Predictive density estimators for daily volatility based on the use of realized measures
Predictive density estimators for daily volatility based on the use of realized measures
复制标题
基于使用已实现测量的每日波动率的预测密度估计器
DOI：
10.1016/j.jeconom.2008.12.015
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发表时间：
2009
影响因子：
6.3
通讯作者：
Corradi V
中科院分区：
文献类型：
--
作者：
Corradi V
本文的主要目的是提出一个可行的，无模型估计的预测密度的综合波动率。在这个意义上说，我们延长最近的论文安德森等人。[Andersen，T.G.，Bollerslev，T.，Diebold，F. X.，Labys，P.，2003.已实现波动率的建模和预测。Econometrica 71，579-626]，以及Andersen et al. [Andersen，T.G.，Bollerslev，T.，Meddahi，N.，2004.波动性预测的分析评估。国际经济评论45，1079-1110; Andersen，T.G.，Bollerslev，T.，Meddahi，N.，2005.纠正错误：使用高频数据和已实现波动率进行波动率预测评估。Econometrica 73，279-296]，他们基于已实现波动率的使用，通过阿尔马模型解决了波动率的逐点预测问题。我们的方法是使用已实现波动率度量来构建每日波动率预测密度的非参数（核）估计量。我们发现，通过选择一个适当的实现措施，可以实现一致的估计，即使在跳跃和微观结构噪声的价格，更准确地说，我们建立了四个著名的实现措施，即实现波动率，双幂变化，和两个措施的微观结构噪声的鲁棒性，满足我们的估计一致一致性所需的条件。此外，我们概述了另一种基于模拟的方法来预测密度建设。最后，我们进行了模拟实验，以评估我们的估计的准确性，并提供了一个实证说明，强调使用高频数据时，使用微观结构的鲁棒性措施的重要性。
The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen et al. [Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2003. Modelling and forecasting realized volatility. Econometrica 71, 579–626], and by Andersen et al. [Andersen, T.G., Bollerslev, T., Meddahi, N., 2004. Analytic evaluation of volatility forecasts. International Economic Review 45, 1079–1110; Andersen, T.G., Bollerslev, T., Meddahi, N., 2005. Correcting the errors: Volatility forecast evaluation using high frequency data and realized volatilities. Econometrica 73, 279–296], who address the issue of pointwise prediction of volatility via ARMA models, based on the use of realized volatility. Our approach is to use a realized volatility measure to construct a non-parametric (kernel) estimator of the predictive density of daily volatility. We show that, by choosing an appropriate realized measure, one can achieve consistent estimation, even in the presence of jumps and microstructure noise in prices. More precisely, we establish that four well known realized measures, i.e. realized volatility, bipower variation, and two measures robust to microstructure noise, satisfy the conditions required for the uniform consistency of our estimator. Furthermore, we outline an alternative simulation based approach to predictive density construction. Finally, we carry out a simulation experiment in order to assess the accuracy of our estimators, and provide an empirical illustration that underscores the importance of using microstructure robust measures when using high frequency data.