Apart from the percentage change in the price of crude oil, there is a growing tradition of using various nonlinear transformations of the price of crude oil to forecast real gross domestic product growth rates, equity returns, inflation and other macroeconomic variables. This study attempts to quantify the additional potential predictive power afforded by crude oil price volatility relative to widely used crude oil price-based variables for more than 300 US macroeconomic time series at the monthly and the quarterly sampling frequency. We observe that regressions employing crude oil price realized volatility and crude oil price realized semivolatilities tend to afford a more consistent pattern of out-of-sample prediction gains relative to competitors using well-known crude oil price measures and the autoregressive benchmark at the quarterly and monthly sampling frequency. While it is somewhat harder to find evidence of finite-sample predictive gains relative to the benchmark, the evidence is stronger with respect to population-level predictability 1 quarter (1 month) ahead for the model with crude oil price realized semivolatilities across the considered data and models. Furthermore, point (density) forecasts employing crude oil price realized volatility tend to be more accurate than corresponding forecasts produced under the crude oil price-based predictive regressions in a horse race.

中文翻译：

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原油价格波动是否应该比原油价格更受关注？基于美国宏观经济数据的大规模样本外预测评估的实证研究

除了原油价格的百分比变化之外，使用原油价格的各种非线性变换来预测实际国内生产总值增长率、股票回报、通货膨胀和其他宏观经济变量的传统越来越多。本研究试图量化原油价格波动提供的额外潜在预测能力，相对于广泛使用的基于原油价格的变量，在月度和季度采样频率下，美国 300 多个宏观经济时间序列。我们观察到，相对于使用众所周知的原油价格指标和季度和自回归基准的竞争对手，使用原油价格实现波动率和原油价格实现半波动率的回归倾向于提供更一致的样本外预测收益模式。每月采样频率。虽然找到相对于基准的有限样本预测收益的证据有些困难，但对于模型而言，在 1 个季度（1 个月）之前的人口水平可预测性方面的证据更强，其中原油价格在所考虑的数据中实现了半波动性和模型。此外，