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 t...
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| Format: | eBook |
| Language: | English |
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Washington, D.C
The World Bank
2019
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| Series: | World Bank E-Library Archive
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| Online Access: | |
| Collection: | World Bank E-Library Archive - Collection details see MPG.ReNa |
| Summary: | 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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| Physical Description: | 28 pages |