Stamford Teaching

FNCE 5352: Financial Programming and Modeling

Instructor: Dr. Do

Office: MSB 218

Office hours: by appointment

Email: cuong.do@engineering.uconn.edu (preferred), or cuong.do@business.uconn.edu

Description

This course introduces MATLAB, R, and Python for financial programming and modeling. Students pick up optimization techniques such as linear and non-linear programming in MATLAB, in R (linprog and quadprog packages), and in Python (CVX and Scipy packages), and apply these in portfolio optimization using various risk measures. Mixed-integer programming is introduced. Others include derivative pricing methods (Monte-Carlo simulation, binomial and trinomial trees, Black-Scholes, and finite difference methods), investment efficiency evaluation (data envelopment analysis – DEA), data mining and machine learning techniques applied in risk management. Students develop various applications across several programming languages. An introductory level of C++ is provided with applications in the financial context.

Students work in groups in their algorithmic trading class projects, write Python code to derive from standard trading strategies, or implement their own trading algorithms, and run them on Quantopian framework. Financial discrete-time signal analysis and synthesis techniques are introduced such as Fast Fourier transform, and wavelet transforms to apply in trading, in addition to optimization techniques. This is conditional on the availability of the framework during the course. Completed projects are posted on the UConn Applied Financial Data Science Group website. Students can find previous class projects to learn from here.

This is fast-pace course. Students are expected to learn in class as well as outside class using various materials provided by the instructor.

Textbooks

Slides are provided by the instructor.

Reference Textbooks

  1. Simulation and Optimization in Finance: Modeling with MATLAB, @Risk, or VBA, Frank J. Fabozzi
  2. Numerical Methods and Optimization in Finance, Manfred Gilli, Dietmar Maringer, Enrico Schumann
  3. Financial Modeling 3rd edition, Simon Benninga
  4. Foundations and Methods of Stochastic Simulation: A First Course, Barry Nelson
  5. Numerical Methods in Finance and Economics: A MATLAB-Based Introduction (Statistics in Practice), Paolo Brandimarte
  6. Financial Numerical Recipes in C++, Bernt Arne Ødegaard

Software

  • R is downloadable from www.r-project.org or on UConn SkyBox at uconn.edu
  • RStudio from rstudio.com or on UConn SkyBox at skybox.uconn.edu
  • MATLAB is available on UConn SkyBox at uconn.edu
  • Python is available from https://www.continuum.io/downloads

Teaching Materials

The syllabus and all teaching material will be posted at http://huskyct.uconn.edu.

Prerequisites

  • Some experience with a scripting language for modeling, either MATLAB or R
  • Some experience with any programming language for application development
  • FNCE 5313: Financial Risk Modeling I
  • FNCE 5321: Financial Risk Modeling II
  • FNCE 5331: Financial Risk Modeling III

Grading

Your final grade will be based on the following distribution:

Applications: 80%

Trading algorithm project: 10%

Class participation: 10%

—————

Total: 100%

The content of this course is not the same as the course taught in Storrs.

Note: The instructor reserves the right to make changes to the syllabus as needed.

If there is any change, you will be notified in class or by your UConn e-mail address