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Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting — arXiv:2611.07287

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.07287
License (as archived): CC0-1.0
Archived: 2026-11-06T14:51:42.455Z
SHA-256 of the archived text: 6af737840f857d8035312ffeeccdad244758a375e29147acb94d8eda30dba4e2
Stable URL of this page: https://metraipolis.com/documento/dc_3cefd1d42001

How to cite

Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting — arXiv:2611.07287 — arXiv (en), https://arxiv.org/abs/2611.07287 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3cefd1d42001 (Metraipolis archive dc_3cefd1d42001, sha256 6af737840f857d8035312ffeeccdad244758a375e29147acb94d8eda30dba4e2)

BibTeX

@misc{dc3cefd1d42001,
  title = {Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting — arXiv:2611.07287},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.07287},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3cefd1d42001; sha256 6af737840f857d8035312ffeeccdad244758a375e29147acb94d8eda30dba4e2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.07287. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

High-frequency volatility forecasting typically separates measurement from prediction: intraday observations are first compressed into a daily realized measure using a fixed aggregation rule, and the resulting statistic is then passed to a forecasting model. This conventional pipeline therefore treats measure construction as forecast-agnostic preprocessing. We ask whether the aggregation stage itself can instead be learned from the downstream forecasting objective while preserving the statistical structure of established realized measures. We propose Neural Realized Measures (NRM), a primitive-preserving, forecast-supervised aggregation framework that retains the original measure-specific intraday contributions but replaces uniform aggregation with positive, mean-normalized context-dependent weights. Uniform weights exactly recover the corresponding raw realized measure (RRM), so the classical construction is nested as a special case. Because NRM remains an explicit weighted sum of established primitive contributions, it also retains direct bar-level attribution. Across SF, HAR, and MIDAS forecasting specifications, NRM is evaluated against corresponding RRM specifications under controlled comparisons. In Heston simulations, NRM reduces RMSE for both contemporaneous and next-day latent integrated variance across all evaluated primitive--regime combinations. On six cryptocurrencies using five-minute data, NRM improves QLIKE in all 54 paired out-of-sample comparisons, with mean gains of 7.23\%, 8.15\%, and 9.22\% under SF, HAR, and MIDAS, respectively. Validation-selected NRM specifications also achieve the lowest mean QLIKE across the six assets among the evaluated approaches. Ablation and SHAP analyses reveal systematic time-of-day and nonlinear local-context effects in the learned allocation.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ML, nuovi lavori del giorno).
Categorie arXiv: Statistical Finance (q-fin.ST) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2611.07287 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:22:27.616Z dalla pagina https://arxiv.org/list/stat.ML/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).