This paper aims to accurately forecast US stock market volatility by using international market volatility information flows. The results show the significant ability of the combined international volatility information to predict US stock volatility. The predictability is found to be both statistically and economically significant. Furthermore, in this framework, we compare the performance of a large set of approaches dealing with multivariate information. Dynamic model averaging (DMA) and dynamic model selection (DMS) perform better than a wide variety of competing strategies, including the heterogeneous autoregressive (HAR) benchmark, kitchen sink model, popular forecast combinations, principal component analysis (PCA), partial least squares (PLS), and the ridge, lasso, and elastic net shrinkage methods. A wide range of extensions and robustness checks reduce the concern regarding data mining. DMA and DMS are also able to significantly forecast international stock market volatilities.

中文翻译：

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预测美国股市波动：如何使用国际波动信息

本文旨在利用国际市场波动信息流来准确预测美国股市的波动。结果表明，综合国际波动率信息对预测美国股票波动率具有显着的能力。发现可预测性在统计上和经济上都是显着的。此外，在这个框架中，我们比较了大量处理多元信息的方法的性能。动态模型平均 (DMA) 和动态模型选择 (DMS) 的性能优于各种竞争策略，包括异构自回归 (HAR) 基准、厨房水槽模型、流行的预测组合、主成分分析 (PCA)、偏最小二乘法(PLS)，以及脊、套索和弹性净收缩方法。广泛的扩展和健壮性检查减少了对数据挖掘的关注。DMA 和 DMS 还能够显着预测国际股市的波动。