基于区间的波动估计器的比较及基于tvp - var的连通性的应用

Yakup Arı
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引用次数: 1

摘要

本文旨在展示基于区间的波动率在连通性分析中的应用。为此,我们比较了基于Yang和Zhang方法的帕金森、Yang-Zhang、Garman-Klass、Rogers-Satchell和改进的Garman-Klass的波动估计。作为一个例子,我们计算了在Borsa Istanbul上市的四家国防工业公司基于区间的股价波动。我们比较了波动性与异方差均方根误差统计的预测性能。我们将表现最好的波动率序列纳入溢出分析。在计算Diebold-Yılmaz连通性指数时,我们采用了基于tpv -VAR的连通性方法,而不是使用Cholesky分解VAR和广义VAR方法。比较结果表明,对于ASELSAN、KATMERLER和PAPIL的Rogers-Satchell估计和对于OTOKAR的Parkinson波动率估计误差最小。TVP-VAR连通性的实证结果表明,网络的平均预测误差方差为34.35%。
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The comparison of range-based volatility estimators and an application of TVP-VARbased connectedness
This paper aims to show the application of range-based volatility in connectedness analysis. For this purpose, we compare the volatility estimators Parkinson, Yang-Zhang, Garman-Klass, Rogers-Satchell, and modified Garman- Klass by Yang and Zhang methods. As an example, we calculated the range-based stock prices’ volatility of four defense industry companies quoted in Borsa Istanbul. We compared the forecast performance of volatility against Heteroskedastic Root Mean Square Error statistics. We include the best performing volatility series in the spillover analysis. Instead of the Cholesky decomposition VAR and generalized VAR approaches used in the calculation of the Diebold-Yılmaz connectedness index, we apply the TVP-VAR-based connectedness approach. The comparison results show that Rogers-Satchell for ASELSAN, KATMERLER, and PAPIL, and Parkinson volatility estimator for OTOKAR have the smallest error, respectively. The empirical findings of TVP-VAR connectedness show that the average forecast error variance of the network is 34.35%.
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