The Volatility Risk Premium: Why Implied Vol Usually Runs Hotter Than Realized
Two different numbers, and a persistent gap
An option's implied volatility (IV) is the market's forward-looking estimate of how much the stock will move, backed out of the option's price. Realized (historical) volatility is what the stock actually did, measured after the fact from its closing prices. Compare the two across a broad set of stocks over long stretches of time and a pattern shows up again and again: implied volatility, on average, sits above the realized volatility that follows it. That persistent gap is the volatility risk premium (VRP) — options tend to be priced as if the future will be a little wilder than it typically turns out to be.
Why the market pays it
The premium isn't a pricing error waiting to be arbitraged away — it's compensation for bearing a specific kind of risk. Buying an option is buying insurance against an uncertain, sometimes severe, future move: a portfolio manager buying puts to hedge a book, a trader buying calls to cap risk on a directional bet. Insurance buyers are structurally willing to overpay a little for protection, the same way homeowners pay premiums that exceed their statistically expected payout. On the other side, sellers of that insurance — market makers and volatility sellers — take on the risk of a large, sudden loss in exchange for a steady stream of smaller premium, and they demand to be paid extra for holding that risk, not just fair value. The imbalance between structural hedge-buying demand and the willingness of capital to sell that insurance is a big part of why the premium exists and persists.
A premium on average, not on every trade
"Implied tends to run above realized" is a statement about averages across many trades and long time periods — it says nothing about any single option or any single stretch of time. Around a binary event like earnings, IV is often priced far above the realized volatility that follows a quiet quarter, but the same setup can just as easily deliver a move violent enough that realized volatility ends up exceeding what was priced in. That's the other side of the vol crush trade — the premium got paid to whoever sold it, but only because the stock happened to cooperate. Market-wide, the VRP also compresses or flips during genuine volatility events: heading into 2008 or March 2020, realized volatility caught up to and then blew past implied for a stretch, which is exactly when option sellers who'd been harvesting the premium for years took the losses the premium was supposed to compensate them for.
How to actually see it on a chart
The premium shows up directly when you plot a stock's implied volatility against its trailing 30, 60, or 90-day historical volatility over time — IV tends to sit above the HV line more often than below it, and the gap widens ahead of known catalysts and narrows in quiet stretches. IV rank and IV percentile are shorthand tools for the same comparison, just measured against a stock's own IV history rather than its realized volatility directly. You can pull both series side by side for any ticker in OptionScope's fair value view, or read the fuller mechanics in the implied vs. historical volatility guide.
What this means for a premium-selling strategy
The existence of a positive average VRP is the structural reason strategies built around selling premium — covered calls, cash-secured puts, credit spreads, iron condors — have a statistical tailwind over long samples: on average, they're collecting slightly more premium than the volatility that eventually shows up would justify. It is not a reason to treat premium-selling as low-risk or the premium as free money. The gap exists precisely because sellers are exposed to the tail events where realized volatility spikes past implied, and a handful of those events can erase a long stretch of collected premium. Sizing positions for the loss scenario, not the average one, is what separates harvesting a real statistical edge from picking up pennies in front of a mechanism that occasionally runs them over.