Katia Babbar

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Exotic Options Valuations Using Deep Learning

The market standard for the pricing and risk management of complex derivatives within the Foreign Exchange markets uses a local-stochastic volatility (LSVOL) model. This type of model captures the relevant market dynamics but is computationally very expensive. Given a typical trading book can consist of tens of thousands of such contracts, investment banks maintain large scale compute grids to manage the risk of these products. Using Artificial Neural Networks and exploiting the Universal Approximation Theorem, these contracts can be valued orders of magnitude faster. Katia will explore this innovative approach, which is a radical departure from the traditional quantitative finance methodology prevalent in banks.

Katia has been Head e-FX Algorithmic Trading, MD at Lloyds Banking Group for the past 4 years leading teams of quantitative traders and data scientists. In this role, Katia explored market microstructure for market-making and trade execution, employing cutting edge Machine Learning techniques. Previously, Katia was Head of FX Quantitative Research at Lloyds for 8 years, building their entire derivatives analytics, pricing and risk management systems from the ground up. Katia started her career at UBS as a Quantitative Analyst in FX in 2001. She has also worked at Citi as a Senior Quant, building their correlation business and working across their complex exotics trading books. Recently, Katia has founded AI Wealth Technologies. This start up looks at using AI in investment portfolio selection and allocation. Katia holds a BSc in Mathematics from UCL and PhD in Stochastic Analysis applied to Finance from Imperial College.

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