Value at Risk Estimation with Neural Networks – Master thesis in cooperation with Royal Institute of Technology (KTH) July 1, 2021 News William Karlsson Lille and Daniel Saphir from Royal Institute of Technology (KTH) have published a master thesis in collaboration with Scila. Their thesis “Value at Risk Estimation with Neural Networks: A Recurrent Mixture Density Approach” investigates if neural network techniques can be used to improve value at risk estimations. More specifically, the thesis focuses on recurrent neural networks and a mixture density output layer for generating mixture density distributions of future portfolio returns from which value at risk estimations are derived. Download the article via the following link: Download Home Surveillance Risk AML Buy-Side Compliance Customer Examples News Careers About us Scila AB Sveavägen 17 SE-111 57 Stockholm Sweden Scila Pte. Ltd. 68 Circular Road, #02-01 Singapore (049422) info@scila.se sales@scila.se support@scila.se Staff email addresses: name.surname@scila.se © Copyright 2021. All Rights Reserved