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Harvesting the Volatility Risk Premium: A Learning-to-Rank Approach — arXiv:2608.24786

Source

arXiv (en)

Original

https://arxiv.org/abs/2608.24786

License (as archived)

CC0-1.0

Archived

2026-10-18T07:30:50.663Z

SHA-256 of the archived text

6fe9b505a49c44ef79df145e967cb04352af9231f63c3d850faf2e3f0b324996

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https://metraipolis.com/documento/dc_aaa5cc0b6839

How to cite

Harvesting the Volatility Risk Premium: A Learning-to-Rank Approach — arXiv:2608.24786 — arXiv (en), https://arxiv.org/abs/2608.24786 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aaa5cc0b6839 (Metraipolis archive dc_aaa5cc0b6839, sha256 6fe9b505a49c44ef79df145e967cb04352af9231f63c3d850faf2e3f0b324996)

@misc{dcaaa5cc0b6839,
  title = {Harvesting the Volatility Risk Premium: A Learning-to-Rank Approach — arXiv:2608.24786},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.24786},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aaa5cc0b6839; sha256 6fe9b505a49c44ef79df145e967cb04352af9231f63c3d850faf2e3f0b324996},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.24786. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a strict out-of-time integrity check. A LightGBM LambdaRank ranker scores a daily nine-strategy cross-section composed of eight delta-targeted short-put positions and a \textit{SKIP} candidate, trained against a path-aware Sortino-on-bars label computed at one-minute resolution. The framework is evaluated under index-option margin requirements, a tiered fee schedule, and bid-to-mid execution assumptions across a four-window walk-forward over 2021-2024 and a strictly held-out 2025 out-of-time slice. Seven sizing methods produce out-of-time annualized Sharpe ratios between 4.31 and 5.76, with the headline method reaching a Probabilistic Sharpe Ratio of 0.964 and a sample-period maximum drawdown of -2.28%, on a single hold-out year against a walk-forward range of 1.90 to 3.11. Out of time, every method exceeds three passive benchmarks (CBOE PUT, CBOE WPUT, SPX buy-and-hold) by at least 3.84 in Sharpe ratio and five internal selection baselines by at least 3.69. A two-by-two ablation of the confidence gate against the tail-risk features places 5.05 of the 5.59 out-of-time Sharpe gap over the CBOE PUT with the ranker and the selection layer, the two risk controls adding 0.54 between them. On walk-forward, where the gate binds, neither control comes close to the headline alone and their interaction supplies most of the result. A fifteen-group feature ablation shows that removing the multiplicative regime interactions collapses walk-forward statistical confidence.

Tema degli esploratori: economia dei servizi IA (retro-catalogo arXiv: annunci del 2026-08-26 in q-fin.CP).
Categorie arXiv: Computational Finance (q-fin.CP) ; Statistical Finance (q-fin.ST).
Pagina del lavoro: https://arxiv.org/abs/2608.24786 — 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 2026-10-18T07:28:05.430Z dalla pagina https://arxiv.org/catchup/q-fin.CP/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).