Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary. The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.
翻译:由于像股票市场指标中那样的多时间序列的集群组合所产生的叠加,可能使波动的性质进一步复杂化,使一个参数测试(依赖零时分布)受到大小和力量问题的影响。我们提议根据多个时间序列的“靴套”方法对波动性进行测试,目的是说明传染效应的可能存在。虽然测试对分布假设来说相当有力,但取决于波动的性质。即使时间序列几乎是非静止的,试验规模也是正确的。当时间序列处于平均不变状态,而且波动性只集中在较少的集群中时,试验也特别强大。我们用全球股票价格数据来说明方法。