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Can idiosyncratic volatility help forecast stock market volatility?

机译:特质波动可以帮助预测股市波动吗?

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This paper examines the predictive power of idiosyncratic volatility in the context of daily stock market volatility dynamics. Specifically, the relative performance of various models of market volatility is considered with respect to whether idiosyncratic volatility is excluded or included as an explanatory variable in such models. Using high frequency data covering the thirty stocks within the Dow Jones Industrial Average (DJIA) index, the results indicate that the inclusion of idiosyncratic volatility leads to significant in-sample and out-of-sample improvements in the fit of all the volatility models considered. These results are shown to be relatively robust to the loss function adopted by the forecaster, with reasonable forecast accuracy improvements available to such forecasters.
机译:本文研究了在每日股市波动动态的背景下特质波动的预测能力。具体而言,考虑到各种市场波动率模型的相对表现,是关于在这种模型中是否排除或包括特质波动率作为解释变量。使用涵盖道琼斯工业平均指数(DJIA)指数中30只股票的高频数据,结果表明,特质波动率的引入导致所有考虑的波动率模型的拟合均产生明显的样本内和样本外改进。 。这些结果显示出相对于预测器采用的损失函数相对稳健,并且此类预测器可以使用合理的预测精度改进。

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