ER-RAMMACH, M. Ilyes et MIKAEL, M. Joseph et NGUYEN, M. Pascal (2021) Automated research of neural networks and application to stochastic control problem PFE - Project Graduation, ENSTA.
This is the latest version of this item.
AutoML is the attempt to automate the tedious task of developing an end-to-end machine learning model for a specific task. In the literature, many techniques have been developed, but most of its applications only concern vision task or natural language processing. Applications on stoschastic control problem sush a risk hedging have not been reported to our knowledge. Among the numerous AutoML techniques, we have adapted one of them based on gradient descent for the Black-Scholes options hedging problem. In this report, we study its performances and also propose some original attepts or some methods based on previous works to improve its stability.
|Item Type:||Thesis (PFE - Project Graduation)|
|Subjects:||Mathematics and Applications|
|Deposited By:||Ilyes ER-RAMMACH|
|Deposited On:||19 oct. 2021 16:18|
|Dernière modification:||19 oct. 2021 16:18|
Available Versions of this Item
- Automated research of neural networks and application to stochastic control problem (deposited 19 oct. 2021 16:18) [Currently Displayed]
Repository Staff Only: item control page