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.
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| Main Author: | |
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| Format: | Working Paper biblioteca |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019-03
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| 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 https://hdl.handle.net/10986/31449 |
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