  We study the price dynamics of 65 stocks from the Dow Jones Composite Average
from 1973 until 2014. We show that it is possible to define a Daily Market
Volatility $\sigma(t)$ which is directly observable from data. This quantity is
usually indirectly defined by $r(t)=\sigma(t) \omega(t)$ where the $r(t)$ are
the daily returns of the market index and the $\omega(t)$ are i.i.d. random
variables with vanishing average and unitary variance. The relation
$r(t)=\sigma(t) \omega(t)$ alone is unable to give an operative definition of
the index volatility, which remains unobservable. On the contrary, we show that
using the whole information available in the market, the index volatility can
be operatively defined and detected.
