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Nvidia vs Google (NVDA vs GOOG): Returns, Risk & Volatility (2025)

Published:

Last updated:

Analysis period: 2025-01-01 to 2025-12-31

Gale Finance Team
Written by Gale Finance Team
Sid Kalla
Reviewed by Sid Kalla CFA Charterholder
NVDA Total Return
↑ +34.9%
GOOG Total Return
↑ +65.3%

Relative Performance of NVDA vs GOOG (Normalized to 100)

NVDA GOOG

Normalized to 100 at start date for comparison

4 events in this period
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Key Takeaways

  • Total Return: NVDA delivered a +34.9% total return, while GOOG returned +65.3% over the same period. GOOG outperformed on total returns.
  • Risk-Adjusted Return (Sharpe Ratio): GOOG had a higher Sharpe (1.62 vs 0.77), indicating better risk-adjusted performance.
  • Volatility (Annualized): NVDA was more volatile, with 49.6% annualized volatility, versus 32.0% for GOOG.
  • Maximum Drawdown: GOOG's maximum drawdown was -29.3%, while NVDA experienced a deeper drawdown of -36.9%.
  • Tail Risk (VaR & Expected Shortfall): At the 5% level (daily log returns), NVDA's VaR was -4.62% and its Expected Shortfall (CVaR) was -7.58%; GOOG's were -2.93% and -4.29%. VaR is the cutoff; Expected Shortfall is the average move on the worst days.
  • Skew & Kurtosis: Skew: NVDA -0.53 vs GOOG 0.16. Excess kurtosis: NVDA 7.83 vs GOOG 3.62. Negative skew leans downside; higher excess kurtosis means fatter tails.
  • Tail Days & Extremes: 2σ tail days (down/up): NVDA 7/2, GOOG 6/4. Worst day: NVDA -16.97% (2025-01-27) vs GOOG -7.51% (2025-05-07). Best day: NVDA +18.72% (2025-04-09) vs GOOG +9.88% (2025-04-09).
  • Risk ratios: Sortino - NVDA: 1.10 vs. GOOG: 2.54 , Calmar - NVDA: 0.95 vs. GOOG: 2.24 , Sterling - NVDA: 1.14 vs. GOOG: 2.10 , Treynor - NVDA: 0.20 vs. GOOG: 0.50 , Ulcer Index - NVDA: 13.79% vs. GOOG: 12.74%
Assets:Nvidia (NVDA)·Google (GOOG)
Related:Arista Networks vs Nvidia·Arm Holdings vs Nvidia·Broadcom vs Nvidia
Live:NVDA vs GOOG (rolling)

Investment Comparison

If you invested $10,000 in each asset on January 1, 2025:

NVDA $13,487.808 +34.9%
GOOG $16,526.138 +65.3%

Difference: $3,038.33 (GOOG ahead)

Nvidia vs Google Correlation

Average Correlation
moderately correlated
0.39
Current (30-day) 0.36
30-day rolling range -0.09 to +0.91

Nvidia and Google were moderately correlated in 2025. With a correlation of 0.39, these assets showed moderate co-movement, offering some diversification when held together.

For portfolio construction, this moderate correlation offers some diversification benefit, though the assets still tend to move together during major market moves.

Metric Value
Current (30-day) 0.36
Average (full period) 0.39
Minimum (30-day rolling) -0.09
Maximum (30-day rolling) 0.91

Correlation measures how closely two assets move together. Values near +1 indicate strong co-movement, near 0 indicates independence, and negative values indicate inverse movement. Current, minimum, and maximum figures are 30-day rolling correlations on shared daily returns.

Drawdown

Maximum Drawdown
NVDA
-36.9%
GOOG
-29.3%

Nvidia experienced its maximum drawdown of -36.9% from 2025-01-06 to 2025-04-04. It has not yet recovered to its previous peak.

Google experienced its maximum drawdown of -29.3% from 2025-02-04 to 2025-04-08. It has not yet recovered to its previous peak.

Smaller drawdowns and faster recoveries indicate lower downside risk and greater resilience during market stress.

Risk-adjusted ratios

Sharpe Ratio of NVDA and GOOG

Sharpe Ratio: NVDA vs. GOOG

Return per total volatility

Sharpe gives us excess return per unit of risk. Upside and downside volatility both count as risk.

Higher is better
Excess return Annualized volatility 0 75% vol 49.6% · excess +38.4% vol 32.0% · excess +51.7%
excess return / total volatility
Formula Sharpe=E[R]−RfσR\displaystyle \mathrm{Sharpe} = \frac{\mathbb{E}[R] - R_f}{\sigma_R}

Sharpe ratio measures return per unit of risk (volatility). A higher Sharpe indicates better risk-adjusted performance. GOOG had a higher Sharpe (1.62 vs 0.77), indicating better risk-adjusted performance.

A Sharpe above 1.0 is generally considered good, above 2.0 is excellent. Negative Sharpe means the asset underperformed the risk-free rate. Calculated on each asset's full 365-day lookback of available prices and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Sortino Ratio of NVDA and GOOG

Sortino Ratio: NVDA vs. GOOG

Return per downside volatility

Sortino keeps the return-over-risk idea, but only returns below the target rate count as volatility.

Higher is better
Frequency (days) Daily return (%) target -18.4% +20.2% 60 0
excess return / downside volatility
Formula Sortino=E[R]−Rfσdown\displaystyle \mathrm{Sortino} = \frac{\mathbb{E}[R] - R_f}{\sigma_{\mathrm{down}}}

Sortino ratio measures return per unit of downside risk. Unlike Sharpe, it only counts downside deviation (returns below the target return). GOOG had better downside-adjusted returns.

A higher Sortino is better. It's useful when upside volatility is common (crypto is the obvious example). Downside deviation: NVDA 34.9% vs GOOG 20.4%. Calculated on each asset's full 365-day lookback of available prices, using the daily risk-free rate as the target return, and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Calmar Ratio of NVDA and GOOG

Calmar Ratio: NVDA vs. GOOG

CAGR per worst drawdown

Calmar compares CAGR against the single deepest peak-to-trough loss over the period.

Higher is better
0% NVDA +35.1% -36.9% GOOG +65.8% -29.4%
CAGR / max drawdown
Formula Calmar=CAGR∣MaxDD∣\displaystyle \mathrm{Calmar} = \frac{\mathrm{CAGR}}{|\mathrm{MaxDD}|}

Calmar ratio compares CAGR to maximum drawdown. Higher Calmar means more return per unit of worst drawdown. GOOG posted the higher Calmar ratio.

Calmar is computed on each asset's full 365-day lookback and uses the max drawdown over that same window.

Sterling Ratio of NVDA and GOOG

Sterling Ratio: NVDA vs. GOOG

Return per average drawdown

Sterling smooths the drawdown penalty by using average drawdown events instead of only the worst one.

Higher is better
0% -10% -19% -29% -39% 10% drawdown threshold
excess annual return / average deep drawdown
Formula Sterling=CAGR−RfD‾>10%\displaystyle \mathrm{Sterling} = \frac{\mathrm{CAGR} - R_f}{\overline{D}_{>10\%}}

Sterling ratio measures excess return per unit of average drawdown (typically drawdowns worse than 10%). GOOG posted the higher Sterling ratio.

Sterling uses average drawdown events deeper than 10% and subtracts the risk-free rate to report excess return.

Treynor Ratio of NVDA and GOOG

Treynor Ratio: NVDA vs. GOOG

Excess return per market beta

Treynor divides excess annualized return by beta — the sensitivity of the asset to broad-market moves. The slope shown is each asset’s beta vs SPY.

Higher is better
Asset return Market return 0 0 β 1.88 β 1.03
excess return / market beta
Formula Treynor=E[R]−Rfβ\displaystyle \mathrm{Treynor} = \frac{\mathbb{E}[R] - R_f}{\beta}

Treynor ratio measures excess return per unit of market risk (beta) instead of total volatility. GOOG posted the higher Treynor ratio.

Treynor uses beta vs the S&P 500 (SPY) on shared dates and the average 3-month Treasury rate as the risk-free rate.

Ulcer Index of NVDA and GOOG

Ulcer Index: NVDA vs. GOOG

Drawdown pain

Ulcer Index is a risk index, not a return-over-risk ratio. Lower means smaller and shorter drawdowns.

Lower is better
0% -10% -19% -29% -39%
root-mean-square drawdown
Formula UI=E[Dt2]\displaystyle \mathrm{UI} = \sqrt{\mathbb{E}[D_t^2]}

Ulcer Index captures drawdown depth and duration. Lower Ulcer Index means less drawdown pain. GOOG had the lower Ulcer Index (less drawdown pain).

Ulcer Index is computed from each asset's drawdown series over the full lookback window.

Tail Risk & Distribution Shape (2025): Nvidia vs. Google

This section looks at the shape of daily returns, not just the average. Tail stats are computed per asset on its own daily series (crypto includes weekends). We use daily log returns ln⁡(PtPt−1)\ln\left(\frac{P_t}{P_{t-1}}\right) so multi-day moves add cleanly.

Definitions: Value at Risk (VaR), Expected Shortfall, skew, kurtosis, and fat tails.

Tail Risk & Distribution Shape: NVDA vs. GOOG (2025)

Actual daily return tails

The bars are real daily log-return observations from the article window. Darker bars are observations at or beyond each asset’s 5% VaR cutoff.

Observed returns
NVDA VaR 5% ES 5% GOOG VaR 5% ES 5% -21.6% 0% +21.6% Daily log return
VaR marks the 5th percentile loss cutoff; Expected Shortfall averages the observations beyond that cutoff.
Formula VaR5%=Q0.05(rt),ES5%=E[rt∣rt≤VaR5%]\displaystyle \mathrm{VaR}_{5\%}=Q_{0.05}(r_t),\quad \mathrm{ES}_{5\%}=\mathbb{E}[r_t\mid r_t\le \mathrm{VaR}_{5\%}]
Metric (2025) NVDA GOOG
5% VaR (daily log return) -4.62% -2.93%
5% Expected Shortfall (CVaR) -7.58% (worst 13 days) -4.29% (worst 13 days)
Skew -0.53 0.16
Excess kurtosis 7.83 3.62
2σ tail days (down / up) 7 / 2 6 / 4
Worst day -16.97% (2025-01-27) -7.51% (2025-05-07)
Best day +18.72% (2025-04-09) +9.88% (2025-04-09)

Downside co-moves (2σ) — 2025

Computed on shared dates only (n=249). A “2σ downside move” means a shared-close log return more than 2 standard deviations below that asset’s own mean on this shared-date series. Dates below show simple returns (%) for readability.

Downside co-move map: NVDA vs. GOOG (2σ)

Shared-close daily returns

Dots mark actual downside days: asset-colored dots are one-sided downside moves, and red dots are joint downside days. Grey dots add sampled shared-return context when available. The shaded lower-left zone shows where both NVDA and GOOG crossed their own 2σ downside threshold.

2σ
-2σ GOOG -2σ NVDA Joint downside zone -8.9% 0% +8.9% +21.2% 0% -21.2% GOOG daily log return NVDA daily log return
Shared daily return 249
NVDA Downside threshold -6.15%
GOOG Downside threshold -3.80%
Show downside tail dates

Dates below are shared-date observations. The “Date” is the period end (close). Tail thresholds are computed on log returns, but the table shows simple returns (%) for readability. Returns are computed from the previous shared close to this one (for example, Friday → Monday includes weekend moves).

Days when both NVDA and GOOG had a big down day (2σ)

Date (interval) NVDA GOOG
2025-01-24 → 2025-01-27 -16.97% -4.03%
2025-04-03 -7.81% -3.92%

Days when NVDA had a big down day

Date (interval) NVDA GOOG
2025-01-07 -6.22% -0.63%
2025-01-24 → 2025-01-27 -16.97% -4.03%
2025-02-27 -8.48% -2.57%
2025-02-28 → 2025-03-03 -8.69% -2.07%
2025-04-03 -7.81% -3.92%
2025-04-04 -7.36% -3.20%
2025-04-16 -6.87% -2.00%

Days when GOOG had a big down day

Date (interval) NVDA GOOG
2025-01-24 → 2025-01-27 -16.97% -4.03%
2025-02-05 +5.21% -6.94%
2025-03-07 → 2025-03-10 -5.07% -4.40%
2025-03-28 -1.58% -4.89%
2025-04-03 -7.81% -3.92%
2025-05-07 +3.10% -7.51%

Read this as “how ugly the ugly days get”, not as a precise forecast. One-year samples are small, so tail estimates are inherently noisy.

Full Comparison of Nvidia vs. Google (2025)

Metric NVDA GOOG
Total Return +34.9% +65.3%
Annualized Volatility 49.6% 32.0%
Sharpe Ratio 0.77 1.62
Sortino Ratio 1.10 2.54
Calmar Ratio 0.95 2.24
Sterling Ratio 1.14 2.10
Treynor Ratio 0.20 0.50
Ulcer Index 13.79% 12.74%
Max Drawdown -36.9% -29.3%
Avg Correlation to S&P 500 N/A N/A
5% VaR (daily log return) -4.62% -2.93%
5% Expected Shortfall (CVaR) -7.58% -4.29%
Skew -0.53 0.16
Excess kurtosis 7.83 3.62
2σ tail days (down / up) 7 / 2 6 / 4
Audit this calculation

Formulas, inputs, and conventions used to compute the metrics on this page.

Inputs & conventions

Shared window for pair metrics
2025-01-02 → 2025-12-31 (last shared close).
Rolling correlation sample (shared closes)
220 rolling 30-day values (from 249 shared daily returns).
Annualization (days/year)
NVDA: 252 days/year; GOOG: 252 days/year.
Risk-free rate
Uses the 3-month U.S. Treasury yield (FRED: DGS3MO), averaged over each asset’s window:
  • NVDA: 4.22% over 2025-01-02 → 2025-12-31.
  • GOOG: 4.22% over 2025-01-02 → 2025-12-31.
Volatility drag (rule of thumb)
Estimated from annualized volatility (simple returns). For the log-return framing, see Log returns.
  • NVDA: ≈ -12.3%/yr
  • GOOG: ≈ -5.1%/yr
Data alignment
No forward fill. Correlation and tail co-moves are computed on shared closes only.
For cross-calendar pairs (e.g., crypto vs stocks), weekend/holiday moves roll into the next shared close.
Return conventions
Volatility/Sharpe/Sortino use simple daily returns. Tail-risk uses daily log returns for distribution stats (but tables show simple returns). Log returns.

Formulas

Daily simple return
rt=PtPt−1−1r_t = \frac{P_t}{P_{t-1}} - 1
Volatility
σann=σ(rt)A\sigma_{ann} = \sigma(r_t)\sqrt{A}
Volatility drag (approx.)
drag≈12σann2\text{drag} \approx \tfrac{1}{2}\sigma_{ann}^2
Sharpe Ratio
S=A rˉ−rfσ(rt)AS = \frac{A\,\bar{r} - r_f}{\sigma(r_t)\sqrt{A}}
Sortino Ratio
So=A rˉ−rfE[min⁡(0, rt−rf/A)2] ASo = \frac{A\,\bar{r} - r_f}{\sqrt{\mathbb{E}[\min(0,\,r_t - r_f/A)^2]}\,\sqrt{A}}
Maximum Drawdown
MDD=min⁡t(Ptmax⁡s≤tPs−1)MDD = \min_t\left(\frac{P_t}{\max_{s \le t} P_s} - 1\right)
Average Correlation
ρ=cov⁡(rA, rB)σA σB\rho = \frac{\operatorname{cov}(r^A,\,r^B)}{\sigma_A\,\sigma_B}
Log returns
ℓt=ln⁡(PtPt−1)\ell_t = \ln\left(\frac{P_t}{P_{t-1}}\right)
Notation
PtP_t
Price on day t.
rtr_t
Simple daily return.
ℓt\ell_t
Log daily return.
rˉ\bar{r}
Average daily return.
σ(rt)\sigma(r_t)
Standard deviation of daily returns.
AA
Annualization factor (days/year).
rfr_f
Annual risk-free rate.

Nvidia vs Google: Frequently Asked Questions

Which had higher volatility: NVDA or GOOG?

NVDA showed higher volatility at 49.6% annualized, compared to 32.0% for GOOG During 2025. Higher volatility meant larger price swings in both directions.

Did NVDA provide diversification when held with GOOG?

NVDA and GOOG were moderately correlated in 2025, with an average correlation of 0.39. This offered some diversification benefit, though they still tended to move together during major market moves.

How bad are the worst 5% days for NVDA vs GOOG?

During 2025, NVDA's 5% VaR was -4.62% and its 5% Expected Shortfall was -7.58% (worst 13 days). GOOG's were -2.93% and -4.29% (worst 13 days).

Do NVDA and GOOG crash together on bad days?

On shared dates (n=249), when GOOG has a 2σ down day, NVDA also does 33.3% (2/6 days). In the other direction, when NVDA has one, GOOG also does 28.6% (2/7 days).

Which had better risk-adjusted returns: NVDA or GOOG?

GOOG showed better risk-adjusted performance with a Sharpe ratio of 1.62 versus NVDA's 0.77 During 2025.

Could NVDA and GOOG have been combined in a portfolio?

Yes, though allocation sizing mattered. Their moderate correlation offered some diversification benefits. NVDA's higher volatility (49.6%) meant even small allocations can materially impact overall portfolio risk.

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