In the trading universe, where risk and uncertainty permeate every decision, the oil futures market emerges as a challenging playing field. This volatile and unpredictable arena, where global macroeconomic forces, geopolitical tensions, changes in energy policies, and fluctuations in supply and demand intertwine to shape price movements, requires intuition, experience, and advanced analytical tools to make informed trading decisions. The ability to accurately predict future prices, assess risks, and position oneself accordingly can distinguish between a successful investment and a significant financial loss. With this in mind, the current study aims to address this challenge by focusing on Brent Crude Oil, a crucial benchmark in the global energy landscape. Before we dive into this analysis, it is worth noting that we have chosen Brent area oil as the specific subject of our study for a number of both strategic and practical reasons that we will analyze below. This type of crude oil is of primary importance globally, serving as a benchmark for oil prices. Brent is used to determine the price of a large share of the world’s oil production and for futures contracts. As a result, its influence extends far beyond the energy sector, permeating the entire global economy. For all these reasons, we decided to focus our study on Brent Crude Oil to provide a rigorous and in-depth analysis that can be a useful decision-making tool for traders active in the oil futures market. We will then analyze the history of Brent, its relevance within the oil industry, and the specifics of its market. This preliminary examination will allow us to establish a solid starting point for the subsequent quantitative analysis.
The ultimate goal of this study is to develop a model to predict future Brent oil prices. We adopted a rigorous methodological approach based on Monte Carlo simulation to achieve this goal. Because of its use of random sampling to model and analyze complex systems, this technique is particularly well suited for estimating the range of possible outcomes and assessing their associated risks. The analytical path has five distinct phases: data exploration, data preprocessing, data analysis, simulation, and visualization. This methodology can generate various future paths for Brent oil prices, each with a specific probability. This approach provides a much broader and more detailed view than simply forecasting a single price, allowing traders to understand the potential risk better and return on their investments.

Brent: History, Relevance and Market Specificity – A Starting Point for Quantitative Analysis

North Sea Brent Crude, internationally recognized as light and sweet crude, was first recovered in the North Sea in the 1960s. This type of oil is considered sweet because of its low sulfur content (less than 0.42 per cent), an element that could reduce the yield of high-value refined products such as gasoline and plastics. In addition, Brent is classified as light because of its low density, making its processing into gasoline and related products relatively easy. This combination of properties helps keep its price high in commodity markets. 

The Brent blend comes from vast deposits in the North Sea bounded by several European countries, including the United Kingdom, Norway, the Netherlands, Germany, France, Denmark, and Belgium. Active oil fields include the Brent, Forties, Oseberg, Ekofisk and Ninian systems. Explorers discovered this oil in the area in 1859, but commercial exploration did not begin until 1966, intensifying in the 1970s, just before the Organization of Petroleum Exporting Countries (OPEC) oil crisis. The high oil quality, North Sea regional stability, and OPEC oil embargo concerns made the cost of producing North Sea Brent crude profitable. 

Brent trading occurs mainly in the futures market, thanks to the accessibility of trading platforms such as the Intercontinental Exchange in Europe and the New York Mercantile Exchange (NYMEX). Investors commonly trade Brent-linked commodity contracts for both hedging and speculative purposes. Entities often take hedging positions, including companies that produce and trade oil, crude oil refineries, and other entities that work with oil.

Airlines and other companies in fuel-dependent sectors can benefit from hedging strategies that use Brent-linked contracts.
Brent oil, which accounts for more than half of globally traded crude oil, is the most commonly used benchmark for setting prices in various oil markets. This benchmark crude oil is instrumental for the industry in establishing a point of comparison for evaluating different varieties of crude oil. In the overview of other important benchmark crudes, we mention West Texas Intermediate (WTI), which is lighter and sweeter than Brent.

Influence of the Geopolitical and Market Context on Brent Prices

The oil market is currently subject to several complex and interconnected factors contributing to a climate of high uncertainty. Traders must closely monitor these developments and adapt to navigate this changing environment. In summary, geopolitical instability, economic uncertainty, and ongoing monetary policies introduce high uncertainty into the future outlook for Brent oil prices. With rate hikes by major central banks, falling demand in China, increased supply from embargoed countries, and Goldman Sachs’ downward forecast, we may see a continuation of the downward trend in oil prices. In practical terms, consumers could benefit from lower energy prices in the short term. However, oil producers and exporting countries could face serious economic difficulties if the price decline persists for a prolonged period. Countries like Russia, Iran and Venezuela, which rely heavily on oil exports to finance their economies, could suffer the most severe consequences. 

We will now deepen our analysis by looking specifically at the geopolitical and market dynamics shaping the current climate of uncertainty in the oil market. Specifically, we will seek to understand how each element, from political conflicts and economic tensions to current monetary policies, is affecting Brent prices. We will explore how each event impacts global markets, supply and demand forecasts, and, ultimately, Brent crude oil prices. We aim to provide a detailed and clear picture of the forces driving the oil market so that traders and investors can adapt and effectively navigate this ever-changing environment.

Political Instability in Russia

First, the geopolitical situation in Russia is, without a doubt, a key determinant that can significantly influence Brent oil prices. Russian mercenaries’ recent failed coup attempt has raised doubts about President Putin’s power’s stability and highlighted deep internal political rifts, which have created a climate of global geopolitical uncertainty. This instability has inevitably impacted global markets, leading to a decline in the MSCI All-World Index by 0.1 per cent and the European STOXX 600 Index by 0.3 per cent. In an environment of geopolitical turmoil, investors tend to take refuge in assets considered safe, such as gold and U.S. Treasury bonds. In this case, a flight to Brent Crude Oil rose 0.5 per cent, bringing the oil price to $74.26 per barrel. This movement is significant, as it suggests that the market views oil as a haven resource in times of geopolitical uncertainty. The reasons behind this dynamic may be varied. First, Russia is one of the world’s leading oil producers, and any disruptions to its oil production or export could significantly impact global supply, causing prices to rise. Second, geopolitical uncertainty may lead to increased speculation in the oil market, with investors seeking to capitalize on instability by predicting higher prices. However, it is important to note that geopolitical uncertainty can affect oil prices in both directions. If political tension is resolved without affecting oil production or exports, investors may decide to sell, leading to lower prices. In addition, if geopolitical conflicts lead to a global economic slowdown, oil demand could fall, contributing to lower prices. 

Ruble and Global Currency Changes

The devaluation of the Russian ruble represents another element of instability in the geopolitical landscape that can significantly affect Brent oil prices. In this case, the Russian currency suffered a 3 per cent devaluation, reaching its lowest in 15 months. As previously highlighted, this currency weakness could have considerable implications for the oil market, especially considering Russia is one of the world’s largest oil exporters. When a currency depreciates, exported products become cheaper for international buyers. Thus, theoretically, a devaluation of the ruble could make Russian oil cheaper for foreign buyers, potentially increasing demand for Russian oil. If demand exceeds supply, this could lead to higher Brent oil prices. However, the reverse effect could occur if ruble weakness leads to domestic economic problems in Russia. If, for example, ruble devaluation makes it more expensive for Russian oil companies to purchase foreign equipment and services needed for oil extraction and production, they could reduce their output. A decrease in oil supply, in the presence of steady or increasing demand, could cause Brent oil prices to rise. On the other hand, if ruble weakness causes Russia to increase its oil production to generate more foreign exchange revenue, this could lead to oversupply in the market, causing prices to fall.

Trends in the Chinese Economy

As the world’s second-largest economy and one of the largest global oil consumers, China significantly influences Brent oil prices. The Chinese economy has recently shown signs of slowing, with economic indicators falling short of market expectations. This slowdown trend was further confirmed by a 16 per cent reduction in oil imports in April compared to March. The slowdown in China’s economy and the resulting decrease in oil demand may have a deflationary effect on Brent oil prices. A drop in demand, with the same supply, can cause prices to fall. This directly affects market laws, according to which prices adjust to balance supply and demand.
In addition, given China’s importance as a global player, signs of economic slowdown can significantly impact investor confidence and market sentiment. This may lead to increased risk aversion, with investors taking refuge in assets considered safer and less exposed to global economic fluctuations. This movement of investors could further put downward pressure on Brent oil prices. At the same time, China’s economic and energy policies may influence oil price movements. For example, if the Chinese government decides to stimulate the economy through fiscal or monetary measures in response to the economic slowdown, this could strengthen oil demand and help support prices.

Central Bank Policies

Central bank decisions, particularly those of the Federal Reserve (Fed), the European Central Bank (ECB), and the Bank of Japan, are another major geopolitical factor influencing Brent oil prices. These institutions are crucial in determining the cost of money globally through managing interest rates, consequently influencing the oil market. Interest rates are a key indicator for investors because they influence the cost of capital and return on investment. A rise in interest rates can strengthen the dollar, as higher yields tend to attract investors to dollar-denominated assets. Since Brent oil is priced in dollars, a stronger dollar makes oil more expensive for buyers using other currencies, which could reduce demand for oil and put downward pressure on prices. The market expects the Fed to keep the interest rate range between 5 per cent and 5.25 per cent. However, should the Fed signal the possibility of further rate hikes, this could push the dollar to strengthen further, potentially affecting Brent oil prices. On the other hand, an interest rate hike may also signal a growing economy and potential inflation, leading to increased demand for oil and higher prices. Similarly, if the ECB or the Bank of Japan were to adopt divergent monetary policies, this could affect the relative value of the dollar and, consequently, oil prices.

Geopolitical Impact on Oil Price Dynamics and the Role of OPEC+

The Organization of the Petroleum Exporting Countries and its allies (OPEC+) are a crucial factor in the geopolitical environment related to Brent oil prices. This group of nations holds enormous influence over the global oil market due to their collective control over a significant portion of the world’s oil reserves. Despite the cartel’s attempts to stabilize or increase oil prices through agreed production cuts, the market has reacted oppositely, with investors betting on further price declines. This disconnect between OPEC+ actions and the market response can be attributed to several complex geopolitical factors. First, economic instability in China, one of the largest global oil consumers, has decreased oil imports. This demand reduction may put downward pressure on oil prices, negating the effects of OPEC+ production cuts. Second, Western sanctions against Russia, a key OPEC+ member, have had a boomerang effect. Kremlin-controlled companies could resume oil exports to pre-conflict levels, thereby increasing supply in the global oil market and potentially putting downward pressure on prices.

Analysis of recent events in the oil market shows that various geopolitical factors, including the economic dynamics of major oil consumers and geopolitical tensions between OPEC+ members and other global powers, can mitigate the influence of OPEC+. To provide an accurate forecast of Brent prices, it is crucial to consider a wide range of such factors in addition to the decisions made by OPEC+. In particular, instability in the Chinese economy, partially attributable to the trade war with the United States, has significantly affected oil demand. Chinese oil imports fell 16 per cent in April compared to March and 1.4 per cent year-on-year. Chinese oil stocks reached their highest levels in two years, reducing the need for purchases. Western sanctions against Russia, at the same time, had a less pronounced impact than expected. Despite an initial drop, Russian companies have restored pre-conflict export levels. Goldman Sachs predicts an increase in supply from Russia and other embargoed countries by 2024, as these countries sell oil below-market prices to increase fiscal revenues. As an example, Russia is offering oil at reduced prices to Indian refineries, which are flooding Europe with their processed products, exploiting the competitive advantage from the policy of buying below cost. Looking ahead, Goldman Sachs has revised its oil price forecast from $95 to $86 per barrel, pointing to a medium-term target of $74 to $75. This revision is in line with data on U.S. inflation, which saw a decline in May to 4 per cent from 4.9 per cent in April. This decline in inflation could signal the beginning of a recessionary phase, which could lead the Federal Reserve to discontinue its interest rate policy.

Brent Crude Oil: Predict Futures Prices Using Monte Carlo Simulation


In the context of this analysis, we employ the Monte Carlo simulation methodology to predict future Brent crude oil prices. Monte Carlo simulation is a probabilistic technique that uses random sampling to model and examine highly complex systems. By simulating numerous hypothetical scenarios based on historical data, we can estimate the range of possible outcomes and assess the associated risks. 

The steps of the analysis are as follows:

1. Data exploration: This initial stage consists of loading the historical Brent crude oil price dataset and performing preliminary data exploration tasks such as checking descriptive statistics and identifying missing data.

2. Data preprocessing: In the preprocessing stage, we convert the date column to a universally recognized format and ensure that the dataset is structured appropriately for analysis.

3. Data analysis: In this step, we calculate logarithmic returns from the historical data. Logarithmic returns are an effective way to measure relative oil price changes over time. Next, we calculate the mean and standard deviation of the logarithmic returns. These values will be used to estimate the trend (drift) and volatility of oil prices.

4. Simulation: With the previously estimated trend and volatility, we simulate future Brent crude oil prices for a given number of trading days. We define the most recent oil price as the initial price and generate random samples from a normal distribution to model daily price changes. We obtain a set of simulated price paths by performing this process iteratively.

5. Visualization: Finally, we produce a graphical representation of the simulated price paths to understand the potential range of future oil prices. The simulated prices are plotted as a function of the number of trading days, with the most recent price used as a reference. 

In conclusion, using a Monte Carlo simulation approach can provide valuable information on the future behaviour of Brent crude oil prices, offering a robust way to assess potential scenarios and related risks.

Data Analysis

The historical dataset consists of daily Brent crude oil prices. This dataset, which is critical for analysis, contains many key pieces of information representing the dynamics of oil prices over time. The key statistics for the dataset are as follows:

1. Number of Observations (Count): 9,011 – This indicates the daily Brent crude oil price records available for analysis. Each observation is a unique data sample representing the oil price on a particular day.

2. Mean (Mean): 48.42 – The arithmetic mean of Brent crude oil prices in the dataset, calculated by summing all prices and dividing by the number of observations. This provides a central measure of the distribution of Brent crude oil prices.

3. Standard Deviation: 32.86 – The standard deviation measures the dispersion of Brent crude oil prices around the mean. A high standard deviation value indicates greater price variance.

4. Minimum Price (Minimum Price): 9.10 – The lowest Brent crude oil price recorded in the dataset. 

5. Maximum Price: 143.95 – The highest price of Brent crude oil recorded in the dataset. 

Next, we calculate logarithmic returns to analyze the relative changes in oil prices over time. Logarithmic returns are a particularly effective way to quantify percentage price changes because they maintain a proportional measure and allow for easy aggregation over time. The average logarithmic returns is about 0.018%, representing the average daily trend in oil prices over the period examined. It is also called the “drift” in the context of Monte Carlo simulations and indicates the overall price trend over time. The standard deviation of the logarithmic returns is 2.55 per cent, which measures the average daily volatility of oil prices. This volatility represents the degree to which oil prices vary daily and is a key indicator of the uncertainty or risk associated with oil prices. These values, or the mean and standard deviation of logarithmic returns, are then used to estimate drift and volatility for Monte Carlo simulation. These are essential parameters in the geometric Brownian motion model, one of the most common models used in Monte Carlo simulations to represent the evolution of financial asset prices.

Simulation Results

We conducted a Monte Carlo simulation to forecast future Brent crude oil prices for 252 trading days, corresponding to one year of trading in the stock market. This choice of time is intended to provide a long-term view of price trends. The simulation was iterated 1,000 times to generate various possible outcomes. This repetitive process, characteristic of Monte Carlo methodology, allows for modelling the inherent uncertainty and obtaining a probabilistic outcomes analysis. Therefore, each simulation iteration can be considered a potential “scenario” for future oil price trends. Over the 1,000 iterations of the simulation, the minimum simulated price we obtained is 90.21, while the maximum price is 93.76. These prices constitute the limits within which the simulated prices fluctuate. They reflect the uncertainty and variability in future oil prices, taking into account historical price fluctuations and estimated volatility. The simulated price paths were then displayed in a graph to show the potential movement of oil prices over 252 trading days. Each line in this graph represents a possible simulated price path, showing how the price could change daily based on randomly generated values according to the probabilistic distribution inferred from historical data.

The graph has marked the most recent oil price as a reference point. This reference point provides a visual anchor that allows us to compare the simulated price trend with the most recent known oil price value. It is important to note that simulated prices are based on historical data and random sampling and, therefore, should be interpreted as potential scenarios rather than accurate predictions. Each simulated price path is not a definitive forecast but a representation of a possible future trend based on historical price dynamics and volatility. In conclusion, the Monte Carlo simulation results provide a valuable picture of the possible future evolution of Brent crude oil prices, but they should be used in conjunction with other information and analysis tools to make informed decisions.

Sensitivity and Risk Analysis

Monte Carlo simulation allows us to assess the sensitivity and risks associated with oil price forecasts. We can observe the wide range of possible outcomes by generating multiple simulated price paths and identifying potential risks. Several factors can influence outcomes and introduce uncertainty, including changes in global oil supply and demand, geopolitical events, economic indicators, and government policies. The simulation provides a framework for incorporating these factors and analyzing their impact on future oil prices. Beyond that, it is critical to consider the limitations of the Monte Carlo simulation approach. The simulation assumes that the future behaviour of oil prices can be modelled using historical data, which may not fully capture all the complex dynamics and unforeseen events that may affect the market.

In terms of modelling, the Monte Carlo simulation is based on a set of probability distributions for the input variables derived from the historical data. These distributions represent the uncertainty associated with each variable. However, the model may not fully capture the actual distributions’ heavy tail or skewness (skewness). In addition, the assumption of independence among the input variables may not always be valid, which may affect the accuracy of the predictions. Risk management techniques, such as portfolio diversification and hedging strategies, can be employed to mitigate potential risks associated with oil price fluctuations. These techniques provide mechanisms to protect against changes in oil prices, allowing investors to limit potential losses. For example, portfolio diversification can be implemented by investing in a variety of assets that are not closely correlated, thereby reducing exposure to a single sector or market. Hedging strategies, on the other hand, may include using futures contracts, options, swaps and other derivatives to offset risks associated with price fluctuations. 

In conclusion, Monte Carlo simulation is a valuable tool for sensitivity and risk analysis in oil price forecasting, but it is important to understand its limitations and underlying assumptions. Its effectiveness is maximized with other risk management techniques and forecasting tools.


Federico Turchi – Dual Degree MS in Finance, Fordham University | MSc in Economics of Markets and Financial Intermediaries, UCSC

+39 3400825728

Pietro Cortinovis – MSc in Finance at Fordham Gabelli School of Business – New York City | Università Cattolica del Sacro Cuore – Milan

+39 3491587498

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