Quantitative Finance and Insurance

The course aims to provide a broad understanding, principles, and techniques of Python coding for the most popular quantitative applications, widely used in the financial industry and academic research. In addition, a primer on Decentralized Finance (DeFi) will be taught.

The course will cover various topics, emphasizing the hands-on implementation of those ideas in Jupyter Notebook (or Spyder) and intuitively visualized output. A short introduction to Python (and some of the essential packages) is given at the start of the course. The necessary historical financial data will be downloaded via various Python APIs straight from the web. To improve the performance of some quantitative models and investment strategies to forward-looking (option-implied) information is referred.

The course roughly splits into four parts (see below for more information): i) Risk Analytics, ii) Portfolio Optimization, and iii) Decentralized Finance. Please note that you will require a good understanding of mathematics to understand some of this material.

Program and analyze trading strategies in Python. Optimize a portfolio with respect to some risk criteria using the SciPy framework. Obtained a primer on Decentralized Finance. These acquired skills and techniques serve as a more than solid foundation for any entry job or internship within the financial industry (Fintech, Robo Advisory, Asset Manager, Hedge Funds, …).

• Risk Analytics

The most important risk statistics (alpha, betas, average return, standard deviation, sharp ratio, information ratio, value at risk, conditional value at risk, drawdown, turnover) will be discussed and implemented in Python. These risk analytics serve as a solid foundation for benchmarking the investment strategies in the upcoming sections.  

• Portfolio Optimization

State-of-the-art portfolio optimization techniques have been proven popular in investment management. In the first step, the Mean-variance framework will be studied and enriched with various alternative specifications such as additional risk constraints, portfolio selection with higher moments, transaction costs, and robustness improvement methods via resampling. The optimization will be performed using SciPy which is a free and open-source Python library used for scientific computing.

• Decentralized Finance

Decentralized Finance (DeFi) protocols are applications on a blockchain that offer financial services such as trading, lending, and borrowing. In the last part of the class, an overview of the functioning of DeFi and its applications will be provided.

Teaching sessions will combine lectures and practical tasks in Python.

 

The final exam is to be taken at home (open book) and in groups of two students (randomly selected). The discussion of the project will consist of a short presentation of the project. The group is expected to deliver a Jupyter notebook containing the code and results. I will supervise the projects and assist the groups in making the code.