Mr Babak Mahdavi-Damghani
I have been working in the financial industry within a broad range of functions going from trading (exotics & high frequency), structuring (hybrid) to quantitative analytics (front office, risk and CCP).
I have experience in most asset classes (equities, commodities, FX and rates) in both the buy and the sell side across different geographical locations in Europe.
I completed my undergraduate education at the University of Pennsylvania in what would now correspond to financial engineering and my post-graduate studies are in several areas of the applied mathematical and computational sciences at the University of Oxford, University of Cambridge and Ecole Polytechnique. I am also the author of publications, including cover stories of quantitative finance journals, some of which contain models that are now taught in the CQF.
My current research is in the application of Machine Learning (ML) to Quantitative Finance, broadly subdivided in 3 themes:
- Electronic Trading: more specifically the formalisation of the High Frequency Trading Ecosystem (HFTE) which main point exposes how the mix of genetic algorithm and neural network architecture may help us, explain the fluctuations of the markets through an ecosystem of strategies competing with each other.
- Data Analysis and Patterns in Data: an example of this being the handling of dimensionality in the context of options modelling. My most recent work being the formalisation of the Implied Volatility surface Parametrisation (IVP).
- Stability of Financial Systems: more specifically the translation and enhancement of classic stochastic differential equation to the world of ML. Some of my recent work in that domain would include the development of trading and risk models such as the Cointelation and the Bandwise Gaussian Mixture models.
I am working with Prof. Steve Roberts and have won a merit based full award studentship (university fees + college fees + stipend) supported by the University of Oxford Social Sciences Doctoral Training Centre (DTC) ESRC.
For recent publications, go to: http://eqrc.co.uk/downloads/