Nonparametric Test for Volatility in Clustered Multiple Time Series

TL;DR

This paper introduces a bootstrap-based nonparametric test for volatility in clustered multiple time series, effectively addressing contagion effects and outperforming parametric tests in size and power, especially with nonstationary data.

Contribution

It proposes a novel bootstrap method for testing volatility in multiple time series that accounts for contagion effects and is robust to distributional assumptions.

Findings

Test maintains correct size with near nonstationary series

High power when volatility is concentrated in fewer clusters

Effective in global stock price data analysis

Abstract

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.