Graduate Courses

For course details prior to the listed term, please visit the Office of the Registrar.

Fall 2025

Asset Pricing I: Pricing Models and Derivatives
Subject associations
FIN 501 / ORF 514

An introduction to the modern theory of asset pricing. Topics include: No arbitrage, Arrow-Debreu prices and equivalent martingale measure; security structure and market completeness; mean-variance analysis, Beta-Pricing, CAPM; and introduction to derivative pricing.

Instructors
Moritz F. Lenel
Statistical Analysis of Financial Data
Subject associations
ORF 505 / FIN 505

The course is divided into three parts of approximately the same lengths. Density estimation (heavy tail distributions) and dependence (correlation and copulas). Regression analysis (linear and robust alternatives, nonlinear, nonparametric,classification.) Machine learning (TensorFlow, neural networks, convolution networks and deep learning). The statistical analyzes, computations and numerical simulations are done in R or Python.

Instructors
Directed Research I
Subject associations
ORF 509

Under the direction of a faculty member, each student carries out research and presents the results. Directed Research is normally taken during the first year of study.

Instructors
Linear and Nonlinear Optimization
Subject associations
ORF 522

This course introduces analytical and computational tools for linear and nonlinear optimization. Topics include linear optimization modeling, duality, the simplex method, degeneracy, sensitivity analysis and interior point methods. Nonlinear optimality conditions, KKT conditions, first order and Newton's methods for nonlinear optimization, real-time optimization and data-driven algorithms. A broad spectrum of applications in engineering, finance and statistics is presented.

Instructors
Statistical Theory and Methods
Subject associations
ORF 524

A graduate-level introduction to statistical theory and methods covering some of the most important and commonly-used principles of mathematical statistics and statistical learning. Covers the statistical theory and methods for point estimation, hypothesis testing, and confidence intervals. These topics are covered in both nonparametric, semiparametric, and parametric settings, and from asymptotic and non-asymtoptotic viewpoints.

Instructors
Probability Theory
Subject associations
ORF 526

This is a graduate introduction to probability theory with a focus on stochastic processes. Topics include: an introduction to mathematical probability theory, law of large numbers, central limit theorem, conditioning, filtrations and stopping times, Markov processes and martingales in discrete and continuous time, Poisson processes, and Brownian motion.

Instructors
Computational Finance in C++
Subject associations
ORF 531 / FIN 531

The intent of this course is to introduce the student to the technical and algorithmic aspects of a wide spectrum of computer applications currently used in the financial industry, and to prepare the student for the development of new applications. The student is introduced to C++, the weekly homework involves writing C++ code, and the final project also involves programming in the same environment.

Instructors
Deep Learning Theory
Subject associations
ORF 543

This course is an introduction to deep learning theory. Using tools from mathematics (e.g. probability, functional analysis, spectral asymptotics and combinatorics) as well as physics (e.g. effective field theory, the 1/n expansion, and the renormalization group) we cover topics in approximation theory, optimization, and generalization.

Instructors