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首页> 外文会议>Proceedings of the Third IASTED International Conference on Financial Engineering and Applications >INCREMENTAL INTRADAY PREDICTION OF EXTREME VALUES AND RANGE-BASED VOLATILITY
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INCREMENTAL INTRADAY PREDICTION OF EXTREME VALUES AND RANGE-BASED VOLATILITY

机译：极值和范围波动率的日内增量预测

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摘要

Range-based volatility is one of the major streams in the volatility study. Garman and Klass [1] proposed an efficient volatility estimator based on the extreme values, open and close. Conventionally, the estimator can be obtained at the market close or can be predicted with a limited accuracy by its lagged values. Nowaday, the latest and cumulative intraday data, which embeds a rich informational content, is ignored in the prediction model while it definitely enhances the accuracy. In this paper, we introduce an incremental intraday prediction problem to explore a unidirectional causal relationship between the volatility estimator and the incremental information. Generally, the causal effect due to the incremental information can reflect through a proper selection of the incremental information content and should increase as the time instant on the same trading day. Two different incremental information contents as exogenous input of a linear model are selected to demonstrate these features.In addition to the selection of exogenous input, we believe that the selection of model also affects the causal effect.We examine the relationship using a linear and a nonlinear neural networks. Empirical results show that the prediction accuracy of nonlinear model is better than that of linear model. It implies that the nonlinear model can reflect the causal effect in a better way.
机译：基于范围的波动率是波动率研究的主要动力之一。 Garman和Klass [1]根据开放和闭合的极值提出了一种有效的波动率估计器。通常，估计器可以在市场收盘时获得，或者可以通过其滞后值以有限的精度进行预测。如今，预测模型中忽略了嵌入了丰富信息内容的最新且累积的日内数据，同时它无疑提高了准确性。在本文中，我们引入了增量日内预测问题，以探讨波动率估计量与增量信息之间的单向因果关系。通常，由于增量信息引起的因果效应可以通过适当选择增量信息内容来反映，并且应随着同一交易日的瞬时时间而增加。选择两个不同的增量信息内容作为线性模型的外来输入来演示这些特征。除了选择外源输入外，我们认为模型的选择也会影响因果关系，我们使用线性和线性来检验关系非线性神经网络。实证结果表明，非线性模型的预测精度优于线性模型。这意味着非线性模型可以更好地反映因果关系。

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