The same goes for dry bulk shipping which is focused on transporting commodities such as iron ore, copper, grains, soy beans, corn, etc.

Energy, industrial metals and grains are all essential to the growth and well-being of the global economy which means that the shipping segments allowing these commodities to move around the world are vital for the stability of both financial and physical markets.

Ships, however, move following precise routes which allow shipping companies to cut costs and deliver the cargo as quickly as possible. The vast majority of these shipping routes go through narrow passages which are squeezed between lands called maritime chokepoints.

These are obligated passages or straits which allow vessels to sail through vast territories without having to circumnavigate lands and sail around them. It goes without saying that these straits are

often targeted by terrorists and independent militias causing geopolitical tensions and severe disruptions to the flow of vessels.

Despite the geopolitical risk, ships have to sail through them because these maritime chokepoints shorten the voyage between loading and discharging ports and avoiding them would inevitably increase costs like fuel, crew, insurance, etc.

The present HyperVolatility research will explore all the most important maritime chokepoints in the world. It will examine each chokepoint from a geographical and then from an energy and commodity perspective.

The present HyperVolatility fundamental research explores all the most important maritime chokepoints which are crucial for commodities like energy and metals. Energy markets would collapse under skyrocketing prices and this is exactly what the world is witnessing in 2026 with the crises in the Strait of Hormuz.

If you want to read the whole research, please click the below reported link to read the HyperVolatility research:

https://www.hypervolatility.com/maritime-chokepoints/

Maritime Chokepoints was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

Analyzing commodity prices require a deep knowledge of how certain financial markets move, their fundamentals and what is happening from a macroeconomic perspective.

All HyperVolatility researches focused on commodities tend to blend an accurate analytics of financial markets with the physical aspect of each commodity. Nevertheless, commodities’ physical markets require not only an understanding of demand/supply dynamics but also an analysis of how those commodities are being transported around the world.

Physical commodities markets completely depend on the shipping industry to ship raw materials and finished products to every corner of the globe. The shipping industry is so essential for commodities markets that without ships, commodities markets would simply not exist.

The shipping market moves continuously, reflecting the demand/supply dynamics of certain commodities but despite prices for ships can fluctuate day-by-day, the most important shipping routes do not change. Shipping companies around the world profit from economies of scale so they need to minimize costs as much as possible and in order to do so they need to send their ships through the shortest routes possible.

Over the last few years, climate change managed to create some new shipping routes that go through an area of the world that had never been available before because of its remoteness: the Arctic region.

Arctic shipping is particularly enticing because it reduces the time spent at sea, in trips between Asia and Europe, by 3–4 days so several companies are interested in knowing if it is actually feasable or not.

Arctic shipping predominantely includes 2 main routes: the Northern Sea Route (NSR) and the North West Passage (NWP).

The economic advantages of saving 3–4 days of sailing are clear: less fuel, lower insurance premiums, quicker delivery, less operational expenses, etc. Nevertheless, the risks that Arctic shipping actually entails are high. In fact, they are so high that they could render sailing through the Arctic Ocean a lot less convenient than one might otherwise think.

The present HyperVolatility fundamental analysis studies the predominant risks involved in shipping through the Arctic region and sub-groups them into 4 categories:

1. Environmental and Biological Risk

2. Geographical Risk

3. Meteorological and Structural Risk

4. Geopolitical Risk

The present HyperVolatility fundamental research examines all the abovementioned risk factors and it attempts to provide an answer to the following question: “Is Arctic shipping really worth the risk?”

Please click the below reported link to read the HyperVolatility research:

https://www.hypervolatility.com/arctic-shipping/

Arctic Shipping - HyperVolatility

Arctic Shipping was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

The covid pandemic had a severe impact on global GDP growth but many economies are now expanding as the numerous supply chain disruptions caused by the pandemic are no longer an issue and raw materials needed for industrial processes can freely circulate on a global scale.

Again, GDP growth is crucial when it comes to understand the commodity flow because, it goes without saying, that an expanding economy will be needing more commodities and energy than a shrinking one. Hence, it is of fundamental importance to monitor how economies are projected to expand in future years.

Now, the International Monetary Fund (IMF) publishes on a regular basis numerous projections, structured by country and region, on how the global GDP will grow in the coming year.

The current HyperVolatility research will dig deeper into those forecasts and analyze how the IMF expects GDP growth to look like in 2024.

However, the study doesn’t end there.

In fact, the current research will also explore the long term trends as far as GDP growth is concerned in order to provide a deeper understanding of which countries will be importing more commodities and will be needing more energy for their economies to function properly.

The forecasts that will be analyzed are those presented in 2 reports written by Goldman Sachs and PriceWaterHouseCooper, which try to predict how countries will grow and how the global GDP ranking will look like in 2050 and 2075.

Click here to read the rest of the research and see the forecasts.

What Will the World Look Like in the Future? was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

Volatility is a key factor when it comes to pricing options and building an effective risk management strategy and this is precisely why this research, which you can read here in full, focuses on the behaviour of volatility in crude oil markets.

The present quantitative research has been based on the implied volatility extracted from Average Price Options (APOs) which are over-the-counter types of options contracts extensively used by commodity market players to hedge their physical market exposure.

What makes APOs so special and wanted by physical market participants is the fact that their underlying contracts are not futures but swaps, which are another very common, over-the-counter financial derivatives utilized by refiners and energy producers to manage the risk in their physical portfolios.

Volatility in Crude Oil Markets: Trading and Risk Management was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

Structuring a portfolio is not particularly easy but when it comes to commodities, where the average volatility can be orders of magnitude higher than in equity markets, the task can become really difficult. In the first part of this quantitative research we found that commodity markets tend to be highly idiosyncratic in nature and consequently a “one size fits all” type of approach is suboptimal at best. In truth, the first research to point out the need for a tailored made approach to commodity investing was raised in another quantitative research entitled Commodity Portfolio Management.

The current HyperVolatility research will focus on extreme returns and how these impact commodity portfolios. Extreme returns have to be managed with extreme care because hedging them, as the analytics will show, makes all the difference between profit and loss.

Please click here to read the rest of the research.

Commodity Portfolio Management: Hedging Market Risk was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why all other crude oils have to be priced according to Brent price fluctuations?

The answer to this question is API gravity, sulphur content and export.
As we previously mentioned there are many types of crude oils in the world but the chemical composition of each crude grade differs slightly. Crude oil is a fossil fuel and it is made of hydrocarbons (molecules of hydrogen and carbon atoms) but what makes the real difference, in terms of commercial value, is the weight of the hydrocarbons. The rule is simple: the lighter, the better. In order to determine how heavy or light petroleum liquids are the American Petroleum Institute introduced a standardized scale called API gravity. The API gravity system is a standardized way to compare and rank the “lightness or heaviness” of diverse crude oils. The system is very simple: the API gravity coefficient measures how heavy or light petroleum liquids are with respect to water. Crude oils with an API gravity greater than 10 are considered to be light (so they float on water) while oils with API lower than 10 are classified as heavy (so they sink when mixed with water). Crude oils with high API values (10 and higher) are lighter and produce greater quantity of marketable product, hence, they are more commercially desirable. This concept can be better understood by looking at the following chart (source: The International Crude Oil Market Report):

The graph displays the distribution of different crude oils according to API gravity (X axis) and sulphur content (Y axis). It is easy to notice that WTI and Brent are both located in the right — hand side of the chart and they are very close to the X axis. The reason these oils are situated in this area is because their API gravity is very high (which means they are light types of oil) and their sulphur content is lower than 0.5% which means they are sweet (the word “sweet” in technical jargon means that there is a low level of impurity). Let’s summarize what has been stated so far:

1) API gravity measures the lightness / heaviness of crude oils

2) API higher than 10 means that the crude oil is light and more profitable in terms of commercial value

3) API lower than 10 means that the crude oil is heavy and produces a minor quantity of commercial product after refining

4) Sulphur content measures the degree of pureness of crude oil, the level of impurity that each crude oil type contains

5) Sulphur content higher than 0.5% indicates a high level of impurity (sour crude oil) that has to be removed

6) Sulphur content lower than 0.5% implies a low level of impurity (sweet crude oil). This condition is preferred because less work is needed and the refining process is faster

7) All the crude oils ranked at the bottom of the right hand side of the chart are considered to be the most attractive under a commercial point of view

The aforementioned bullet points explain fairly well why the Brent is one of the best crude oils in the world but why is it better than the WTI?
The answer is straightforward: the European Brent is exported while the West Texas Intermediate remains within the US. Consequently, the WTI has a “minor impact” on international markets (in reality, a part of the Alaskan oil output is exported to Japan and South Korea but the quantity is so small to be irrelevant in terms of international impact).
There are other chemical and physical aspects that need to be mentioned when talking about crude oil and one of these is certainly viscosity. Viscosity is the “ability” of a specific crude oil or refined product to flow.

Why is this factor important?

The degree of viscosity is very important to determine how crude oil will be stored or transported which means that the cost of carry will be primarily influenced by this variable. Crude oils can be classified according to their viscosity coefficient:

1. Paraffinic crude oils have low viscosity but they are easily flammable. Most of the engines lubricating oils are made of paraffinic crude oil. Paraffinic oils have a high API gravity and therefore tend to be light types of crude oil
2. Naphthenic (or Asphaltic) crude oils have a high viscosity coefficient but they are not easily flammable. This is the case of bitumen. Naphthenic oils have low API gravity and therefore tend to be heavy types of crude oil

This classification is very useful because it helps us understand a bit better how the refining process works. Let’s combine all the information together:

1. Light and sweet crude oils (Brent, WTI, Bonny) have high API gravity, low sulphur content, low viscosity, high flammability and therefore are paraffinic oils. Light and sweet crude oils, once refined, tend to produce high quantity of gasoline
2. Heavy and sour oils (Venezuelan BCF, Russian Urals crude, etc) have low API gravity, high sulphur content, high viscosity, low flammability and therefore are naphthenic oils. Heavy and sour crude oils, once refined, tend to be used as bitumen feedstock

The refining process aims to separate petroleum liquids in different chemical components which will be subsequently treated and combined with solvents to generate new oil derivatives.

How does the process work?

The crude oil is essentially pumped into a furnace and here the raw petroleum releases gases and liquids which are subsequently channeled in a tower to start the fractional distillation process. The point of directing the oil in this tower is to separate or fractionate different chemical components using heat. Specifically, each chemical component will have a specific boiling point and by increasing the temperature every constituent will start vaporizing as soon as its own boiling point will be reached. This process is gradual so the crude oil will fractionate into different gases at different temperatures but it is also continuous, which means that new raw petroleum liquid will be injected into the distillation tower at regular intervals to replace the fluid that has been already fractioned. The refining process usually produces a standardized set of oil derivatives such as gasoline, jet fuel, diesel fuel and asphalt. Nevertheless, other products (methane, propane, kerosene, etc) are often distillated. Oil derivatives have a wide range of applications; here we list some of them:

1. Methane also knows as natural gas, can be used for heating
2. Ethane is usually employed as a feedstock for other production processes (like the one followed to produce plastic)
3. Propane can be used for both cooking and heating
4. Gasoline is primarily used as fuel for vehicles
5. Naphtha is another feedstock and it is generally reused in the petrochemical industry
6. Kerosene (known as paraffin in UK, Ireland, South Asia and South Africa) is predominantly employed to produce Jet fuel oil
7. Gas oils are used to distillate diesel engine fuels or for home heating
8. 8) Fuel oils are reused to power refineries or power stations. Alternatively, they are often utilized as a fuel for ships but in this case they are referred to as bunker fuel or bunker fuel oil

Now, this information is surely very important to anyone who is seriously thinking to invest or trade oil markets. Oil fundamentals are sometimes overlooked but a sound understanding of the dynamics underlying the fossil fuel industry is essential to fully comprehend market movements. As we anticipated at the beginning of this article, this is only the first part of a broader project.
If you are interested in trading crude oil you may want to read some HyperVolatility researches dealing with this topic:

“The Oil Arbitrage: Brent vs WTI”

“Oil Fundamentals: Reserves and Import/Export Dynamics”

“Oil Fundamentals: Upstream, Midstream, Downstream & Geopolitics”

“The Crack Spread”

“The Baltic Dry Index”

Visit HyperVolatility for more quant researches

This is information — not financial advice or recommendation. The content and materials featured or linked to are for your information and education only and are not attended to address your particular personal requirements. The information does not constitute financial advice or recommendation and should not be considered as such.

Oil Fundamentals: Crude Oil Grades and Refining Process was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

The American crude oil chart shows a very strong relationship with Euro, Swiss Franc and South African Rand. However, the correlation is strong and positive for Swiss Franc (+0.36) and Euro (+0.34) but it is negative for the South African currency (-0.34). The positive connection with the Euro is a consequence of the fact that the Single currency is responsible for the 56.7% of US Dollar Index fluctuations. The Swiss Franc, being a European currency, shows a robust positive correlation to the WTI (+0.36), however, the chart signals that the Swiss Franc is not a good hedging tool for the American oil market. Usually, the Swiss currency is adopted by many portfolio managers and market players as a hedge against volatile periods in risky assets but, when it comes to WTI oil, this is not the case. The negative correlation with the South African Rand (-0.34) is given by the fact that South Africa is the second largest oil refiner in Africa. Nevertheless, almost all the oil that is imported belongs to OPEC countries. Consequently, the Rand is more linked to the price of oils extracted from OPEC countries than to the American West Texas Intermediate. Finally, if we exclude a “mild correlation” with the Australian and Canadian Dollars, the remaining exchanges (Yens, British Pounds and New Zealand Dollars) do not show a great sensitivity to WTI price changes. The next market under examination will be Gold:

The gold market has very good positive connections with many asset classes. If we exclude New Zealand Dollars (+0.07), the Euro (+0.20) and the South African Rand (-0.65), gold futures are strongly bounded to many exchanges. The strong positive correlation to Australian Dollars (+0.62) is obvious because Australia is one of the largest gold exporters in the world while the +0.69 rapport with the Japanese Yen is a signal that many market players started to use again gold as a hedging tool for their portfolios. Furthermore, Canada (+0.57) has large gold mines while the UK (+0.63) is a strong importer and it primarily purchases gold for luxury goods or investment purposes. Once again the South African Rand is a standalone market because its strong negative correlation to gold futures (-0.65) is not just due to portfolio hedging but also to strong fundamental reasons. Specifically, the South African mining industry has been subject to many strikes in the last 3–4 years that greatly influenced its extraction rate. Consequently, the SA gold’s output precipitously fell and this could have caused a major loss of “sensitivity” between gold prices and the local currency. The next commodity market is the Henry Hub Natural Gas:

The Henry Hub is the core distribution point in the natural gas pipeline scheme located in Louisiana, USA. Natural gas futures traded on the NYMEX have been named after it because of the great logistic importance of this area. The above reported chart shows an unstable negative correlation to Australian, Canadian Dollars and British Pounds (-0.22, -0.30 and -0.33 respectively), a rather weak but positive link to New Zealand Dollars and Euros and no correlation at all to Swiss Franc futures(-0.03). On the other hand, there are two currencies that are strongly correlated to natural gas prices: Japanese Yen and the South African Rand. The reasons Japanese Yens have a robust negative relationship (-0.57) to the Henry Hub is that the Asian country has recently doubled its nat–gas consumption but the local demand is almost entirely satisfied via imports (circa 4,500 billion cubic feet). Furthermore, the Japanese Yen is often considered to be a “safe haven” type of investment, hence, many market players will tend to buy the Asian currency when risky assets plummet. The negative relationship between Yens and nat–gas prices is an evident sign that many portfolio managers tend to hedge their natural gas positions with the Japanese Yen. Conversely, the great explosion in natural gas consumption in South Africa (over 160 billion cubic feet in 2011 against 58.2 billions in 2000) and the low import levels make the South African currency rather “susceptible” to oscillations in natural gas prices.

Let’s summarize the main findings in a few bullet points:

WTI CRUDE OIL
1) The correlation is strong and positive with Swiss Franc (+0.36) and Euro (+0.34) but it is negative for the South African currency (-0.34)

2) South Africa is the second largest oil refiner in Africa but almost all the in–flowing oil is imported from OPEC countries. This explains the negative correlation (-0.34) with the Rand

GOLD
1) Australia is one of the largest gold exporters in the world, hence, Australian Dollars are strongly connected to this commodity (+0.62)

2) The +0.69 rapport with the Japanese Yen implies that gold is still considered to be a hedging tool for portfolio risk

3) Canadian Dollars are positively correlated to gold (+0.57) because Canada has large gold mines

4) There is a positive and robust link between gold and British Pounds (+0.63) because UK is a strong importer and Great Britain buys gold for luxury goods or investment purposes

5) The South African gold’s output plunged in recent years due to several strikes in the mining industry. Hence, the drop in the SA gold extraction rate could have caused the negative connection between gold prices and the local currency (-0.65)

HENRY HUB NATURAL GAS
1) There is a robust negative relationship with Japanese Yens (-0.57) because Japan increased its natural gas consumption but it imports almost all the natural gas needed (circa 4,500 billion cubic feet)

2) Negative correlation with the Japanese Yen (-0.57) implies that natural gas portfolios tends to be hedged with the Asian currency

3) The large increase in natural gas consumption in South Africa (over 160 billion cubic feet in 2011 against 58.2 billions in 2000) and a strong local production strengthened the link between the South African currency and natural gas prices (+0.73)

Visit HyperVolatility for more quant researches

All traders and investors interested in trading commodities and in particular Crude Oil and Gold are strongly advised to read the following HyperVolatility researches:
“Commodity Volatility Indices: OVX and GVZ”
“The Oil Arbitrage: Brent vs WTI”
“Trading Gold and Silver: A Realized Volatility Approach”

This is information — not financial advice or recommendation. The content and materials featured or linked to are for your information and education only and are not attended to address your particular personal requirements. The information does not constitute financial advice or recommendation and should not be considered as such.

Commodities and Currencies: Inter–Market Analysis was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

The most important levels to observe are the 25%, the median and the 75% ones. The OVX has a median value fluctuating around 32.8% while the GVZ is much lower and, on average, it does not get higher than 18.1%. In case of low volatility environments the OVX remains around the range 29% — 30% while the GVZ tends to oscillate around 16%. On the other hand, high volatility days would probably see the OVX moving around 37% while the GVZ does not usually surpass the 21.3%. These levels are crucial to anyone who wants to trade oil or gold volatilities because they will provide buy / sell signals for options traders. Let’s list some of them (remember these are not recommendations but only a general rule):

1) Long Gold or WTI Oil volatility when the OVX is below 30% and the GVZ is around 16%

2) Range trading strategies, such as condors or butterflies, should be adopted when the OVX is higher than 35% and the GVZ is above 19%

3) If the OVX or the GVZ are fluctuating around their median levels, it implies that some major movement is about to occur. Therefore long volatility strategies could be implemented when the OVX is around 30% — 31% and the GVZ is in the 17% — 18% interval.

The fact that a volatility index has a high value does not mean that it is the most volatile one. We already tried to address this issue when dealing with equity volatility indices so now we will attempt to explain such a phenomenon for commodity volatilities too. The next chart plots the volatilities for the OVX and GVZ indices:

The chart clearly displays the volatility of both commodity volatility indices. The relationship between the OVX and the GVZ is positive, in fact, the correlation between the 2 indices is +0.77 while the correlation between the volatility of the OVX and the volatility of the GVZ is +0.62. It is interesting to notice that the 10% — 13% interval is a key mean reverting level in both markets because all the time the volatility of the OVX or GVZ touched this level both asset classes experienced a major volatility explosion. The next chart shows the distribution ranking for the volatility of commodity volatility indices:

The distribution ranking demonstrates that the GVZ, although lower than the OVX in volatility points, is more volatile than the OVX index. Specifically, GVZ’s volatility oscillates on average around the 18% while the OVX’s oscillation rate does not go higher than 13.3%. In low volatility environments the oil volatility index’s fluctuations rate is not lower than 10.6% while gold’s index oscillations rarely gets lower than 13.9% (these are the mean reverting points for the volatility of commodity volatilities). On the other hand, volatility explosions are not higher than 20.7% for the volatility of the OVX and 22.5% for the GVZ. Now we can improve on the list of buy/sell signals we previously mentioned (again these are not trading recommendations but only a general guide):

1. Long Gold or WTI Oil volatility when OVX’s volatility is around 10% — 11% or when GVZ’s volatility is within 13% — 14%
2. Condors or butterflies, should be entered when OVX’s volatility is higher than 19% — 20% and the GVZ’s one is above the 20% — 21% interval
3. If OVX and GVZ volatilities are oscillating around their median values some long volatility strategies are definitely safer than selling straddles or naked options. Hence, when the volatility of the OVX is around 13% — 14% and the volatility of the GVZ is in the 18% — 19% interval, it is a good risk management practice to hedge the portfolio against a potentially higher degree of market fluctuations

If you are interested in trading gold futures or options you might want to read our research “Trading Gold and Silver: A Realized Volatility Approach”. Instead, if you are looking to trade WTI oil futures or options you will find these researches “The Oil Arbitrage: Brent vs WTI” and “Commodity Volatility Indices: OVX and GVZ” very helpful

Visit HyperVolatility for more quant researches

This is information — not financial advice or recommendation. The content and materials featured or linked to are for your information and education only and are not attended to address your particular personal requirements. The information does not constitute financial advice or recommendation and should not be considered as such.

Commodity Volatility Indices: OVX and GVZ was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Brent/WTI arbitrage (the word arbitrage is a misnomer because we are buying and selling two different asset classes) is a fairly popular trading technique within the energy sector and its aim is to profit from price discrepancies. The strategy is reasonably simple and it consists of contemporarily selling the WTI and buying the Brent (short arb) or selling the Brent and buying the WTI (long arb). Clearly, this is a spread trading technique rather easy to implement and control because both Brent and WTI futures share the same size (1,000 barrels) while the tick value (1 cent per barrel) equals to $ 10 for both contracts.

How does the trade work? Let’s assume that a trader decides to sell WTI and buy Brent futures (short arb). He sells the WTI at $ 100 and buys the Brent at $ 110 and he will make money if the 2 asset classes will move in opposite directions. If the WTI drops to $ 97 while the Brent closes at $ 112 our hypothetical trader would have made a $ 3,000 profit from the WTI and $ 2,000 from the Brent for each contract traded.

What happens if WTI and Brent move in the same direction? The strategy would still be profitable if the price augment in the Brent market outweighs the rise in WTI futures. If the Brent gains $ 3 and the WTI $ 1.5, the trader would make a $ 3,000 profit from the long Brent position but he would lose $ 1,500 on the short WTI contract which implies that the overall profit would be equal to $ 3,000 — $ 1,500 = $ 1,500

As you can see the trade would still show a profit because in our example WTI and Brent experienced different volatilities and consequently their fluctuations were not symmetrical in terms of magnitude (the first moved 3 dollars and the second only $ 1.5). However, should the Brent had moved lower and the WTI higher the short WTI / long Brent position would have lost money.

The chart #1 shows how the most important oils oscillated since 2009 until 2011:

It is evident that until 2011both WTI and Brent were moving symmetrically but for some fundamental reasons, such as global demand and some logistic problems with the WTI, the prices started to diverge and the spread became rather large. On the other hand, the narrowing of the arb from September 2011 onwards is mainly due to an increased demand and to the construction of the Seaway pipeline which facilitates the transportation of the WTI from the Cushing in Oklahoma to Freeport in Texas. Let’s have a look at the WTI/Brent spread now:

The chart #2 shows very clearly that since 2009 until the beginning of 2011 the differential oscillated following a mean reverting process (because it always tended to get back to the 0 line) and it used to fluctuate within fixed boundaries (because it rarely surpassed the $ 2.5 threshold and infrequently remained below the — $2 level for an extended period of time). However, the scenario has quite changed because in 2011 the Brent/WTI spread increased substantially and achieved the $ 25 level. If we go back to the first chart we can immediately understand what caused such a high spread: the Brent price kept increasing while WTI futures prices kept dropping.

How can a trader take advantage of such divergence? When the trade should be triggered?

Buying or selling the oil arb is up to the trader and it depends on fundamental data such as supply/demand forces, industrial productivity, etc but it is possible to identify when the trade could have a higher probability of success. The chart #3 will help us prove our point:

The graph shows the correlation (which fluctuates within -1 and 1) between Brent and WTI since 2009 until the end of the 2011 and its interpretation is straightforward: the higher the correlation, the stronger the relationship between the 2 asset classes. The correlation on average is rather high which means that Brent and WTI tend to experience similar fluctuations, although with different volatilities, but there is a second important characteristic that it is very useful in practical terms: the correlation is mean reverting because it tends to drop and then explode again.

In practical terms, all the time the correlation index drops the relationship between Brent and WTI weakens, hence, the probability of dissimilar fluctuations amplify. Conversely, an increasing correlation would imply the opposite scenario.

We now know that a plunge in the correlation index would increase the probability of maximizing our profits because it would highlight that the relationship between the 2 asset classes is not going to be strong and that the spread will likely expand. However, in real trading conditions we will need specific entry points, some numerical thresholds to look at in order to trigger our trades and the following tables should be a valuable tool for anyone interested in trading the oil arb:

Bear in mind that these are not trading recommendations but merely a guide and the price / correlation levels refer to the period 2009–2011.

The table #1 represents the price distribution of the Brent/WTI spread. The outcome of our research shows that the lowest price achieved by the spread is $ -5.42 (which means that the Brent was lower than the WTI) while the highest point was $ 26.84. The percentage row displays the percentage of observations below the reported price levels. In other words, the 24.97% of total observations were below the $ -0.36, almost 50% of the Brent/WTI spread prices were below the $ 2.28 level while the price fluctuated below the $10.98 threshold in the 74.77% of cases. Now let’s see what the correlation key points are:

On average the correlation is around 0.82 but in the 25% of cases it dropped to 0.62 and it remained lower than 0.91 almost the 80% of the time. The extreme points are -0.35 and 0.99 that have been touched only once.

Strategy Analysis

1) The Brent/WTI spread fluctuated within $ 2 and $ 2.3 most of the time

2) The correlation is usually fairly strong and it frequently oscillates around 0.78 and 0.82

3) In order to have a reliable entry point both price and correlation should be out of their ranges. We should be in a situation where there is an evident mismatch

4) Entry points are signalled by a breakthrough of the aforementioned price and correlation levels because if the arb price is higher than $ 2.3 and the correlation is lower than 0.78 then the probability of success is higher. Needless to say that good opportunities arise also when the arb price is lower than $ 2 dollars and the correlation is higher than $ 0.8

If you liked this research, you might also be interested in the following ones:

“Oil Fundamentals: Crude Oil Grades and Refining Process”

“Oil Fundamentals: Reserves and Import/Export Dynamics”

“Oil Fundamentals: Upstream, Midstream, Downstream & Geopolitics”

“The Crack Spread”

“The Baltic Dry Index”

Visit HyperVolatility for more quant researches

This is information — not financial advice or recommendation. The content and materials featured or linked to are for your information and education only and are not attended to address your particular personal requirements. The information does not constitute financial advice or recommendation and should not be considered as such.

The Oil Arbitrage: Brent vs WTI was originally published in HyperVolatility on Medium, where people are continuing the conversation by highlighting and responding to this story.

Options are way older than one might imagine. Aristotele mentioned options for the first time in the “Thales of Miletus” (624 to 527 B.C.), Dutch tulip traders began trading options at the beginning of 1600 while in 1968 stock options have been traded for the first time at the Chicago Board Options Exchange (CBOE). The pricing of options has always attracted academics and mathematicians but the first breakthrough in this field was pioneered at the beginning of the 1900 by Bachelier. He literally discovered a new way to look at option valuation, however, the real shift between academia and business occurred in 1973 when Black, Scholes and Merton developed the most popular and used option pricing model. Such a discovery opened an entire new era for both academics and market players. Being one of the most crucial financial derivatives in the global market, options are now widely adopted as an effective tool to leverage assets or control portfolio risk. Nowadays, it is easy to find articles, researches and studies on option pricing models but this article will instead focus on options Greeks and in particular first-order Greeks (derived in the BSM world). Options Greeks are important indicators for assessing the degree of risk coming from exogenous variables, in fact, they measure option premium’s sensitivities to small changes in different parameters. Mathematically, Greeks are the partial derivatives of the option price with respect to different factors such as volatility, interest rate and time decay.

The purpose of this article is to explain, as clearly as possible, how Options Greeks work but we will concentrate only on the most popular ones: Delta, Gamma, Vega (or Kappa), Theta and Rho. It is worth mentioning that all the charts that will be presented have been extrapolated by assuming that the underlying is a WTI futures contract, that the options have a strike price (X) of 100, that the risk-free interest rate (r) is 0.5%, that the cost of carry (b) is 0 while implied volatility is 10%.

Delta: Delta measures the sensitiveness of the option’s price to a $1 fluctuation in the underlying asset price. The chart displays how the Delta moves in respect to the underlying price S and time to maturity T:

The chart clearly shows that in-the-money call options have much higher Delta values than out-of-the-money options while ATM options have a Delta which oscillates around 0.5. Call options have a Delta which ranges between 0 and 1 and it gets higher as the underlying approaches the strike price of the option which means that out-of-the-money call options will have a Delta close to 0 while ITM options will have a Delta fluctuating around 1. Many traders think of Delta as the probability of an option expiring in-the-money but this interpretation is not correct because the N(d) term in its formula expresses the probability of the option expiring ITM but only in a risk-neutral world. In real trading conditions higher Delta calls do have a higher probability to expire ITM than lower Delta ones, however, the number itself does not provide a reliable source of information because everything depends on the underlying. The Delta simply expresses the exposure of the options premium to the underlying: a positive Delta tells you that the premium will rise if the underlying asset will trend higher and it will decrease in the opposite scenario. Put options, instead, have a negative Delta which ranges between -1 and 0 and the below reported chart displays its fluctuation in respect to the underlying asset.

It is easy to notice that as the underlying asset moves below the $100 threshold (the strike price of our hypothetical put option) the Delta approaches -1, which implies that ITM put options have a negative Delta close to -1, while OTM options have a Delta oscillating around 0. In practical trading the value of the Delta is very important because it tells you how the options premium is going to change in the case the underlying moves by $1. Let’s assume you purchase a 100 call options on crude oil with a Delta of +0.5 and the premium was $1,000. If the option is at-the-money the WTI (the underlying asset) will be at $100 but if oil futures go up by $1 dollar to $101 the premium of your long call will move to $1,500. The same applies to put options but in this case the ATM Delta will be -0.5 and your long put option position will generate a profit if WTI futures move from $100 to $99.

Gamma: Gamma measures Delta’s sensitivity to a $1 movement in the underlying asset price and it is identical for both call and put options. Gamma reaches its maximum when the underlying price is a little bit smaller, not exactly equal, to the strike of the option and the chart shows quite evidently that for ATM option Gamma is significantly higher than for OTM and ITM options.

The fact that Gamma is higher for ATM options makes sense because it is nothing but the quantification of how fast the Delta is going to change and an ATM option will have a very sensitive Delta because every single oscillation in the underlying asset will alter it.

How Gamma can help us in trading? How do we interpret it?

Again, the value of Gamma is simply telling you how fast the Delta will move in the case the underlying asset experience a $1 oscillation. Let’s assume we have an ATM call option on WTI with a Delta of +0.5 while futures prices are moving around $100 and Gamma is 0.08, what does that imply? The interpretation is rather simple: a 0.08 gamma is telling us that our ATM call, in the case the underlying moves by $1 to $101, will see its Delta increasing to +0.58 from +0.5

Vega (or Kappa): Vega is the option’s sensitivity to a 1% movement in implied volatility and it is identical for both call and put options. The below reported 3-D chart displays Vega as a function of the asset price and time to maturity for a WTI options with strike at 100, interest rate at 0.5% and implied volatility at 10% (the cost of carry is set to 0 because we are dealing with commodity options).

The chart clearly highlights the fact that Vega is much higher for ATM options than for ITM and OTM options. The shape of Vega as a function of the underlying asset price makes sense because ATM options have by far the highest volatility potential but what does Vega really tell us in real trading conditions? Again, Vega (or Kappa) measures the dollar change in case of a 1% shift in implied volatility, therefore, an at-the-money WTI options whose value is $1,000 with a Vega of , say, 100 will be worth $1,100 if the implied volatility moves from 20% to 21%. Vega is a very important risk measure for options traders because it estimates how your P/L is going to change as a function of implied volatility. Implied volatility is the key factor in options pricing because the price of a single options will vary according to this number and this is precisely why implied volatility and Vega are essential to options trading.

Theta: Theta measures option’s sensitivity to a small change in time to maturity (T). As time to maturity is always decreasing it is normal to express Theta as negative partial derivatives of the option price with respect to T. Theta represents the time decay of option prices in terms of a 1 year move in time to maturity and to view the value of Theta for a 1 day move we should divide it by 365 or 252 (the number of trading days in one year). The below reported chart shows how Theta moves:

Theta is evidently negative for at-the-money options and the reason behind this phenomenon is that ATM options have the highest volatility potential, therefore, the impact of time decay is higher. Think of an option like an air balloon which loses a bit of air every day. The at-the-money options are right in the middle because they could become ITM or they could get back into the OTM “limbo” and therefore they contain a lot of air, consequently, if they have got more air than all other balloons they will lose more than others when the time passes. Let’s look at a practical example. Let’s assume we are long an ATM call option whose value is $1,000 and has a Theta equals to -25, if the day after both the underlying price and volatility are still where they were 1 day before our long call position will lose $25.

Rho: Rho is the option’s sensitivity to a change in the risk-free interest rate and the next chart summarises how it fluctuates with respect to the underlying asset:

ITM options are more influenced by changes in interest rates (negative Rho) because the premium of these options is higher and therefore a fluctuation in the cost of money (interest rate) would inevitably cause a higher impact on high-premium instruments. Furthermore, it is rather clear that long dated options are much more affected by changes in interest rates than short-dated derivatives. The below reported chart displays how Rho oscillates when dealing with put options:

The Rho graph for put options mirrors what it has been stated for calls: ITM have a larger exposure than ATM and OTM put options to interest rate changes and long term derivatives are much more affected by Rho than in the short term (even in this case the 3-D graph displays negative values). As previously mentioned Rho measures how much the option’s premium is going to change when interest rates move by 1%. Hence, an increase in interest rates will augment the value of a hypothetical call option and the rise will be equal to Rho. In other words, the value of the call option will increase by $50 if interest rates move from 5% to 6% and our WTI call option has a premium of $1,000 but Rho equals 50.

As stated at the beginning of the present report this is only the first part and a second article dealing with second order Greeks will be posted soon.

If you think this research was informative for you, have a look also at the following ones:

“Options Greeks: Vanna, Charm, Vomma, DvegaDtime”

“Option Greeks and Hedging Strategies”

“Extracting Implied Volatility: Newton-Raphson, Secant and Bisection Approaches”

“The Pricing of Commodity Options”

“The VIX Index: step by step”

“The Volatility Smile”

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