
Jonathan Seibel
Fähigkeiten und Kenntnisse
Werdegang
Berufserfahrung von Jonathan Seibel
Munich Re Markets - Structuring Team Asia
Derivatives Counterparty Risk Methodology
- Implemented the particle method for calibration of Local Stochastic Volatility Models - Developed a parametric Local Stochastic Volatility Model as a parsimonious extension of the Heston Model which is capable of producing exploding implied volatility short-term skew - Continued to work on pricing and hedging of autocallables and used my own implemented exotic derivative pricing engine for benchmarking against Bloomberg's Derivative Pricing Library (DLIB)
- Implemented an arbitrage-free SVI calibration routine which was integrated into production - Implemented a prototype of Dupire's Local Volatility Model for pricing of exotic derivatives - Structuring of credit and equity derivatives (e.g. autocallables)
- Quantitative analyses in collaboration with the Financial Engineering Team - Support of the Investment Management Team and handling of client inquiries
- 1 Jahr und 1 Monat, Mai 2015 - Mai 2016
Working Student
Zuse Institut Berlin
- Development of new algorithmic approaches to solve linear multi-criteria mixed-integer problems - Development and implementation of cluster algorithms for the evaluation of long-term technology scenarios - Modelling of different kinds of optimisation problems - Analysis and evaluation of mathematical algorithms For most of my projects used Python in combination with SCIP, one of the fastest non-commercial solvers for mixed integer programming developed by the Zuse Institute Berlin.
Ausbildung von Jonathan Seibel
- 2016 - 2017
Financial Mathematics
University of California, Santa Barbara
Stochastic Calculus
- 4 Jahre, Okt. 2014 - Sep. 2018
Mathematik
TU Berlin
Probability Theory, Stochastic Calculus, Financial Mathematics and Scientific Computing
- 3 Jahre, Okt. 2011 - Sep. 2014
Physik
Georg-August-Universität Göttingen
Theoretical Physics and Computational Physics
Sprachen
Englisch
Fließend
Deutsch
Muttersprache
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