  We provide an exhaustive treatment of Linear-Quadratic control problems for a
class of stochastic Volterra equations of convolution type, whose kernels are
Laplace transforms of certain signed matrix measures which are not necessarily
finite. These equations are in general neither Markovian nor semimartingales,
and include the fractional Brownian motion with Hurst index smaller than $1/2$
as a special case. We establish the correspondence of the initial problem with
a possibly infinite dimensional Markovian one in a Banach space, which allows
us to identify the Markovian controlled state variables. Using a refined
martingale verification argument combined with a squares completion technique,
we prove that the value function is of linear quadratic form in these state
variables with a linear optimal feedback control, depending on non-standard
Banach space valued Riccati equations. Furthermore, we show that the value
function of the stochastic Volterra optimization problem can be approximated by
that of conventional finite dimensional Markovian Linear--Quadratic problems,
which is of crucial importance for numerical implementation.
