We propose a sparse vector heterogeneous autoregressive (VHAR) model for realized volatility forecasting. As a multivariate extension of a heterogeneous autoregressive model, a VHAR model can consider the dynamics of multinational stock volatilities in a compact manner. A sparse VHAR is estimated using adaptive lasso and some theoretical properties are provided. In practice, our sparse VHAR model can improve forecasting performance and explicitly show the connectivity between stock markets. In particular, our empirical analysis shows that the NASDAQ market had the strongest influence on stock market volatility worldwide in the 2010s.

中文翻译：

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稀疏向量异构自回归建模以实现波动性

我们提出了一种稀疏矢量异构自回归（VHAR）模型，用于实现波动率预测。作为异质自回归模型的多元扩展，VHAR模型可以紧凑地考虑跨国股票波动的动态。使用自适应套索估计稀疏VHAR，并提供一些理论属性。实际上，我们的VHAR稀疏模型可以提高预测性能，并明确显示股市之间的联系。特别是，我们的经验分析表明，纳斯达克市场对2010年代全球股票市场的波动影响最大。