We propose a novel method to quantify the volatility clustering behavior observing in the ?nancial time series generally. To create the solid results calculated by the proposed method in terms of the volatility clustering behavior, we used the international market indices of 14 countries. We ?nd that regardless of used data sets, although the degree of volatility clustering of each country is di®erent, all data exhibits the volatility clustering properties, whereas those which eliminate the volatility clustering e®ect by the GARCH model reduce volatility clustering signi?cantly. To test the usefulness of proposed method in this paper, we generates the arti?cial time series by the GARCH(1,1) model with the coe±cients of original time series estimated by the GARCH(1,1) model. We also ?nd that the degree of volatility clustering of arti?cial data is very similar to those of the original time series. That is, we assert that this method can estimate the volatility clustering behavior in the ?nancial markets.