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Bond Risk Premiums with Machine Learning
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DOI：10.1093/rfs/hhaa062.png)
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We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for shortterm maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.
Keyword:
TERM STRUCTURE MODELS
TIME-SERIES
PREDICTABILITY
COMBINATION
RETURNS
RATES
FORECASTS
NETWORKS
NUMBER
TESTS
期刊
IF:
5.4
论文数:
2.8K
被引数:
3.0W
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Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks.png)
JOURNAL OF FINANCE
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