This article describes results of stock index analysis by using wavelet filtering. Wavelet filtering is applied by using Direct and Inverse CWT for each scaling parameter. Thus for each scaling parameter the signal component (which is part of the original signal) is calculated. For subsequent research volatility indicator is analyzed by using 20-days time window, which is shifted on the time axis. Volatility analysis is done for each signal component. As a result volatility evolution in time is obtained for each signal component. According to research, a slight increase in volatility in the low-frequency components of the signal leads to significant disturbances in high-frequency components destine entire signal volatility growth. This article ilustrates North-East Volatility Wind Effect in stock market data, shows its nature and fundamental economical meaning.