Computational Finance

 

Projects

Development of efficient and robust numerical methods for option pricing using high performance computers

Fields of Research

  • dimension-adaptive sparse grid quadrature 
  • multilevel quasi-Monte Carlo simulation
  • multivariate binomial trees

Please test our new online tool!

SG-ALM is a software package for the efficient asset liability management. Our new online tool gives you an impression of the productive efficiency and performance of the software:

Computational Finance is a new interdisciplinary field of scientific computing. Its aim is to estimate as accurately as possible the risks that financial instruments generate. 
 
Application areas include the pricing and trading of financial securities, the development of hedging strategies, risk assessment and management, asset-liability management, investment decisions and corporate strategic planning. Current challenges are more and more complex financial products, market models involving several sources of uncertainty and the simultaneous management of assets and liabilities as an optimization problem.
 
In the Department of Computational Finance efficient and robust numerical algorithms are developed and realized on parallel supercomputers. Thereby, modern computational methods, such as multilevel Monte Carlo and Quasi-Monte Carlo simulation, dimension-adaptive sparse grid quadrature, and sparse multinomial trees are employed. These new methods allow the computation of high accuracy solutions and simultaneously a substantial reduction of computing times.