These notes are for students in science, economics or finance who have followed at least one undergraduate course in financial mathematics and statistics. In this way, the reader will be able to immediately understand what I am talking about.
However, every time I will introduce a concept, I will also add a link to the corresponding wiki page or to a reference manual. In these notebooks I will not explain what is a call option, or what is a stochastic process, or a partial differential equation. A basic knowledge of python programming is also necessary. These topics require a basic knowledge in stochastic calculus, financial mathematics and statistics. The aim of these notebooks is to present these interesting topics, by showing their practical application through an interactive python implementation.
Usually, topics such as PDE methods, Lévy processes, Fourier methods or Kalman filter are not very popular among practitioners, who prefers to work with more standard tools. It contains several topics that are not so popular nowadays, but that can be very powerful. This is just a collection of topics and algorithms that in my opinion are interesting.
This is a collection of Jupyter notebooks based on different topics in the area of quantitative finance.
0 kommentar(er)
