Dr Katia Amrit Babbar

I have been a visiting lecturer for the MSc Mathematics and Computational Finance programme since 2018, where I teach Statistics and Financial Data Analysis. The emphasis of the course is on practical applications, with a large element of coursework required to be delivered in Python. I am also looking forward to teaching a new elective "Blockchain, Crypto and DeFi" offered as part of the MSc MCF course, starting in 2022.

I have a practitioner's background: I have spent most of my career in the Financial Markets industry, leading teams of Quantitative Analysts and an e-FX Algorithmic Trading Desk, with an experience spanning 20+ years across various Financial Institutions in the City. Much of my work was dedicated to financial models for the Foreign Exchange (FX) Options market (vanillas and exotics), with a focus on real-world enterprise implementation, where robustness and speed of risk management are key. The majority of this work was of a proprietary nature, though recently I have spoken about the use of Deep Neural Networks for Local Stochastic Volatility valuation of large exotic portfolios, for example. 

Since 2020, my key research interests have been in Crypto and Decentralized Finance (DeFi). With my students, I am researching the close relationship between traditional financial central limit order books (CLOBs) and Automated Market Makers on Liquidity Pools. 

I have also co-founded a FinTech to provide robust risk management solutions for Crypto Derivatives and AMMs.

I am a strong advocate of collaboration and team work: I believe diversity of thinking and true inclusivity have the potential to unlock much untapped creativity and talent. "Alone we go faster, together we go further".

I hold a BSc in Mathematics from University College London and a PhD Financial Mathematics from Imperial College London, "Aspects of Stochastic Implied Volatility in Financial Markets" (co-supervised by Prof. Terry Lyons and Prof. Mark Davis).