PhD Quantitative analysis consulting for finance professionals.
Custom built financial algorithms in C++/python/VBA for trading, investing, insuring and financial risk.
My name is Lorenz and I completed a PhD in pure mathematics and worked as a university lecturer before transitioning into finance. I work as a quantitative analyst doing such things as modelling of credit and market risk, derivative pricing and model validation.
Learn how I bested one of the top four financial consulting firms, which has a net revenue in the tens of billions.
My quantitative consulting services include:
- Development of financial risk models including operational, credit, and market risk models
- Development of derivative pricing models including Black-Scholes, Monte Carlo, finite difference and Longstaff-Schwartz
- Monte Carlo risk models and dependency modelling tools such as copulas
- Designing mathematical algorithms for trading or investing in languages like C++, Python and VBA. See Algorithmic Trading Consulting Services.
- Model validation of risk and pricing models, including sensitivity and stress testing
- Code maintenance and debugging
- Financial applications of data science and machine learning, including doing financial computations on Nvidia GPUs.
- PhD-level mathematical research projects (see mathematics consulting)
Check out my LinkedIn profile
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Would you like to design Monte-Carlo risk models which complete in 15 minutes instead of taking all day?
Have you ever wondered why heavy tailed risk models dramatically overstate the median loss?
Learn how to convert volatility surfaces from moneyness to delta
Mathematical modelling is everywhere in the financial services industry, but the expertise to do it in a meaningful way is thin on the ground. I speak from personal experience when I say that at many banks virtually every model contains not one, but several critical errors that render the output meaningless. I was able to find material errors that had been overlooked by internal validators, external validators and the regulator! In fact, I’ve seen expensive risk modelling work from one of the big four financial consulting firms where, had they simply taken a guess at what the VAR would be, it would have been more useful. Simple models are just as likely to be erroneous as sophisticated ones. And business managers are left frustrated, unable to understand why the regulators are never satisfied.
I therefore believe that my ability to validate financial models is virtually second to none.
Some finance managers may harbor the stereotype that quants like models to be as complex as possible. However, In my work as a risk modeller, I reduced the length of code in the bank’s operational risk model by about a factor of ten, and recoded it in a clean, object-oriented format that was easy to follow. Furthermore, I was able to reduce the run time from 6 hours to around 30 minutes. This meant that sensitivity testing of the model now took a day, instead of weeks. When the financial regulatory recommended the model be simplified, I was able to remove about 80% of the model’s mathematical complexity while leaving the model output unchanged within simulation error. This streamlining of the model greatly reduced the bank’s workload around running, maintaining and validating the model, with no loss of function.
Keeping in mind the poor quality of much of the quantitative work in the finance industry, I believe there is considerable scope for firms to gain a competitive advantage from my consulting services. I also offer sophisticated mathematical research services around things like data science and machine learning.
To register your interest, simply contact us.
