Computational Finance (MGT412)

Syllabus [here]
Software needed: Anaconda Python Distribution (Mac, Windows, Linux): open source
Literature: Textbook on Amazon.fr

Schedule

  1. Introduction to Python. Operators. Control flows. Data types and structures.
  2. Introduction to Python. The numpy library for numerical analysis. 
  3. Data analysis and management: I/O operations and the pandas library 
  4. Data analysis. Working with high-frequency time objects. Plotting in matplotlib.
  5. Data fitting: OLS regression, non-linear regression and interpolation.
  6. Midterm test
  7. Convex optimization in Python and symbolic math. 
  8. Stochastic processes and Monte Carlo simulations. Simulating stock price paths.
  9. Portfolio theory, CAPM, and the efficient frontier. The scipy library. 
  10. Fama-MacBeth regressions and factor testing. Value-at-risk and expected shortfall.
  11. Option pricing: Binomial model algorithms. Programming recursive trees.
  12. Option pricing with Monte Carlo simulations: American options.

Old schedule:

  1. Basic Tools in Python [slides] [Jupyter]
  2. Data Analysis with pandas.  [slides] [Jupyter]
  3. High-frequency Data and Vizualisation. [slides] [Jupyter] [data]
  4. Regression. Optimization. Symbolic Math.  [slides] [Jupyter]
  5. Stochastic Processes in Python.  [slides] [Jupyter]
  6. Option Pricing.  [slides] [Jupyter]
  7. Portfolio Theory and PCA Analysis.  [​slides] [Jupyter] [data] [data CAC]
  8. GARCH and the EM Algorithm. [slides] [Jupyter] [data SEM] [data GARCH]

Exam

TBC