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

- 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.

Stochastic Calculus

Probability Theory, Stochastic Calculus, Financial Mathematics and Scientific Computing

Theoretical Physics and Computational Physics