  We propose a novel method to quantify the clustering behavior in a complex
time series and apply it to a high-frequency data of the financial markets. We
find that regardless of used data sets, all data exhibits the volatility
clustering properties, whereas those which filtered the volatility clustering
effect by using the GARCH model reduce volatility clustering significantly. The
result confirms that our method can measure the volatility clustering effect in
financial market.
