ORF 405 / FIN 505: Statistical Analysis of Financial Data in R

Divided into three parts of approximately the same lengths.

• Density estimation (heavy tail distributions) and dependence (correlation and copulas)
• Regression analysis (linear, nonlinear, nonparametric) and robust alternatives
• Machine learning with 1) support Vector Machines, 2) Graphical Lasso, and 3) Neural Networks

The students choose to do statistical analyzes, computations and numerical simulations in the R software environment, or Python. The second part of the class will rely on Tensor Flow. The lectures, the problem sets and the examinations are based on Jupyter Notebooks.

ORF 542: BSDEs, Stochastic Control and Stochastic Games

Review of (forward) stochastic differential equations; Introduction to Backward Stochastic Differential Equations (BSDE) and Forward/Backward Stochastic Differential Equations (FBSDEs); Stochastic Control: Dynamic Programming, Hamilton-Jacobi-Bellman equations and connection with BSDEs; Stochastic Maximum Principle and the Probabilistic Approach to Stochastic Control; Stochastic differential games, Nash equilibriums, Isaacs conditions, BSDEs and FBSDEs; Applications: LQ models, Predatory trading game. Mean Field Games and Control of McKean-Vlasov dynamics; Applications: systemic risk, macro-economic growth models, flocking, schooling and crowd behavior

ORF 531 / FIN 531: Computational Finance in C++

Introduction to object oriented programming and C. Introduction of the technical and algorithmic aspects of a wide spectrum of computer applications currently used in the financial industry, and C implementations of these concepts. All bi-weekly homework assignments involves C code, and the final project comprises the development of a financial application in C.

ORF 455: Energy and Commodity Markets

ORF 569: Topics in Random Graphs and Network Games

Lecture Notes (Spring 2019) use at your own risk