Videos & TutorialsSee it with your own eyes
- Fundamentals of approximation theory
- The power of the Chebyshev techniques; the maths behind them
- How our software library works
- Applications of the Chebyshev framework to enhance risk calculations
Practical applications of our technology
We were invited by Bank of America in London to present our research around risk calculation optimisation via Chebyshev and Machine Learning methods.
Chebyshev provides the tools for an accurate and ultra-fast stochastic simulation of dynamic sensitivities and Initial Margin. Find out how in this video
In this talk we present results obtained within the systems of a tier-1 bank for a capital calculation within FRTB IMA, using Chebyshev tensors to massively accelerate and economise the calculation while retaining a high level of accuracy required by the regulation.
This video is similar to the one titled “Machine Learning + Chebyshev for Risk calculations”, but making more emphasis on the IMA-FRTB results at at a Tier-1 bank.
In this presentation we see how Chebyshev Tensors and Machine Learning techniques can be used in the calculation of Dynamic Initial Margin (DIM). We start by giving an overview of the main mathematical properties behind Chebyshev Tensors. Then we see how these can be used to approximate pricing functions within risk calculations to alleviate the huge computational burden associated with them. Finally we explain how Chebyshev Tensors can be used in the calculation of DIM and present DIM calculations obtained with Chebyshev Tensors, Deep Neural Networks and other regression types.
Our software library
We describe how to use Chebyshev Tensors in combination with Machine Learning techniques to compute risk calculations efficiently.
This video is similar to the one titled “Computational Challenge of IMA FRTB”, but making more emphasis on the Machine Learning part of the algorithms.
Maths and Methods – fundamentals
Maths and Methods – more advanced stuff