This article describes volatility evolution of a stochastic process. Evolution of volatility is affected by 'North-East Volatility Wind Effect' described in previous papers. 'North-East Volatility Wind Effect' is describing behavior of stock market indices. An exposure of this allows to predict stock indices volatility bursts. Analysis of 'North-East Volatility Wind Effect' is based on signal decomposition by using the wavelet filtering. Wavelet filtering is applied by using Direct and Inverse CWT for each scaling parameter [2, 3]. Thus for each scaling parameter the signal component (which is part of the original signal) is obtained. 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. In current paper ’North-East Volatility Wind Effect' is formalized in a form of stochastic process, which volatility is evolving in time. Volatility is decomposed in parts or layers. Complicated volatility evolution is happening in each volatility layer. Volatility layers have complicated interrelationships and volatility is transmitted from layer to layer as it is happening in stock markets. The main object to be described in paper is wave nature of volatility.