Computational Finance (MGT412)
Syllabus [here]
Software needed: Anaconda Python Distribution (Mac, Windows, Linux): open source
Literature: Textbook on Amazon.fr
Literature: Textbook on Amazon.fr
Schedule
- Introduction to Python. Operators. Control flows. Data types and structures.
- Algorithms: Solving problems in Python.
- Data analysis and management: The pandas library
- High-frequency trading data. Plotting in matplotlib.
- Optimization and maximum likelihood: GARCH model of volatility.
- Stochastic processes: Simulating stock price paths.
- Portfolio theory and plotting the efficient frontier.
- Empirical asset pricing: Testing factor models.
- Option pricing: Programming a binomial tree.
- Option pricing: Monte Carlo simulations and American options.
- Discussion of Large Data Assignment and Recap.
- 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