Robust Score and Portmanteau Tests of Volatility Spillover
Robust Score and Portmanteau Tests of Volatility Spillover
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波动性溢出的稳健评分和组合检验
DOI：
10.2139/ssrn.2003387
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Jonathan B. Hill
中科院分区：
文献类型：
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作者：
Mike Aguilar;Jonathan B. Hill
本文介绍了各种波动溢出检验，这些检验对由大误差或GARCH型反馈产生的重尾具有稳健性。这些检验是在一个具有特殊冲击的一般条件异方差框架下进行的，如果它们是独立的，则只需要有一个有限的方差。我们忽略了检验方程或方程的组成部分，并构造了重尾稳健得分和混合统计量。修剪是基于指示器功能的简单修剪，或者是平滑修剪。特别地，我们提出了用于稳健推断的去尾样本相关系数，并证明了其在零溢出假设下的高斯极限在不考虑尾部厚度的情况下具有相同的标准化。此外，如果溢出发生在指定的水平内，我们的检验统计量将渐近获得1的幂。我们讨论了修剪部分的选择，包括在极端观测窗口上平滑的p值。蒙特卡洛的一项研究表明，我们的测试比现有的基于GARCH的溢出测试提供了显著的改进，我们将这些测试应用于金融回报数据。最后，基于Patton(2011)的思想，我们构造了一个重尾稳健预测改进统计量，这使得我们可以证明我们的溢出检验可以作为改进波动性预测的模型规范预试。
This paper presents a variety of tests of volatility spillover that are robust to heavy tails generated by large errors or GARCH-type feedback. The tests are couched in a general conditional heteroskedasticity framework with idiosyncratic shocks that are only required to have a finite variance if they are independent. We negligibly trim test equations, or components of the equations, and construct heavy tail robust score and portmanteau statistics. Trimming is either simple based on an indicator function, or smoothed. In particular, we develop the tail-trimmed sample correlation coefficient for robust inference, and prove that its Gaussian limit under the null hypothesis of no spillover has the same standardization irrespective of tail thickness. Further, if spillover occurs within a specified horizon, our test statistics obtain power of one asymptotically. We discuss the choice of trimming portion, including a smoothed p-value over a window of extreme observations. A Monte Carlo study shows our tests provide significant improvements over extant GARCH-based tests of spillover, and we apply the tests to financial returns data. Finally, based on ideas in Patton (2011) we construct a heavy tail robust forecast improvement statistic, which allows us to demonstrate that our spillover test can be used as a model specification pre-test to improve volatility forecasting.