Financial Data Analysis; Mathematical Finance; Stochastic Processes; Numerical Analysis
Dr. Linus Wunderlich (Postdoctoral Researcher)
Christian Pötz (PhD student)
Domagoj Demeterfi (PhD student)
Financial data and low-rank tensor techniques: Boosting data-driven machine learning algorithms, creation of synthetic datasets, storing and processing large financial datasets, approximation of high-dimensional nonlinear data, modelling.
Function approximation methods: Fourier transform, Partial (integral) differential equations, reduced basis, interpolation, sparse grids, low-rank tensor approximation, deep neural networks, Monte Carlo, kernel learning,...
Modelling with stochastic processes: modelling with semimartingales, particularly Lévy processes
Applications in Finance: pricing, hedging, calibration, credit exposure calculation,
(2019). The chebyshev method for the implied volatility Journal of Computational Finance.
(2019). COMPLEXITY REDUCTION FOR CALIBRATION TO AMERICAN OPTIONS The Journal of Computational Finance.
(2019). A new approach for American option pricing: The dynamic Chebyshev method SIAM Journal on Scientific Computing.
(2018). A Flexible Galerkin Scheme for Option Pricing in Lévy Models SIAM Journal on Financial Mathematics.
(2018). Chebyshev Interpolation for Parametric Option Pricing Finance and Stochastics.
(2018). Calibration to American Options: Numerical Investigation of the de–Americanization Method Quantitative Finance.
(2017). Parametric Integration by Magic Point Empirical Interpolation IMA Journal of Numerical Analysis.
(2017). Magic Points in Finance: Empirical Integration for Parametric Option Pricing SIAM Journal on Financial Mathematics.
(2016). A Feynman–Kac-type formula for Lévy processes with discontinuous killing rates Finance and Stochastics.
(2016). Classification of Lévy Processes with Parabolic Kolmogorov Backward Equations Theory of Probability & Its Applications.