{"paper":{"title":"Marking-Aware Sequential VaR Recalibration for Standardized Option Books","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Sequential conformal recalibration corrects the systematic underestimation of downside risk produced by base quantile forecasts for standardized SPX option books.","cross_cats":["q-fin.ST"],"primary_cat":"q-fin.RM","authors_text":"Keyuan Wu, Tenghan Zhong","submitted_at":"2026-04-03T22:45:54Z","abstract_excerpt":"Daily Value-at-Risk (VaR) for option books requires more than an accurate quantile forecast. It first requires a precise definition of the loss target. Before any model is evaluated, the protocol must fix the book construction rule, the marking rule for the next day, the loss scale, and the information set available at forecast time. Common pipelines instead apply VaR methods to underlying returns or preconstructed book loss series, leaving these operational choices outside the statistical target. We propose a marking-aware sequential VaR recalibration framework that targets normalized book-le"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Using SPX option data from 2018 to 2025, we show that the uncalibrated base model systematically underestimates downside risk across multiple standardized books. Sequential recalibration removes much of this shortfall, brings exceedance rates closer to target, and improves rolling-window tail stability.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The base conditional quantile forecast provides a sufficiently stable starting point whose errors can be corrected by sequential conformal recalibration without introducing new biases under changing market conditions and next-day marking approximations.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Sequential conformal recalibration improves exceedance rates and tail stability for VaR in standardized SPX option books compared to uncalibrated quantile forecasts.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Sequential conformal recalibration corrects the systematic underestimation of downside risk produced by base quantile forecasts for standardized SPX option books.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"74284370c1a1909220fd4c6a637fdc09a2ea1f1e02a1be3278b1e533b0e0c640"},"source":{"id":"2604.03499","kind":"arxiv","version":2},"verdict":{"id":"007c94ed-218d-49f7-b0aa-b4669c8f706c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T19:07:48.895449Z","strongest_claim":"Using SPX option data from 2018 to 2025, we show that the uncalibrated base model systematically underestimates downside risk across multiple standardized books. Sequential recalibration removes much of this shortfall, brings exceedance rates closer to target, and improves rolling-window tail stability.","one_line_summary":"Sequential conformal recalibration improves exceedance rates and tail stability for VaR in standardized SPX option books compared to uncalibrated quantile forecasts.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The base conditional quantile forecast provides a sufficiently stable starting point whose errors can be corrected by sequential conformal recalibration without introducing new biases under changing market conditions and next-day marking approximations.","pith_extraction_headline":"Sequential conformal recalibration corrects the systematic underestimation of downside risk produced by base quantile forecasts for standardized SPX option books."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.03499/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"51a9dea99905de2e453bf321dad0962d85c209dbc3b9670e31960352fdac5578"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}