ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
Patton and Johanna F
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Diebold-Mariano test converges to non-Gaussian stable limits under infinite-variance loss differentials, causing severe size distortions, with sub-sampling proposed as valid inference independent of tail index.
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ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall
ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.