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. Algorithms: Solving problems in Python.
  3. Data analysis and management: The pandas library
  4. High-frequency trading data. Plotting in matplotlib.
  5. Optimization and maximum likelihood: GARCH model of volatility.
  6. Stochastic processes: Simulating stock price paths.
  7. Portfolio theory and plotting the efficient frontier.
  8. Empirical asset pricing: Testing factor models.
  9. Option pricing: Programming a binomial tree.
  10. Option pricing: Monte Carlo simulations and American options.
  11. Discussion of Large Data Assignment and Recap.
  12. Discussion of Take-Home Final Test

Assessment:
  • 20% from a five-problem algorithm solving assignment (general programming)
  • 25%  from a short code adaptation assignment (portfolio formation)
  • 30% from a Large Data Assignment (more complex project, in teams of 2 or 3)
  • 25% from the final Term Test