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D. Zherlitsyn

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Open access Sep 2026

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...

D. Zherlitsyn, M. Kuzheliev, V. Mandra et al. · 0 citations

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