CLICK HERE TO DOWNLOAD THE PDF
Brian Huge and Antoine Savine combine automatic adjoint differentiation with modern machine learning. In addition, they introduce general machinery for training fast, accurate pricing and risk approximations, applicable to arbitrary transactions or trading books, and arbitrary stochastic models, effectively resolving the computational bottlenecks of derivatives risk reports and regulations
Pricing approximation has proved tremendously useful with advanced
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact [email protected] to find out more.
You are currently unable to copy this content. Please contact [email protected] to find out more.
The post Differential machine learning: the shape of things to come – Risk.net appeared first on abangtech.
source https://abangtech.com/differential-machine-learning-the-shape-of-things-to-come-risk-net/
No comments:
Post a Comment