Course description:
The course aims at improving the student’s knowledge of computational methods in the area of finance. Numerical algorithms and partial differential equations, in particular in the field of modelling asset prices and in option pricing, are presented. The student learns to apply methods in a computer project.
Expected prior knowledge:
Basic knowledge of partial differential equations (PDEs), of numerical methods for solving PDEs, of linear algebra and of the basic of numerical linear algebra, computing tool: Python.
Expected Load: 1.5h per week
Content of the Course:
Lecture 1- Introduction and Overview of Asset Classes
Lecture 2- Stock, Options and Stochastics
Lecture 3- Option Pricing and Simulation in Python
Lecture 4- Implied Volatility
Lecture 5- Jump Processes
Lecture 6- Affine Jump Diffusion Processes
Lecture 7- Stochastic Volatility Models
Lecture 8- Fourier Transformation for Option Pricing
Lecture 9- Monte Carlo Simulation
Lecture 10- Monte Carlo Simulation of the Heston Model
Lecture 11- Hedging and Monte Carlo Greeks
Lecture 12- Forward Start Options and Model of Bates
Lecture 13- Exotic Derivatives
Lecture 14- Summary