一种替代长视界回归的频域方法及应用于回归可预测性

N. Sizova
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引用次数: 8

摘要

本文旨在提高噪声序列(如股票市场收益)长期可预测性检验的准确性。长期视界回归以前是这一领域的主要方法。我们建议另一种方法产生更准确的结果。我们发现,即使使用基于子抽样的无模型方法构建置信区间,标准普尔500指数的回报也具有可预测性。
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A Frequency-Domain Alternative to Long-Horizon Regressions with Application to Return Predictability
This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of predictability in S&P 500 returns even when the confidence intervals are constructed using model-free methods based on subsampling.
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