NVDA Moves a Lot — But How Much, Exactly?
NVIDIA's reputation as a high-beta, high-volatility name is well-earned, but most traders don't actually know the numbers. Over the 2016-2026 period, NVDA's average daily range (high minus low, divided by the prior close) has been roughly 3.1%, with a median closer to 2.6%. That's about twice the SPY's daily range over the same period, and roughly 50% higher than the average S&P 500 component.
Realized 30-day volatility on NVDA has averaged around 42% annualized over the decade, versus roughly 18% for SPY. In calm markets, NVDA drops to the high-20s; during stress periods it spikes into the 70-80 range. The AI-boom rally from late 2022 through 2024 was unusual in that volatility stayed elevated even as prices trended sharply higher — a regime most stocks don't share.
- Average daily range 2016-2026: ~3.1% of prior close
- Median daily range: ~2.6% of prior close
- Average 30-day realized volatility: ~42% annualized
- Highest 30-day realized volatility: ~85% (March 2020)
- Lowest 30-day realized volatility: ~22% (mid-2017)
The Biggest Single-Day Moves
NVDA has printed some truly outsized single-day moves over the last decade. The largest up days include the roughly +24% move the day after the May 2023 earnings report — arguably the most consequential earnings reaction in recent S&P history — along with several +10% to +15% moves tied to other earnings and AI-related catalysts. The largest down days include a roughly -18% move during the late 2018 crypto drawdown and multiple -10% moves during the 2022 inventory correction.
The distribution is notably fat-tailed. On a normal distribution with 42% annualized volatility, you'd expect roughly zero days per year with moves exceeding 8%. In reality, NVDA has averaged about 4-5 such days per year. The tails are fatter, especially on the upside in recent years.
Volatility Clusters — and That Matters for Position Sizing
One of the most robust facts in quantitative finance is that volatility clusters: high-volatility days tend to be followed by more high-volatility days, and vice versa. NVDA follows this pattern clearly. The autocorrelation of daily absolute returns is roughly 0.25 at lag 1 and stays positive out to about lag 10, meaning today's volatility genuinely predicts tomorrow's.
For position sizing, this is important. Sizing NVDA based on its long-run average volatility (42%) will lead you to overweight during calm periods and get blown out during stress periods. A better approach is to scale position size by recent realized volatility — halve your size when 10-day volatility doubles, for example.
Volatility Regimes and Forward Returns
Splitting the data by volatility regime reveals something interesting. When NVDA enters a high-volatility regime (top quartile of 30-day realized vol), the forward 20-day return averages roughly +1.1% with a win rate around 52% — essentially coin-flip. When it enters a low-volatility regime (bottom quartile), the forward 20-day return averages roughly +3.2% with a win rate around 61%.
In other words, calm periods have historically been better setups than volatile ones for holding NVDA. This runs counter to the intuition that 'you need volatility to make money' — at least for long-only positions in this particular stock.
Using the Data
If you're trading NVDA, the practical upshot is simple: know where realized volatility is versus its long-run average, and size accordingly. The Chart Library API exposes realized volatility alongside pattern matches, so you can check both at once:
curl -H "X-API-Key: cl_..." \ "https://chartlibrary.io/api/v1/intelligence?symbol=NVDA&include_volatility=true"
For broader context on how volatility feeds into the embedding space, see our methodology page — the volatility channel is a core part of the fixed-dim vector Chart Library uses for similarity matching.