Screening-Off Information and Conditional Risk in Portfolio Choice

Conditional portfolio models estimate risk relative to a chosen information set, yet rarely test whether that information removes common cross-asset dependence. When it does not, systematic risk may be treated as idiosyncratic, distorting portfolios and attainable efficient frontiers. We formulate this prior problem as screening-off for portfolio choice. A hierarchy separates causal, distributional and second-moment requirements, while an exact covariance decomposition distinguishes represented systematic response, omitted response risk and residual cross-asset dependence. The two representation errors have different financial implications: omitted response necessarily overstates attainable mean--variance opportunities, whereas incomplete screening can distort them in either direction. Exact finite perturbation identities and non-asymptotic bounds map both errors into changes in constrained portfolio weights and frontier potential. Controlled experiments verify the mechanisms under static, heavy-tailed, dynamic and nonlinear designs. Frozen out-of-sample market tests combine a broad universe of observable economic and financial drivers with 150 U.S. equities and 17 hedge-fund strategy indices. Compact selected representations materially reduce residual dependence in both panels; in equities, residual-aware covariance estimation improves materially on the uncorrected diagonal-residual restriction while remaining competitive with established covariance regularizers. The framework provides a falsifiable information criterion for conditional risk and a direct map from representation failure to portfolio instability.