Late last week, Israel launched a preemptive strike on Iran’s nuclear facilities, sending shockwaves through global oil markets. After nearly a week of escalating conflict during the week of June 16, markets remain volatile as investors grapple with uncertainty around the duration of the conflict and the potential scope of U.S. involvement.
Geopolitical instability in the Middle East is often accompanied by heightened volatility in energy markets. In the chart below, we show the at-the-money (ATM) implied volatility of WTI Crude, sourced from the IvyDB US Futures Database. Implied volatility reflects the market’s expectations of future price fluctuations, inferred from options pricing. In essence, higher option premiums signal greater anticipated uncertainty.
Following the Israel–Iran escalation, oil volatility has surged to its highest level since the onset of the Russia–Ukraine war in early 2022. While a spike in volatility naturally indicates increased uncertainty around oil prices, the effects don’t stop there. This volatility transmits into the broader economy by influencing firms’ decisions about inventory and production levels — particularly for industries that are heavily exposed to energy inputs.
This transmission mechanism has broader financial implications. Specifically, oil volatility is associated with a negative risk premium. That means during periods of elevated oil volatility, stocks with greater sensitivity to oil price swings tend to underperform. Investors demand compensation for bearing this risk, and assets that are more exposed to oil volatility shocks are typically penalized by lower returns.
We are interested in identifying stocks with the highest exposure to changes in oil volatility—in other words, which stocks are most at risk if the current conflict escalates. A common way to estimate a stock’s sensitivity to a given factor is through ordinary least squares (OLS) regression, which models the average relationship between returns and explanatory variables.
However, oil price shocks and volatility spikes are tail events, not typical market conditions. Since OLS focuses on minimizing the squared error around the mean, it may fail to capture the effects of these rare but impactful occurrences.
To better understand how stocks behave under extreme conditions, we use a quantile regression. Unlike OLS, quantile regression estimates the relationship between variables at different points in the return distribution, with our focus at the 20th percentile . This allows us to specifically measure which stocks are most exposed in worst-case scenarios, making it a more appropriate tool for assessing tail risk. Our equation is defined below:
Our coefficient of interest is β2 which captures the sensitivity of stock i ’s returns to changes in oil implied volatility. A more negative β2 indicates that the stock tends to perform worse when oil volatility rises—implying higher downside risk in turbulent energy markets.
We sort stocks by their estimated β2values, ranking those with the most negative exposures as the most vulnerable to oil volatility shocks, as can be seen in the following chart.
Many of the stocks showing the most negative betas to oil volatility come from energy, utilities, and industrials. Traditional energy names like Chevron, ConocoPhillips, and Devon may seem like they should benefit from higher oil prices, but spikes in oil volatility often signal geopolitical risk or demand uncertainty, not just supply shocks. That creates a murkier outlook for capital spending. Utilities like NRG and Constellation also appear, as volatility across the energy complex introduces uncertainty into power pricing and input hedges.
Other sectors include cyclicals like Boeing and ON Semiconductor, which are exposed to industrial demand or capex, and consumer names like Best Buy and Norwegian Cruise Line, which suffer when fuel costs rise or consumers pull back. Overall, negative oil vol betas reflect exposure to the uncertainty that volatility creates, not just oil price levels.
The recent surge in oil volatility, driven by geopolitical tensions in the Middle East, underscores the impacts of energy market instability. Stocks with high sensitivity to oil price uncertainty are particularly vulnerable during periods of elevated volatility. By identifying tail risks through oil volatility and employing the quantile regression technique, investors can gain a more precise understanding of extreme market conditions and individual stock risks.