论文标题：Factor High-Frequency Based Volatility (HEAVY) Models

　　发表时间：2019

　　论文所有作者：Kevin Sheppard, Wen Xu

　　期刊名及所属分类：Journal of Financial Econometrics(国际A)

　　英文摘要：We propose a new class of multivariate volatility models utilizing realized measures of asset variance and covariance extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationarity of returns, are established. The performance of the model is assessed using a panel of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covariances, betas, and scenario-based risk measures. The model’s performance is particularly strong at short forecast horizons.

　　中文摘要：利用从高频数据中提取的资产方差和协方差，我们提出了一类新的多元波动模型。对协方差矩阵的估计进行降维是通过施加具有时变条件因子载荷的因子结构来实现的。建立了模型的统计性质，包括保证收益协方差平稳性的条件。在金融危机期间，我们使用一组美国大型金融机构对模型的性能进行评估，实证结果表明，新模型在样本内和样本外都具有优越的属性。我们表明，优越的性能适用于广泛的数量的利息，包括波动率、协方差、beta和基于场景的风险度量。该模型在短期预测范围内的表现尤其强劲。