Apr 23, 2020 8:00 PM
Registration deadline: Apr 23, 2020

Machine Learning + Chebyshev Techniques for Risk Calculation: Boosting each other

by Mariano Zeron, PhD - Head of Research & Development, MoCaX | Online


The computation of risk metrics poses a huge computational challenge to banks. Many different techniques have been developed and implemented in the last few years to try and tackle the problem. We focus on Chebyshev tensors and their strong mathematical properties, and combine them with ML-like techniques to overcome the curse of dimensionality challenge. In this webinar we show why they are such powerful pricing approximators and how they can be applied in different risk calculations. Numerical results are presented to illustrate the computational gains Chebyshev tensors bring to calculations of Counterparty Credit Risk (CVA, IMM capital, PFE), Market Risk (FRTB, full-reval VaR) and Dynamic Initial Margin.

Mariano Zeron, PhD - Head of Research & Development, MoCax Intelligence

Mariano heads the Research & Development group at MoCaX Intelligence. He has vast experience in Chebyshev Spectral Decomposition, Machine-Learning and related disciplines, and their application to quantitative problems in the financial markets. Mariano holds a Ph.D. in Pure Mathematics from the University of Cambridge.

Registration deadline: Apr 23, 2020

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