Computational Finance (CompFin)
Are you fascinated by financial markets and want to understand how complex derivatives are priced using advanced mathematics and simulation? In the Computational Finance specialization, you’ll model real-world financial problems with stochastic and partial differential equations, Monte Carlo methods, and numerical schemes inspired by models like the Heston stochastic volatility model.
If you’re ready to combine cutting-edge computational techniques with practical projects in collaboration with financial institutions and the energy sector, this specialization is made for you!
Description of the Specialization
The students get familiar with basic concepts in Computational Finance. They learn how to model in finance, develop and use simulation tools and judge their efficiency and practicability in front offices. They study modelling and numerical simulation of problems in financial mathematics using stochastic and partial differential equations, Monte Carlo simulations, transformation techniques, semi-analytic Fourier methods, stochastic correlation approaches, symplectic integration methods in finance. Master theses are in general in cooperation with financial institutes and companies for energy supply.
Module 1. Computational Finance 1 (CompFin1) – mandatory
Workload: 8 ECTS (240 hours, 1 semester)
Final assessment: oral or written exam, not restricted in attempts
Components:
• Computational Finance 1 (CompFi1-a)
E.g. modelling of financial markets, Black-Scholes model, stochastic differential equations.
Module 2. Computational Finance 2 (CompFin2) – mandatory
Workload: 8 ECTS (240 hours, 1 semester)
Final assessment: oral or written exam, not restricted in attempts
Components:
• Computational Finance 2 (CompFi2-a)
E.g. finite difference methods, finite element methods, partial differential equations arising in finance, numerical solution of initial boundary value problems.
At least 24 ECTS credits (or 13% of completed Bachelor´s degree) in the following fields: Analysis I, II and III, Linear Algebra I and Introduction to Stochastic Calculus, Introduction into Numerical Analysis, Theory of Ordinary Differential Equations, Functional Analysis.
Parallel study of Partial Differential Equations is advised.
You can check yourself, if you can study on this specialization by completing the:
Graduates of the Computational Finance specialization are prepared for careers in banks, financial institutions, investment companies, insurance firms, energy trading companies, and financial technology (FinTech) enterprises. Thanks to their expertise in stochastic modelling, Monte Carlo simulation, and numerical methods for partial differential equations, they are well suited for quantitative and analytical roles in front-office and risk management environments.
Typical positions include Quantitative Analyst (Quant), Risk Analyst, Financial Engineer, Derivatives Analyst, Energy Market Analyst, Algorithmic Trading Specialist, and Financial Software Developer. Alumni work on pricing complex financial instruments, risk modelling, portfolio optimization, and the development and validation of simulation tools.
The specialization also provides strong preparation for research-oriented positions and doctoral studies in financial mathematics, computational finance, and quantitative risk management.
TBA
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Contacts
Person responsible for the specialization:
Prof. Dr. Matthias Ehrhardt, +49 202 439 5297, ehrhardt[at]math.uni-wuppertal.de
Lecturers:
- Prof. Dr. Matthias Ehrhardt (ehrhardt[at]uni-wuppertal.de) – Computational Finance
- Prof. Dr. Michael Günther (guenther[at]uni-wuppertal.de) – Computational Finance
- Prof. Dr. Thomas Kruse (tkruse[at]uni-wuppertal.de) – Computational Finance
- PD Dr. Long Teng (teng[at]math.uni-wuppertal.de) – Computational Finance
Links:
Website of the working group for Applied and Computational Mathematics
Moodle Course for Summer term 2026
Last modified: 23.03.2026
