GOOGL volatility clustering over ~3 years: abs-return autocorr vs directional randomness
Volatility in GOOGL does cluster, but only faintly. Over roughly three years we find positive autocorrelation in absolute daily returns — lag‑1 |return| autocorr ≈ 0.0644 — while signed returns show near‑zero (lag‑1 ≈ -0.0442). That pattern means you can weakly predict the size of tomorrow’s move, but not its direction.
We resampled minute bars to daily closes, measured |return| autocorrelations at several lags, and compared next‑day |return| across terciles. High‑tercile next‑day |ret| averages 1.4399% versus 1.4009% for the low tercile; a Welch test gives p ≈ 0.759. The detailed statistics, charts, and robustness checks below show a real but tiny volatility clustering signal—statistically fragile and not useful for directional prediction.
For GOOGL over the past ~3 years, does the size of today's move predict the size of tomorrow's move — real volatility clustering — even though the direction of the next day stays essentially random? Thesis: absolute daily returns show strong positive autocorrelation (big days follow big days, calm follows calm) while signed returns have near-zero autocorrelation, so you can forecast how large tomorrow's move will be but not which way it goes.
How this was measured
Resampled GOOGL minute bars to daily closes over the trailing ~3 years, computed close-to-close returns, and took absolute returns as a volatility proxy. Measured Pearson autocorrelation of |returns| at lags 1/5/10/20, and compared against lag-1 signed-return autocorrelation (directional predictability proxy). As a complementary test, bucketed days by current-day |return| terciles and compared next-day |return| between high and low terciles via a Welch two-sample t-test. Directional randomness was summarized by overall P(next day up) and its conditioning on today's sign.
The key numbers
Reading the numbers
Across 751 trading days, absolute daily returns show a small positive 1-day autocorrelation (~0.064), while signed returns are essentially unpredictable (lag-1 autocorr ≈ -0.044). That says size is weakly persistent but direction is not.
The charts
The bar chart lays out Pearson autocorrelations of |return| at lags 1, 5, 10 and 20 days: 0.0644, 0.0122, 0.0820 and 0.0734 respectively. Notice the small positive bars at lags 1, 10 and 20 (around 0.06–0.08) and the near-zero at lag 5 — volatility clustering is present but modest and not monotonic with lag. Against the headline signed-return autocorr of -0.044, this reinforces that magnitude persistence exists even while direction stays essentially random.
The box plots compare next-day |return| after low, mid and high current-day |return| terciles; the group means are nearly identical: low 0.0140, mid 0.0137, high 0.0144. The full ranges overlap heavily (min 0.0001 up to maxs 0.078–0.126), so there is no clear separation of distributions. A Welch t of 0.307 with p=0.759 confirms the tiny mean differences are not statistically clear — current-day tercile does not produce a reliable jump in next-day |return|.
The scatter of |ret_t| vs |ret_{t+1}| is a dense cloud centered around the means (both ≈0.014, n≈750) with a few extreme points out to 0.126. Look for the slight upward tilt in the cloud, which is the visual counterpart of the lag‑1 autocorr of 0.0644: a detectable but very weak relationship. Practically, big days are a bit more likely to be followed by bigger days, but the signal is tiny compared with the noise in any single observation.
Next-day |return| by current-day vol tercile
| tercile | mean_next_abs_ret | std_next_abs_ret | n |
|---|---|---|---|
| low | 0.014 | 0.0126 | 250 |
| mid | 0.0137 | 0.0135 | 250 |
| high | 0.0144 | 0.0157 | 250 |
The takeaway
Short answer: there is a very small tendency for big (or calm) GOOGL days to be followed by slightly bigger (or smaller) days, but the signal is tiny and statistically unconvincing — you still can't predict direction. The data show |return| autocorrelation around 0.064 at lag-1 (and ~0.073 at lag-20), while signed-return autocorr at lag-1 is about -0.044; mean next-day |ret| is 0.0144 after high-vol days vs 0.0140 after low-vol days. A Welch test comparing high vs low terciles returns p = 0.759 (and the sample is 751 trading days), which in plain terms means there's roughly a 76-in-100 chance the observed difference is just noise. Directional odds also look random: overall P(next day up) ≈ 53.5% with only a 2.18 percentage-point gap between P(up|up)=54.5% and P(up|down)=52.3%. Bottom line: volatility clustering exists in the raw autocorrelations but is extremely small and not robust in cross-group tests — it's a weak lean at best, not a reliable forecast. Practically, expect occasional clustering in move sizes, but don't rely on these numbers for confident next-day sizing or trading decisions.
The fine print
- Window is ~3 years (751 days); longer regimes could change the pattern.
- Tercile comparisons and thresholds were fit in-sample; out-of-sample behavior may differ.
- Autocorr and Welch p-values assume independent errors; serial dependence or regime shifts can bias tests.
- Using close-to-close absolute returns ignores intraday volatility structure that could show stronger clustering.