and methodologies, including cross validation, dimensionality reduction, random forests, neural networks, clustering, and support vector machines. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Macroeconomics and Financial Markets (GRSEC745)

Topics and approaches combine macroeconomics and finance, with an emphasis on developing and testing theories that involve linkages between financial markets and the macro economy.

Spring Electives (choose two)

Fixed Income Securities (QSTMF728)

The course focuses on the valuation, hedging and management of fixed income securities. Theoretical and empirical term structure concepts are introduced. Short rate models and the Heath-Jarrow-Morton methodology are presented. Market models and their application for the valuation of forwards, swaps, caps, floors and swaptions, and other interest rate derivatives are discussed in detail. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Stochastic Methods in Asset Pricing II (QSTMF794)

The course covers: the Feynman-Kac formula and the Fokker-Plank equation, stochastic calculus with jumps, Levy processes and jump diffusion models in finance, Bellman's principle of dynamic programming and the Hamilton-Jacobi- Bellman equation, classical problems for optimal control in finance (Merton's problem, etc.), investment-consumption decisions with transaction costs, the connection between asset pricing and free-boundary problems for PDEs, optimal stopping problems and the exercise of American-style derivatives, capital structure and valuation of real options and corporate debt, exchange options, stochastic volatility models, and Dupire's formula. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Computational Methods of Mathematical Finance (QSTMF796)

This course introduces common algorithmic and numerical schemes that are used in practice for pricing and hedging financial derivative products. Among others, the course covers Monte-Carlo simulation methods (generation of random variables, exact simulation, discretization schemes), finite difference schemes to solve partial differential equations, numerical integration, and Fourier transforms. Special attention is given to the computational requirements of these different methods, and the trade-off between computational effort and accuracy. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Algorithmic and High-Frequency Trading (QSTMF821)

This course will introduce concepts of electronic markets, and statistical and optimal control techniques to model and trade in these markets. We will begin with a description of the basic elements of electronic markets, some of the features of the data, its empirical implications and simple microeconomic models. Next, we will study statistical tools to estimate and predict price and volatility of the high-frequency price. Then we will investigate algorithmic trading problems from the stochastic optimal control perspective, including the optimal execution problem and show how to modify the classical approaches to include order-flow information and the effect that dark pools have on trading. Trading pairs of assets that mean-revert is another important algorithmic strategy, and we will see how stochastic control methods can be utilized to inform agents how to optimally trade. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Data Analysis and Financial Econometrics (QSTMF840)

This is the second course of the econometrics sequence in the Mathematical Finance program. The course quickly reviews OLS, GLS, the Maximum Likelihood principle (MLE). Then, the core of the course concentrates on Bayesian Inference, now an unavoidable mainstay of Financial Econometrics. After learning the principles of Bayesian Inference, we study their implementation for key models in finance, especially related to portfolio design and volatility forecasting. We also briefly discuss the Lasso and Ridge methods, and contrast them with the Bayesian approach Over the last twenty years, radical developments in simulation methods, such as Markov Chain Monte Carlo (MCMC) have extended the capabilities of Bayesian methods. Therefore, after studying direct Monte Carlo simulation methods, the course covers non-trivial methods of simulation such as Markov Chain Monte Carlo (MCMC), applying them to implement models such as stochastic volatility. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

Financial Econometrics (GRSEC794)

Presents econometric theory and methods for the analysis of financial markets. Topics include cross section and time series properties of asset returns, parametric and nonparametric volatility measurement, implied volatility, estimation of asset pricing models, continuous time models, systemic risk, and model uncertainty.

Total Credits: 48-64

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