Computational Finance

Knowledge (= a rough lecture plan)

Topics may include but are not limited to:

• High-level programming
• Data and computational resources
• Monte Carlo techniques in option pricing: variance reduction, diffusion (and possibly Levy) process simulation, American options, adjoint techniques
• Numerical transform methods for option pricing
• Numerical optimization and model calibration
• Numerical methods for solving parabolic partial differential equations
• Machine learning approaches in quantitative finance

Only a selection (based on lecturer and student interest) of the topics will be covered.

Skills

High- and low-level programming as fits the problem.

Extracting and handling financial data.

Ability to implement numerical techniques (to investigate pricing and hedging) for a range of financial products and models.

Ability to implement a (limited) number of more specialized methods for more specific models and problems.

Competencies

Proficieny classical and modern numerical methods for quantitative finance problems. This is a question of having both a sizeable "toolbox" and the ability pick the appropriate on in a given situation.