发表机构
Miralta Finance Bank S.A.; University of Reading; Albert School（米尔塔金融银行股份公司; 雷丁大学; 阿尔伯特学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究投资组合问题中的因果分离条件，通过该条件推导完整静态投资组合理论，给出投影马科维茨解等，证明相关性质并通过实验验证理论。
AI 中文摘要
我们将投资组合问题的一个结构条件形式化，即因果分离：给定一组声明的驱动因素在投资期限内的实现路径，资产回报相互独立。由此推导出它所诱导的完整静态投资组合理论。分离通过精确的塔式分解强制产生对角加低秩条件协方差……
英文摘要
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.
Comments28 Pages, 6 Figures, 3 Tables. Under peer-revision in a quantitative finance journal
