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In this paper, a fuzzy volatility labeling algorithm is offered to detect the periods with abnormal activities on daily share returns. Considering the vagueness in the switches of the time periods, the membership functions of high and normal volatility classes are introduced. In the assignments, both the density structure and membership degree are used. It is believed that this algorithm may be helpful to construct different estimation models for the time periods with normal and abnormal activities. Authors offer algorithm, which can be used as a tool for sustainable risk management.

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CLUSTERING IN KEY G-7 STOCK MARKET INDICES: AN INNOVATIVE APPROACHIntroduction and background. Integration among the major stock exchanges of the financial world has always been attracted attentions due mainly to globalization efforts and innovations in the sector that the financial markets have become more integrated and complicated in today's head to head competitive investment environment. However, so-called integrated and information-rich markets do not continuously follow the same patterns due mainly to the local market conditions where advance quantitative methods through econometric models are required using to forecast the behaviors. Moreover, understanding those models is almost impossible for individuals eager to making better investment decisions [4; 16; 17; 18].Clustering, which was first used as a term by Tyron (1939), is a widely held multivariate analysis which is practiced in a number of fields such as biology, medical sciences computer vision, sociology, urban planning, marketing, and so forth [7; 9; 13; 33; 34; 36;]; for example, Li et al., (2015) distinctly grouped the sports gamblers according to the demographic and behavioral characteristics in China who bought sports lottery tickets more than once in a year by developing a typology based upon the scale of assessing problem gambling [22].The method, which is relatively new and innovative approach in empirical finance, like factor analysis and multi-dimensional scaling, can be used to facilitate the selection process in portfolio management to solve abovementioned problem. In this analysis, objects or variables are separated into a small number of similar categories and relationships between objects and subjects are explored without a dependent variable being identified [5; 30]. In finance, cluster analysis unlike factor analysis is applied seldomly. The finance literature documents a small number of studies applying different clustering methods to given financial classification problems for instance, [1; 3; 6; 14; 26; 28;] and more recently, [8; 15; 19; 25]. Stock exchanges of G-7 countries can be seen as a leading group not only for their market capitalizations within the world's total value of equities but also for the solidity in today's