Course - Date Science in Finance: Financial Analytics and Financial Econometrics

Workload

Attendance / Contact Hours (2 Events á 2 SWS) = 45 h

Preparation/Homework/Self-Study = 45 h

Time for Exercises/Group Work = 60 h

Teaching Forms

Interactive Lecture

Specifics

Programming tasks, exercises and projects with R, presentation of results

Students shall be able to do the following:

Knowledge

Describe basic algorithms and models interconnecting data science, financial market analysis and investment management
Explain basic concepts of data-driven models of financial markets using specialist and simple terminology and critically discuss complex models
Illustrate and discuss current questions and problems

Skills

Effectively analyze, evaluate and visualize big financial data sets
Develop a systematic way to build financial programs with R

Competencies

Implement and apply important models of financial econometrics
Operate articulately within the quantitative and scientific field of financial econometrics, as well as within its applications
Objectively interpret and discuss financial data sets

We build and develop tools to analyze financial data: We explore basic methods from data science and implement these methods in the programming language R to tackle problems from the financial industry.

Introduction to R and importing of financial data
Financial Analytics:
- Introduction to basic methods of data analysis (big data analysis, clustering, classification and covariance analysis, natural language processing) and the applications to financial data
- Development and implementation of algorithms for automated risk management, portfolio optimizing, securities selection and valuation, simulation of financial markets and trading strategies
Financial Econometrics:
- Basics properties and non-predictability of financial returns
- Stylized facts, in particular: heavy-tailed distributions, stochastic volatilities, volatility clustering, averaging of volatilities, etc.
- GARCH-models and extensions
- Copula-models, in particular pair-copula-constructions
- Simulations of financial econometrics with R
- Applications to risk- and portfolio-management

Bennett, M., & Hugen, D. (2016). Financial Analytics with R: Building a Laptop Laboratory for Data Science. Cambridge: Cambridge University Press

Carmona, R (2014). Statistical analysis of financial data in R. Second edition. New York, NY: Springer

McNeil, Frey, Embrechts: Quantitative Risk Management

Current publications of regulatory bodies and financial sites

Akkordeon