Estimation of the Ex Ante Distribution of Returns for a Portfolio of U.S. Treasury Securities Via Deep Learning
This paper presents different deep neural network architectures designed to forecast the distribution of returns on a portfolio of U.S. Treasury securities. A long short-term memory model and a convolutional neural network are tested as the main building blocks of each architecture. The models are then augmented by cross-sectional data and the portfolio's empirical distribution. The paper also presents the fit and generalization potential of each approach
Year of publication: |
2019
|
---|---|
Authors: | Foresti, Andrea |
Publisher: |
[2019]: [S.l.] : SSRN |
Subject: | USA | United States | Kapitaleinkommen | Capital income | Portfolio-Management | Portfolio selection | Staatspapier | Government securities |
Saved in:
freely available
Extent: | 1 Online-Ressource (28 p) |
---|---|
Series: | World Bank Policy Research Working Paper ; No. 8790 |
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 21, 2019 erstellt |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012889888
Saved in favorites
Similar items by subject
-
Chua, Choong Tze, (2005)
-
Foresti, Andrea, (2019)
-
Girardin, Eric, (2010)
- More ...
Similar items by person