Estimation of the ex ante Distribution of Returns for a Portfolio of U.S. Treasury Securities via Deep Learning

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 bu...

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Bibliographic Details
Main Author: Foresti, Andrea
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2019
Subjects:
MACHINE LEARNING
NEURAL NETWORKS
CONVOLUTION
LSTM
MARKET RISK
SECURITIES PORTFOLIO
Online Access:http://documents.worldbank.org/curated/en/433791553192242300/Estimation-of-the-ex-ante-Distribution-of-Returns-for-a-Portfolio-of-U-S-Treasury-Securities-via-Deep-Learning
http://hdl.handle.net/10986/31449

Internet

http://documents.worldbank.org/curated/en/433791553192242300/Estimation-of-the-ex-ante-Distribution-of-Returns-for-a-Portfolio-of-U-S-Treasury-Securities-via-Deep-Learning
http://hdl.handle.net/10986/31449

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