Volatility
How volatile has this ticker been, how does that volatility behave over time, where is it headed, what could it cost you, and what is the options market charging for future volatility.
Get a cross-checked read on current volatility, placed against the ticker's own history, before deciding whether risk here is calm, typical, or stressed.
See whether volatility clusters into persistent stretches and whether losses move it more than gains — the behavior that makes today's reading useful or not.
Compare several forecasting models for where volatility is headed over the next month, and grade them against what actually happened, not just what fit the past.
Turn a volatility read into a loss number — VaR, Expected Shortfall, and a position size that keeps expected risk near a target level.
How volatile is this ticker right now, by every reasonable measure, checked against its own history. Start here to size up current risk before going further.
How volatility behaves once you look past the current reading: whether calm and turbulent stretches persist, and whether losses move it more than gains do.
Where volatility is likely headed over the next month, and which of several competing models has actually earned the right to be believed.
How much you stand to lose in a bad stretch, and how to size a position so that loss stays inside what you're willing to tolerate.
The implied side. Three cards over one volatility surface: the surface itself, the smile across delta at a chosen expiry, and the term structure across expiry at a chosen delta. Because the two slices come from the same grid, they agree wherever they cross. The top card draws the surface either in three dimensions or flat as a heatmap — delta across, expiry down, implied volatility as colour — on the same numbers and the same colour ramp.
Switch the cone estimator (Yang-Zhang / Close-to-close / Garman-Klass / Parkinson) to change which volatility measure the comparison to history is built from.
Toggle confidence between 95% and 99% in Risk to recompute VaR, Expected Shortfall, and the backtest scorecard at that level.
Pick a conditional-vol Filter (EWMA / GARCH / GJR / EGARCH) in Risk to change the volatility model VaR/ES are built on.
Run the out-of-sample test in Forecast to walk-forward backtest the candidate models against realized volatility (about 20 seconds).
Switch the top IV card between Surface and Heatmap, and pick an expiry on the smile or a delta on the term structure. Reach for the surface to judge the shape and for the heatmap to read a value off a cell — comparing one height against another across a receding axis is what a 3D plot does worst — and the smile and term structure are slices of the same grid, so they agree with the surface and with each other wherever they cross.