A Multiple Indicators Model for Volatility Using Intra-Daily Data
A Multiple Indicators Model for Volatility Using Intra-Daily Data
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DOI：
10.3386/w10117
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发表时间：
2003-10
期刊：
影响因子：
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通讯作者：
G. Gallo;R. Engle
中科院分区：
文献类型：
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作者：
G. Gallo;R. Engle
存在许多方法来测量和建模金融资产波动性。原则上，随着数据频率的增加，预测的质量应该会提高。然而，对于一个真正的“或最好的”波动性衡量标准，人们并没有达成共识。在本文中，我们提出了联合考虑绝对日收益率，每日最高-最低范围和每日已实现的波动率，建立一个预测模型的基础上，他们的条件动态。由于所有的非负序列，我们开发了一个乘法误差模型，是一致的，渐近正常的误差密度函数的规格范围很广。估计结果表明，指标之间的显着的相互作用。我们还表明，一个月前的预测匹配以及（无论是在和样本外）基于市场的波动率的指标所提供的平均隐含波动率的指数期权，衡量波动率。
Many ways exist to measure and model financial asset volatility. In principle, as the frequency of the data increases, the quality of forecasts should improve. Yet, there is no consensus about a true' or best' measure of volatility. In this paper we propose to jointly consider absolute daily returns, daily high-low range and daily realized volatility to develop a forecasting model based on their conditional dynamics. As all are non-negative series, we develop a multiplicative error model that is consistent and asymptotically normal under a wide range of specifications for the error density function. The estimation results show significant interactions between the indicators. We also show that one-month-ahead forecasts match well (both in and out of sample) the market-based volatility measure provided by an average of implied volatilities of index options as measured by VIX.