  We present a novel methodology to quantify the "impact" of and "response" to
market shocks. We apply shocks to a group of stocks in a part of the market,
and we quantify the effects in terms of average losses on another part of the
market using a sparse probabilistic elliptical model for the multivariate
return distribution of the whole market. Sparsity is introduced with an
$L_0$-norm regularization, which forces to zero some elements of the inverse
covariance according to a dependency structure inferred from an information
filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes
impact and response to shocks both applied to and received from individual
stocks and group of stocks. We observe that the shock pattern is related to the
structure of the network associated with the sparse structure of the inverse
covariance of stock returns. Central sectors appear more likely to be affected
by shocks, and stocks with a large level of underlying diversification have a
larger impact on the rest of the market when experiencing shocks. By analyzing
the system during times of crisis and comparative market calmness, we observe
changes in the shock patterns with a convergent behavior in times of crisis.
