2023 Springmeeting
Participants of the 2022 Spring meeting at KU Leuven
Wednesday May 31, 2023, the Dutch-Flemish Scientific Computing Society organizes its annual spring meeting. This year it takes place at Technical University Eindhoven. A mix of young and senior researchers are invited to present their research.
Participation including lunch is free of charge but you will need to register. Registration will open in March.
Location:
TU Eindhoven
Zwarte Doos, gebouw 4 it is a 10 minute walk from the NS station Eindhoven Centraal
Organization
The spring meeting is organized yearly by the Dutch-Flemish Scientific Computing Society (SCS), this year in cooperation with Technical University Eindhoven.
Organizing comittee: Barry Koren (TU Eindhoven) and Martine Anholt (CWI, Secretary SCS).
Support for this meeting has been obtained from Centrum Wiskunde & Informatica (CWI) and TU Eindhoven.
Draft Program 2023
09:00-09:30 |
Registration, coffee and tea |
09:30-10:10 |
Svetlana Dubinkina, VU Amsterdam |
10:10-10:35 |
Emil Løvbak, KU Leuven |
10:35-11:00 |
Fang Fang, Technical University Delft |
11:00-11:30 |
Coffee and tea |
11:30-11:55 |
Philipp Horn,Technical University Eindhoven
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11:55-12:20 |
Jonas Thies, Technical University Delft |
12:20-12:30 |
Group picture |
12:30-13:30 |
Lunch |
13:30-13:55 |
Mariya Ishteva, KU Leuven |
13:55-14:20 |
Pascal den Boef, Technical University Eindhoven |
14:20-14:50 |
Coffee, tea and refreshments |
14:50-15:15 |
Anne Eggels, Sioux Technologies |
15:15-15:55 |
Wim Vanroose, University of Antwerp |
15:55-16:00 |
Closure |
Speakers Spring meeting SCS 2023
Svetlana Dubinkina, VU Amsterdam | |
Wim Vanroose, University of Antwerp | |
Anne Eggels, Sioux Technologies | |
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Fang Fang, Technical University Delft Dr. Fang Fang obtained a PhD in Computational Finance from TU Delft in 2010, based on the innovation of “the COS method”. Since 2021 she has been working for TU Delft as a part-time assistant professor. She is also a senior quant consultant and a modelling expert, with 14 years hands-on experience in pricing model validation and risk model development at Tier-1 financial institutions in the Netherlands. Her research interest lies in improving numerical methods and models for 1) risk quantification and allocation, 2) derivative pricing and 3) time series predictions. Courses she teaches/moderates include Computational Finance (Msc), Advanced Credit Risk Management (MOOC course joint prepared by TU Delft and Deliotte) and Introduction of Credit Risk Management (MOOC by TU Delft). |
Mariya Ishteva, KU Leuven | |
Jonas Thies, Technical University Delft | |
Pascal den Boef, Technical University Eindhoven | |
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Philipp Horn, Technical University Eindhoven Philipp Horn is a PhD student in the UNRAVEL project at TU Eindhoven. The current focus of his research are structure preserving neural networks for Hamiltonian systems. He obtained his B.Sc. degree in Simulation Technology from the University of Stuttgart. Followed by a double master program in Simulation Technology at the University of Stuttgart and Industrial and Applied Mathematics at TU Eindhoven. After his studies he shortly had a position as Junior Researcher at DIFFER in Eindhoven, researching structure preserving neural network surrogate models for fusion simulation |
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Emil Løvbak, KU Leuven Emil obtained a BSc in Computer Science and Electrical engineering and an MSc in Mathematical Engineering from KU Leuven. After four years as a PhD Fellow of the Research Foundation Flanders, he is currently a researcher in the NUMA group at KU Leuven. His research areas cover multilevel Monte Carlo methods, stochastic optimization and kinetic equations. |
Abstracts Spring meeting SCS 2023
Svetlana Dubinkina, VU Amsterdam |
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Wim Vanroose, University of Antwerp |
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Anne Eggels, Sioux Technologies |
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Fang Fang, Technical University Delft |
A Novel Fourier-cosine method for risk quantification and allocation of credit portfolios Credit risk quantification and allocation in the factor-copula model framework underlies various practical applications in the banking industry. The popular numerical method in the banking industry is Monte Carlo (MC) simulation, which not only takes a considerable amount of computational time for large portfolios, but also fails to return reliable results when it comes to risk allocation at a standard high quantile like 99.9%. Herewith we present a novel Fourier-cosine method, which not only serves as a fast solver for portfolio-level risk quantification, but also fills the niche in literature that an accurate numerical method for risk allocation is lacking. The key insight is that, compared to directly estimating the portfolio loss distribution, it can be much more efficient to solve the characteristic function (ch.f.) instead, after which the ch.f. can be inverted to recover the cumulative distribution function (CDF) semi-analytically via the popular Fourier-cosine (COS) method in the field of option pricing but with some extension. We therefore name this method the COS method. As for allocation of risk measures, we show that, via the Bayes law, the original problem can be transformed to the evaluation of a conditional CDF, which can again be solved following the same insight. Theoretical proof of the error convergence is also provided, which effectively justifies the stability and accuracy of this method in recovering CDFs of discrete random variables in general. For real-sized portfolios, the calculation speed and accuracy are tested to be significantly superior to Monte Carlo simulation in the two-factor set-up. A Gaussian copula and a Gaussian-t hybrid copula are taken as examples to illustrate the flexibility of this method regarding copula choices; Value-at-Risk, Expected Shortfall (ES) and Euler allocation of ES are risk metrics selected for testing. The potential application scope is wide: Economic Capital for Banking Book, Default Risk Charge for Trading Book, valuation of credit derivatives, etc. |
Mariya Ishteva, KU Leuven |
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Jonas Thies, Technical University Delft |
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Pascal den Boef, Technical University Eindhoven |
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Philipp Horn,
Technical University Eindhoven |
Structure-Preserving Neural Networks for Hamiltonian Systems |
Emil Løvbak, KU Leuven |
Adjoint Monte Carlo particle methods with reversible random number generators |