Research on covariance, correlation, missing data, and the models used to allocate and measure portfolio risk.
Curated by Signal & Evidence. Read our research approach for how we distinguish author findings from house tests.
Higher-order surface dynamics help explain curvature. The improvement is much smaller for the whole smile.
Study: Beyond the Skew-Stickiness Ratio: Transport Geometry of Spot-Driven Variance Surface Dynamics
A reconfiguration index describes a volatility environment. Its premium measure is not a variance-swap payoff.
Study: The Reconfiguration Premium: Co-movement Structure as an Unspanned Dimension of the Variance Risk Premium
Entropy regularization reduces some portfolio churn. The study’s tables show a trade-off, not universal superiority.
Study: An Entropic Factor Model for Robust Portfolio Replication
MINGLE builds a graph from shared exposures. The gains are promising, but crisis protection remains limited.
Study: Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification
A compact neural risk model improves the authors’ backtest. Its drawdowns and universe rules deserve equal billing.
Study: Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage
A neural covariance estimator reports lower risk, but the implementation deserves an equally careful audit.
Study: End-to-End Neural Shrinkage of Indefinite Pairwise Correlation Matrices for Small-Cap-Inclusive Portfolios
