  We present the Shortfall Deviation Risk (SDR), a risk measure that represents
the expected loss that occurs with certain probability penalized by the
dispersion of results that are worse than such an expectation. SDR combines
Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce,
contemplating two fundamental pillars of the risk concept, the probability of
adverse events and the variability of an expectation, and considers extreme
results. We demonstrate that SD is a generalized deviation measure, whereas SDR
is a coherent risk measure. We achieve the dual representation of SDR, and we
discuss issues such as its representation by a weighted ES, acceptance sets,
convexity, continuity and the relationship with stochastic dominance.
Illustrations with real and simulated data allow us to conclude that SDR offers
greater protection in risk measurement compared with VaR and ES, especially in
times of significant turbulence in riskier scenarios.
