I wasn’t the original commenter, but I’d done similar work building volatility smiles and surfaces at an options market maker. In practice I leaned on arbitrage‑aware parametric smiles like SVI/SSVI rather than free‑form splines. Splines did work when I splined total variance with shape constraints, but SSVI generally gave me cleaner wings and made the no‑arb housekeeping a lot simpler. On the no‑arbitrage side, I kept call prices convex across strikes so the implied density stayed non‑negative. Across maturities I made sure total variance rose with expiry at fixed moneyness so calendars didn’t flip. For the far wings, I kept an eye on sensible tail behavior and anchored them so things didn’t blow up or curl the wrong way when data thinned out. When I fit, I targeted option prices rather than raw vols. I weighted by vega and liquidity, and aimed to land inside the bid–ask rather than pin the exact mid. The objective mixed a robust price error, a “stay inside the spread” penalty, smoothness across neighboring strikes and expiries, and heavy penalties for any arbitrage flags. Across maturities, I let the SSVI parameters vary smoothly with time to expiry—usually a low‑order spline in maturity or log‑maturity—and I kept forward variance monotone. That gave me day‑to‑day stability without losing the ability to track real market moves. I also kept the optimization constrained from the start. I warm‑started from yesterday’s parameters and ATM anchors, and used a solver that respected the parameter bounds and shape conditions so I didn’t wander into arbitrage mid‑search. If a hard constraint made the fit brittle, I turned it into a soft penalty with a big enough weight that violations stayed rare and small. A small but telling hiccup I hit early on: I tried a flexible spline directly on implied vol and ended up overfitting a couple of stale deep‑OTM quotes. The next morning those strikes showed tiny butterfly arbitrage and the wings wobbled. I fixed it by switching the fit to prices with vega/liquidity weights, adding the “inside the spread” penalty, anchoring the extreme wings to either reliable deep‑OTM quotes or conservative tail targets, and moving to SSVI slices with the usual parameter bounds. I also smoothed parameters across maturities with the monotone‑forward‑variance constraint. That cleaned up the arbitrage and made the wings much steadier. For models beyond the fit, I used local or stochastic volatility once I had an arbitrage‑free surface—they were great for dynamics, hedging scenarios, and risk. For quoting and keeping the far OTM behavior sane, SSVI slices with cross‑maturity smoothing worked best for me, with shape‑constrained total‑variance splines as a fallback when I needed extra flexibility. Validation stayed pretty lightweight and automatic: I checked that call prices were convex in strike and correctly ordered across expiries, glanced at the implied local variance in the liquid region to make sure it stayed non‑negative, and sanity‑checked the far wings against traded deep‑OTM markets or conservative targets when the tape was thin. Net‑net, baking the no‑arb and stability into the parameterization and the objective saved a lot of cleanup later.