基于机器学习的债券风险溢价

Daniele Bianchi, M. Büchner, A. Tamoni
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引用次数: 63

摘要

我们表明,机器学习方法,特别是极端树和神经网络(nn),为债券回报的可预测性提供了强有力的统计证据。基于宏观经济和收益率信息的神经网络预测转化为比仅使用收益率获得的经济收益更大的经济收益。有趣的是,无跨度因素的性质沿着收益率曲线变化:股票和劳动力市场相关变量与短期期限更相关,而产出和收入变量对长期期限更重要。最后,神经网络预测与时变风险厌恶和不确定性的代理相关,为具有这两个渠道的模型提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bond Risk Premia with Machine Learning
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 short-term 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 of these channels.
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