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V-Lab volatility against the valuation: convergence odds, value vs price distributions, and tail risk.
A valuation gives you a destination: what dbot thinks the stock is worth. The Volatility page adds the terrain between here and there. It pulls live volatility analytics for the company from V-Lab, the Volatility Laboratory at NYU Stern, and plays them against the report's own numbers to answer three practical questions: can the price plausibly reach the value, is the valuation call bigger than market noise, and does the upside survive a bad year?
The header shows the company's current annualized volatility from V-Lab's GARCH model, with roughly two years of history as a sparkline. As a rough guide, 20% is a calm large cap, 40% is lively, and 80%+ is a stock in a storm. The strip also notes the date of the report's price anchor: the convergence math below tests the claim the report made at that price.
The fan chart projects where volatility alone can plausibly carry the price over the next 24 months. The inner band covers half of outcomes, the outer band ninety percent, and the dashed horizontal line is dbot's value per share. The fan assumes no drift and lognormal prices: it is pure chance, not a forecast, and that caveat matters when you read it.
The stat tiles translate the picture into odds. The sigma gap is the size of the value-to-price gap measured in units of volatility at that horizon, and the touch odds are the probability that the price touches dbot's value at some point before the horizon. A gap under one sigma is ordinary wandering distance; the market can close it without any news. A gap of two or three sigma means drift alone will not do it, and the thesis quietly depends on a repricing event such as an earnings surprise or a multiple re-rating. Hover along the fan to read the odds at any month.
dbot's Monte Carlo already produced a distribution of what the stock is worth. This chart overlays it (the bars) with where volatility puts the price in one year (the line), on one dollar axis. Three readings follow: the overlap says how much the two views have in common, the fair-band figure is the chance the price lands inside dbot's p25 to p75 value range within a year, and the dominant-uncertainty tile names which is wider. When the value distribution is wider, the uncertainty is fundamental and better research narrows it. When the price range is wider, market noise dominates and patience matters more than precision.
When V-Lab publishes long-run value-at-risk for the company, this panel compares the report's upside cushion against the loss a 1-in-20 bad year can plausibly reach. A cushion that a single tail year erases twice over can still be a good call, but it is not a safe one, and the sizing should reflect that. Not every company has V-Lab VaR coverage, so this panel appears only when the data exists.
Volatility analytics are fetched live from V-Lab (the Volatility Laboratory at NYU Stern Volatility Institute), so they reflect the market as of your visit, while the value and price anchors are the report's own. Every chart carries the source line, and downloads keep it. The convergence math is deliberately simple (driftless, lognormal); treat the odds as orientation, not precision.
To see which assumptions drive the value itself, see Reading the Sensitivity page.