In finance, computation efficiency can be directly converted to trading profits sometimes. Quants are facing the challenges of trading off research efficiency with computation efficiency. Using Python can produce succinct research codes, which improves research efficiency. However, vanilla Python code is known to be slow and not suitable for production. In this post, I explore how to use Python GPU libraries to achieve the state-of-the-art performance in the domain of exotic option pricing.
This is a collection of Jupyter notebooks based on different topics in the area of quantitative finance. Wow!
NAG has developed, in collaboration with Xi-FINTIQ, a CVA demonstration code to show how the NAG Library and NAG Algorithmic Differentiation (AD) tool dco/c++ combined with Origami – a Grid/Cloud Task Execution Framework available through NAG – can work together to solve large scale CVA computations.
Calculating Black-Scholes implied volatilities is a key part of financial modelling, and is not easy to do efficiently.
The benchmark in this field is the iterative method due to Peter Jaeckel (2015), though some banks have their own methods. NAG have teamed up with Dr Kathrin Glau and her colleagues from Queen Mary University of London to see whether their research in Chebyshev interpolation could be combined with NAG’s expertise in efficient computing to provide a faster way of obtaining implied volatilities.