Recent volatility in oil prices has put energy risk back on investors’ dashboards. For those interested in understanding how broad-market ETFs respond to crude oil moves, quantifying this relationship is a natural first step. Here, we walk through a basic beta analysis between several major ETFs and crude oil returns. The aim is not to make forecasts, but to illustrate how simple tools can shed light on market relationships—while keeping in mind their real-world limits.
The beta coefficient in this context answers: “If crude oil moves by 1%, how much do we expect this ETF to move, on average, on the same day?” It’s a simple linear regression of ETF daily returns on crude oil daily returns. Alpha captures average drift not explained by oil, and R² tells us how much of the ETF’s return variation is statistically “explained” by oil.
We use the S&P GSCI Crude Oil Index alongside five widely followed ETFs, focusing on daily returns from the start of this year. This shorter time frame is intentional: it allows us to capture market dynamics around recent oil price shocks, rather than diluting the signal over several years of quieter markets.
The selected ETFs represent major slices of global equity exposure—large-cap US (SPY, QQQ), small-cap US (IWM), developed ex-US (EFA), and emerging markets (EEM). This gives a practical cross-section for illustrating how oil sensitivity can vary by region and market segment.
The charts below show scatter plots and rolling beta estimates for:
- SPY (S&P 500)
- QQQ (Nasdaq 100)
- EFA (Developed ex-US)
- EEM (Emerging Markets)
- IWM (Russell 2000)
SPY (S&P 500)
- Beta ≈ 0.35: A 1% daily move in crude oil is linked to a 0.35% move in SPY, on average.
- R² ≈ 0.18: Oil explains about 18% of SPY’s daily return variation—a moderate link, but most variation is unrelated.
QQQ (Nasdaq 100)
- Beta ≈ 0.38: Slightly more sensitive to oil than SPY, which is somewhat surprising given QQQ’s tech focus. Could reflect short-term macro effects or the sample period.
- R² ≈ 0.16: Still, the majority of QQQ’s return variance comes from other factors.
EFA (Developed ex-US)
- Beta ≈ 0.19: Much lower sensitivity, as expected. Oil is a weak driver for developed non-US markets.
- R² ≈ 0.08: Barely 8% of variance “explained” by oil.
EEM (Emerging Markets)
- Beta ≈ 0.20: Slightly higher than EFA, consistent with the fact that emerging markets often include oil exporters.
- R² ≈ 0.09: Still a weak relationship.
IWM (Russell 2000)
- Beta ≈ 0.33: High sensitivity, close to SPY. Small-caps tend to have more cyclical, industrial exposure.
- R² ≈ 0.17: Oil is a non-trivial—but not dominant—driver.
It’s important not to overstate these results. The betas are positive, but the R² values remind us that oil explains a modest portion of ETF return variation—even during a period of significant oil price moves. The findings are illustrative, not actionable on their own. Sensitivity to crude oil is only one thread in the much larger tapestry of return drivers.
More robust insight would come from:
- Extending the analysis to sector and single-stock level.
- Segmenting by country/region for international ETFs.
- Factoring in macroeconomic controls.
- Testing over different sample periods or market regimes.
This kind of analysis is best seen as a starting point—a practical illustration of what quantitative tools can show, but also their limitations. In real-world portfolios, relationships like these are rarely stable or simple.
It’s tempting to overinterpret simple beta numbers—especially during periods of heightened oil volatility, when every move in energy prices seems to echo across the rest of the market. But the reality, as this analysis illustrates, is always more nuanced. Beta is not a crystal ball. It is one statistical lens among many, capturing just a slice of the risk and opportunity embedded in multi-asset portfolios.
At Kiski, we take these types of analyses well beyond the single ETF. Our systems run regressions like this daily across thousands of securities in multiple universes—U.S. equities, global developed and emerging markets, sectors, factors, and more. For us, tracking beta sensitivities is not a one-off exercise, but a continuous process that feeds into broader risk management and portfolio construction.
The real value emerges when these basic calculations are put into context:
- Looking at beta-adjusted returns and beta-adjusted drawdowns helps investors understand performance net of systematic risk.
- Comparing rolling betas across different timeframes highlights regime changes—when old relationships break down or new ones emerge.
- Analyzing sector and geographic betas within an ETF allows for deeper attribution and more precise risk targeting.
In practice, no single metric tells the full story. But embedding beta sensitivity analysis—alongside other quantitative tools—forms a core part of a robust, prudent approach to risk. For many clients, what matters is not just knowing “what happened yesterday,” but having infrastructure in place to spot changes, adapt exposures, and avoid being blindsided by market surprises.
