Benchmarking Machine Learning and Econometric Models for Joint Value-at-Risk and Expected Shortfall in Mixed Equity and Cryptocurrency Portfolios
Cryptocurrency holdings in conventional portfolios challenge the empirical adequacy of standard tail-risk estimators. This study identifies a calibration mechanism that brings feature-based machine learning to supervisory-grade value at risk (VaR) coverage, improves its joint VaR and expected shortfall (ES) record rela...