  We introduce the price probability measure {\eta}(p;t) that defines the mean
price p(1;t), mean square price p(2;t), price volatility {\sigma}p2(t)and all
price n-th statistical moments p(n;t) as ratio of sums of n-th degree values
C(n;t) and volumes U(n;t) of market trades aggregated during certain time
interval {\Delta}. The definition of the mean price p(1;t) coincides with
definition of the volume weighted average price (VWAP) introduced at least 30
years ago. We show that price volatility {\sigma}p2(t) forecasting requires
modeling evolution of the sums of second-degree values C(2;t) and volumes
U(2;t). We call this model as second-order economic theory. We use numerical
continuous risk ratings as ground for risk assessment of economic agents and
distribute agents by risk ratings as coordinates. We introduce continuous
economic media approximation of squares of values and volumes of agents trades
and their flows aggregated during time interval {\Delta}. We take into account
expectations that govern agents trades and introduce aggregated expectations
alike to aggregated trades. We derive equations for continuous economic media
approximation on the second-degree trades. In the linear approximation we
derive mean square price p(2;t) and volatility {\sigma}p2(t) disturbances as
functions of the first and second-degree trades disturbances. Description of
each next n-th price statistical moment p(n;t) with respect to the unit price
measure {\eta}(p;t) depends on sums of n-th degree values C(n;t) and volumes
U(n;t) of market trades and hence requires development of the corresponding
n-th order economic theory.
