This tutorial demonstrates porting an existing machine learning model to a virtual machine on the Microsoft Azure cloud platform. We will train a small movie recommendation model using a single GPU to give personalised recommendations. The total cost of performing this training should be no more than $5 using any of the single GPU instances currently available on Azure.
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.