Graph-Based Uncertainty-Aware Financial Forecasting via Cross-Asset Conformal Prediction
Cross-Asset Graph Conformal Prediction (CA-GCP), a framework that pools volatility-normalized nonconformity scores across the correlation-graph neighborhood of each target asset using a proximity- and recency-weighted quantile, grounded in the theory of weighted conformal prediction, is proposed.