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首页> 外文期刊>Journal of Industrial Engineering and Management >Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component
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Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component

机译：提高准确性：使用高频数据和变分成分进行波动率建模和预测

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In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the realized volatility. We also tried to investigate whether dividing volatility into simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The results provide the evidence that inclusion of realized bipower variance in the HAR models helps in predicting future volatility.
机译：在这项研究中，我们使用基本的“异构自回归”（HAR）及其变体来预测印度S＆P CNX NIFTY市场指数的每日波动。为此，我们使用不同的回归变量估计了几种HAR模型和HAR模型的Log形式。通过从实现的波动率中提取跳跃和连续成分以及阈值跳跃和连续成分，可以获得不同的回归变量。我们还尝试研究将波动率分为简单波动和阈值跳跃以及连续变化是否会大幅改善波动率预测。结果提供了证据，即在HAR模型中包含已实现的双功效方差有助于预测未来的波动性。

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