What is Financial Mathematics?

Financial Mathematics is a special area of probability and mathematical statistics focusing on mathematical models of financial (and other) markets and in general on mathematical areas relevant for the financial (and insurance) industry. Financial mathematics is also heavily linked to analysis, numerical mathematics and optimization. Needless to say that it is also heavily related to economics and business studies, as the mathematical modelling of economic markets and agents needs considerable insight into them.

Students will also gain familiarity with the professional financial market information system Bloomberg, which is available in the faculty’s trading room and standardly used in many financial institutions. Graduates with a profound knowledge of financial mathematics are heavily sought after at the job market - not only by financial institutions where many different job profiles from investment banking via asset management and consulting to group risk control are suitable. Of course, there are also many interesting possibilities to pursue a PhD after obtaining the master degree and to work on challenging mathematical research questions of high practical relevance.

Financial Mathematics includes (but is by far not limited to) the following topics:

  • Mathematical Modelling of Financial Markets (in a broad sense, includes commodities, raw materials, food, energy, currencies, credits, ...)
  • Statistical Analysis of Financial Markets, Financial Econometrics
  • Pricing and Hedging of Financial Products
  • Risk Assessment and Risk Management Strategies
  • Optimal Investment, Optimal Consumption, ... (Utility Optimization)

Many aspects of Financial Mathematics are explained also by Robert Stelzer in this film (unfortunately only available in German)at the faculty's youtube channel.

Main Mathematical Areas

  • "Financial Mathematics" (in a narrower sense, i.e. the theory of the mathematical modelling of (arbitrage free) financial markets
  • Stochastic Analysis and Stochastic Processes
  • Stochastic Optimal Control
  • Risk Measures
  • Extreme Value Theory
  • Time Series Analysis, Statistics in general
  • Stochastic Simulation, Numerical Analysis (e.g. PDEs)