Published Papers

We study intraday canonicalization and auditing of implied-volatility (IV) surfaces for S&P 500 index (SPX) options when the available observations are sparse, trade-based open–high–low–close–volume (OHLCV) bars. Such panels exhibit strongly non-uniform cross-sectional support—dense near at-the-money (ATM) and short maturities, and thin in the wings and long tenors—so naive fixed-grid completion can create visually smooth surfaces with unstable calibration and misleading feasibility diagnostics. We propose a reliability-aware canonicalization pipeline that (i) constructs forward and discount proxies from liquid futures strips, (ii) quantifies local information content via an effective sample size (ESS) diagnostic induced by kernel receptive fields, and (iii) calibrates a per-timestamp Surface SVI (SSVI) total-variance surface directly in price space under robust losses. Our main methodological contribution is a reliability-weighted vega-normalized robust objective that downweights weakly supported marks while retaining the interpretability and tractability of price-space calibration. We compare three calibration objectives—robust price residuals, vega-normalized residuals, and reliability-weighted vega residuals—using paired timestamp-level inference with Holm-adjusted randomization tests, and we evaluate economic plausibility with a unified static-arbitrage audit that reports both violation rates and hinge-type severity measures. Empirically, price-residual calibration minimizes price root-mean-squared error (RMSE), whereas reliability-weighted vega residuals yield the most consistent reductions in out-of-sample (OOS) arbitrage severity and in average any-rule violation rates. These results support reliability-weighted robust objectives as default canonicalizers for economically plausible intraday surfaces built from sparse transaction bars and for downstream learning tasks.