  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.
