Generative Adversarial Networks for Finance
Generative Adversarial Networks for Finance


The Gaussian assumption in the Black-Scholes formula for option pricing has proven its limits. Although it is a good approximation, market returns do not adhere exactly to a gaussian curve. This is all the more important since pricing options correctly is a very competitive task. Today, Generative Adversarial Networks (GANs) are the new golden standard for simulation. It has worked wonders in image generation, but can it be applied to option pricing? Here is the story of how 2 data scientists (inc. a former trader) deployed a GAN for option pricing in real-time, in 10 days.


I have been a happy data scientist at Dataiku for 3 years, applying Machine Learning (but not only) to solve real issues. To me, putting a data project in production is like writing a good Haiku. Prior to that, I was involved in building the data science team at Capgemini Consulting. I started my career in economics, so I like my models interpretable as well as deep. I am into travelling, learning new things and crafting useful products.

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google