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首页> 外文期刊>Communications in Statistics >Do we need the constant term in the heterogenous autoregressive model for forecasting realized volatilities?
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Do we need the constant term in the heterogenous autoregressive model for forecasting realized volatilities?

机译:我们是否需要异质自回归模型中的常数项来预测已实现的波动率?

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摘要

No-constant strategy is considered for the heterogenous autoregressive (HAR) model of Corsi, which is motivated by smaller biases of its estimated HAR coefficients than those of the constant HAR model. The no-constant model produces better forecasts than the constant model for four real datasets of the realized volatilities (RVs) of some major assets. Robustness of forecast improvement is verified for other functions of realized variance and log RV and for the extended datasets of all 20 RVs of Oxford-Man realized library. A Monte Carlo simulation also reveals improved forecasts for some historic HAR model estimated by Corsi.
机译:对于Corsi的异质自回归(HAR)模型,考虑采用非恒定策略,这是因为其估计的HAR系数的偏差要比恒定HAR模型的偏差小。对于某些主要资产的已实现波动率(RVs)的四个真实数据集,非常数模型产生的预测要好于常数模型。对于实现方差和对数RV的其他功能以及牛津曼实现库的所有20个RV的扩展数据集,验证了预测改进的稳健性。蒙特卡洛模拟还揭示了由Corsi估算的某些历史性HAR模型的改进预测。

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