Interest Rate Exotics
Specialised pricing for Swaptions, Range Accruals, and Bermudans using SABR/LMM frameworks.
QUANTITATIVE FINANCE AND MODEL ENGINEERING
- Model Prototyping & Integration
- Quantitative Risk Frameworks
- ESG & Climate Risk Analytics
- Exotic Derivatives Pricing
- Algorithmic Calibration
- Regulatory Validation
- Artificial Intelligence
We design, modernise, and validate institutional risk and pricing frameworks — combining financial engineering, regulatory expertise, and machine learning into production-grade systems.
References
Project references available upon request.
Derivatives Valuation
Calibration and independent validation...
Regulatory Frameworks
Incorporation and enhancement of ICAAP...
(Causal) Artificial Intelligence
Practical expertise in applying AI...
ESG Integration
Incorporation of ESG and climate-related risk factors...
Data Analytics & Governance
Design of BCBS 239–aligned data governance frameworks...
Meet Our Team
With more than 25 years of experience in quantitative finance and risk management, I advise financial institutions on the design, validation, and implementation of advanced risk‑modelling frameworks. My work focuses on strengthening market and credit risk architectures, including Value‑at‑Risk (VaR), Expected Shortfall (ES), stress testing, IRRBB/CSRBB, and ESG‑integrated credit models.
I support banks, exchanges, energy firms, and fintechs in building robust, production‑ready quantitative solutions grounded in rigorous mathematics and implemented efficiently in Python. Experience includes internal model development and validation, historical‑simulation and Monte Carlo engines, macroeconomic stress modelling, and LSI stress‑test implementations aligned with supervisory expectations.
Current research interests (kept high-level while academic work is ongoing):
Project 1: Modern RFR interest‑rate analytics: examining behaviour of risk‑free rate term structures (SOFR‑OIS, ESTR‑OIS, SONIA‑OIS) across currencies and market regimes.
Project 2: Causal AI & event analysis in sovereign yields: using causal‑inference approaches to assess how ESG‑relevant policy/news flow may influence government bond yields.
Alongside consulting, I teach Quantitative Risk Analysis and Financial Engineering.
Benedikt holds a master degree in banking and finance with a major in capital markets and data science accompanied by the receipt of two academic honours. He leverages artificial intelligence to automate the complete lifecycle of quantitative finance, from engineering predictive trading algorithms, deploying low-latency execution infrastructure and accelerating the calibration of stochastic volatility models.
Timo is in the final-year of his undergraduate studies in International Finance (BSc). He has received several academic honours and scholarships and has also completed academic studies in the US. His expertise includes time series modelling, valuation of fixed income products and quantitative portfolio optimisation. He has worked on the specification, calibration and validation of quantitative models as well as the design and deployment of data-driven web applications.
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