Categories

Archives

The Crack Spread

The crack spread is probably the most important financial strategy within the energy industry. The price of the crack spread is so crucial that it is closely monitored by commercials, hedge funds, banks, energy companies and governments. The value of the crack spread summarizes and combines, in 1 strategy, the price of crude oil and its two most important derivatives: diesel and gasoline. The entire oil industry, which still plays a very significant role as far as energy production is concerned, is strongly and inevitably linked to the performance of the crack spread. The present research investigates the structure of the aforementioned strategy and the quantitative relationships of and among its components.

Commodity traders and portfolio managers are no strangers to the concept of spread trading. In fact, many commodities are powerfully linked by common factors such as fundamentals, demand/supply dynamics, extraction/production procedures, import/export processes, shipping/transportation routes, geographical availability, geopolitical variables and so forth. There are many types of spreads within the commodity sector (natural gas vs power, copper vs aluminium, gold vs silver, platinum vs palladium) and all of them are based on some of the above mentioned common factors. The crack spread, the focus of the present research, is built using WTI Crude oil, RBOB gasoline and diesel prices. Nevertheless, it is worth mentioning that the term “crack spread” can also refer to another spread combination which involves WTI, RBOB and heating oil or to the less popular, but still frequently traded, European crack spread (Brent Crude vs European Gasoil).

The crack spread provides a fairly good approximation of the margin earned by refiners and this is precisely why this strategy is at the core of the oil industry. In fact, it simply expresses the relative value of the cost of crude oil with respect to refined products (gasoline and diesel). Refiners can enter a simple 1:1 crack spread (crude oil vs gasoline or crude oil vs diesel), however, in a typical refinery the gasoline output is usually two times larger than that of distillate fuel oils (diesel, heating oil, jet fuel oil, bunker oil, etc). Consequentially, it would be more appropriate to trade diversified crack spread constructions like a 3:2:1 spread or a 5:3:2 spread. The present analysis will focus on the 3:2:1 crack spread which is the most popular and useful construction because it meets the needs of many refiners. The formula for calculating the price of a crack spread is the following:

[(2 * RBOB $/bb + 1 * ULSD $/bb) – (3 * WTI $/bb)] / 3

The calculation is simple. However, RBOB and ULSD prices (or RBOB and heating oil prices) are expressed in $ per gallon and not in $ per barrel. Hence, they have to be multiplied by 42 (1 barrel contains 42 gallons) in order to express them in barrel terms. For example, on the 9th of June 2014, RBOB and NY Harbor Diesel closed at $3.049 and $2.886 per gallon respectively, hence, their “per–barrel prices” would automatically be ($3.049 *42) = $128.058 for RBOB and ($2.886*42) = $121.21 for ULSD.

Refiners are naturally long the crack because they have to purchase crude oil, refine it and then sell the products. Hence, the profit margin comes from the relation between crude prices and product prices while the biggest concerns for refiners come from any unwanted changes in such relation. Refiners predominantly fear increases in crude oil prices and drops in product prices because in this case their profit margin would shrink. The refiner, in order to lock in the price, will therefore cover its physical position on the financial market. Let’s assume a refiner decides to lock in the current margin because he fears an increase in the price of crude oil with respect to product prices. The best strategy he can implement involves selling a crack spread so that the long crude position will offset potential augments in oil prices while the short positions on products will counterbalance losses coming from potential plunges in diesel and/or gasoline prices. WTI is trading at $100.52, RBOB gasoline trades at $2.905 per gallon while NY Harbor diesel is at $2.927 per gallon. Consequently, RBOB gasoline and NY Harbor diesel in barrel terms are priced at ($2.905*42) =$122.01 and ($2.927*42) = $122.93. The 3:2:1 crack spread can now be constructed, so the refiner will purchase 3 WTI crude contracts at $100.52 and simultaneously sell 2 gasoline futures at $122.01 and 1 diesel futures at $122.93. The margin from the financial crack spread is equivalent to:

[(2 * $122.01 + 1 * $122.93) – (3 * $100.52)] / 3 = $21.79

The refiner has now locked in a $21.79 margin and it has secured its transaction. In fact, any potential rises in crude oil prices will be counterbalanced by a larger profit on the long WTI futures position while any potential plunges in gasoline or diesel prices will be offset by the gains on the financial short positions. Clearly, refiners tend to hedge looking forward so the contracts that will be used can expire in 1, 2 or 3 months from the implementation time depending on the delivery date. On the other hand, there are times where refiners are forced to sell crude oil and buy products. Consequentially, in order to hedge such exposure, they need to implement a reverse crack spread (also known as crack spread hedge). The reverse crack spread is just the opposite of a regular crack, in fact, it involves taking a short futures position on WTI crude oil and long positions on gasoline and diesel futures. Why would a refiner sell crude and buy products? Isn’t that counterproductive? Refineries work at full capacity to satisfy the demand for oil derivatives, however, forced shutdowns, due to machine breakdowns or unexpected technical problems, can happen. Contractual agreements must always be honoured, therefore, in the unfortunate event of a technical breakdown, refiners have to purchase products from someone else and deliver it to their clients as per contract. Furthermore, refiners tend to buy crude oil 2–3 months in advance so a breakdown would obligate them to sell the amount already delivered, or about to be delivered, because a technical “crash” would not allow them to refine it. These situations can easily happen, not that frequently, but they do happen. Hence, the best way to protect the business is to enter a reverse crack spread where crude oil is shorted and products are bought. The final margin is the difference between the operations on the physical market (selling WTI barrels and purchasing barrels of products) and the ones on the financial market (selling WTI futures while purchasing gasoline and diesel futures). Let’s assume that on the physical market the refiner faces a loss of $22.52 because he is forced to buy many thousands of barrels of products at a higher price due to the unexpected breakdown. On the other hand, thanks to the reverse crack strategy, he manages to lock in a $23.46 profit. The final margin would be $23.46 – $22.52 = $0.94.

Again, refiners do not usually buy products and sell crude unless obligated to do so, hence, the crack spread hedge is implemented only in particular occasions.

It is also worth mentioning that many refiners may want to enter different types of crack spread in order to cover the so–called energy basis risk. The basis risk is the difference in price between the same product delivered or traded in 2 different locations. The price difference between US Gulf Coast Ultra Low Sulphur Diesel and New York Harbor Ultra Low Sulphur Diesel is an example of basis risk. A refiner that uses only NY Harbour diesel will build a crack spread using NY Harbor ULSD futures while another one may want to take simultaneous positions on Gulf Coast and NY Harbor diesel because he refines them both.

The following section of the present research will quantitatively analyze the crack spread and it will separate each component of the strategy to study its behaviour and fluctuations. The first chart displays the price oscillations of the 3:2:1 crack spread that has been synthetically replicated using WTI crude, RBOB gasoline and NY Harbor diesel futures prices ranging from June 2006 to June 2014:

Crack Spread 3:2:1

It is evident that the margin earned by refiners has remarkably fluctuated over the past years and it is safe to say that geopolitical factors have strongly impacted its performance. The chart shows that the price of the crack spread has predominantly oscillated within $10 and $40 with occasional deviations from such channel. The price drop in 2008 and the violent spike in 2012 are clear examples of what happens to the margin when crude oil and products prices diverge in relative terms: crude oil goes up while products prices plunge or vice versa. The following graph summarizes the performance of the crack spread on a yearly basis:

Crack Spread average price

The figures reported in each bubble represent the average price for the crack spread in that year. The graph shows rather eloquently that, in the time interval 2006–2010, the price action moved between $13.3 and $17.3. However, the trend observed during the aforementioned 5 years was clearly bearish and the refiners’ margins decreased by $2–$3. The second part of the chart, instead, displays a diametrically opposite scenario. In fact, the data for the interval 2011–2014 show a violent explosion of the price action and a consequential widening of the margins. The highest average price was achieved in 2012 ($34.05) while 2013 and the first half of 2014 registered lower average prices ($25.55 and $23.77 respectively). It is interesting to notice that the price gap between 2010 and 2011 is as high as $15.94. Once again, the price jump was due to a divergence between the price of WTI, which at the beginning of 2011 dropped below $90 in several occasions, and the price of gasoline and diesel that, contrarily, remained constant. The initial imbalance between crude oil and products, however, persisted throughout the rest of the year even if the WTI recovered and moved back in the $100 area. It is important to point out that in November 2011 the Seaway pipeline project, which reversed the flow of crude oil, allowing its transportation from the Cushing to Houston’s vast refining area, got started. This project, which was completed on May 2012, has without a doubt contributed to increase the margin for refiners.

The next chart displays the fluctuations of the realized volatility for each component of the crack spread: WTI Crude Oil, RBOB gasoline and Ultra Low Sulphur diesel:

Volatility: WTI, Gasoline, Diesel

At a first glance, it is clear that the most volatile component is RBOB gasoline because the spikes in volatility are usually more violent in this market than in all the others. The second component, as far as volatility fluctuations are concerned, is WTI Crude Oil because its realized volatility is rather close to the RBOB one but slightly lower. The least volatile component of the entire crack spread is the diesel. In fact, it is evident that diesel realized volatility is well below the RBOB one and it is lower than the volatility curve observed for crude prices. The next graph, in order to provide a more accurate and quantitative approach, plots the distribution of the realized volatilities for each component and it ranks them:

Volatility Distribution: WTI, Gasoline, Diesel

The above reported chart eloquently confirms that RBOB is the asset class with the highest average volatility (37.73%), followed by WTI (27.97%) and ULSD (26.32%). It is important to point out that we are dealing with commodities and therefore it is not surprising to observe average volatilities well higher than the ones usually obtained by filtering equity indices data. The RBOB market is so volatile that its Low range values oscillate around 30.28% while WTI and diesel experience Low values fluctuating around 21.16% and 19.76% respectively. The same scenario can be easily noticed in the High segment of the distribution because, even in this case, the RBOB has the highest figure (46.61%) while WTI (35.76%) and ULSD (34.07%) rank lower. The examination of the extreme values, Minimum and Maximum, shows rather similar results. In particular, the Minimum segment has the identical ranking seen so far: RBOB is still the most volatile (16.68%), WTI is the second most volatile (11.15%) while diesel remains the third most volatile component (8.55%). The analysis of the Maximum segment, instead, provides some interesting evidence. Firstly, realized volatility spikes within energy markets can be rather violent and aggressive, therefore, it is not surprising to see values well beyond 100% for both RBOB gasoline (125.46%) and WTI Crude Oil (122.63%) while ULSD ranks again 3rd (81.26%).Secondly, it is very interesting to notice that in the Maximum segment, WTI values are very close to RBOB ones implying that extreme realized volatility explosions in the American crude oil market can be tremendously violent. Numerically speaking, a realized volatility explosion, that would cause the volatility to shift from the Medium segment to the High segment, would mean a 232.5% increase for RBOB gasoline but, in the case of WTI, it would imply an increment of 365.9%. It means that WTI extreme volatility explosions can be up to 57.3% more aggressive than the ones on RBOB which is, on average, the most volatile component of the crack.

So far, the concepts that have been discussed and expanded are:

1) The fundamental reasons behind the crack spread

2) How to construct a crack spread

3) Utilization of the crack spread for hedging purposes

4) Analysis of the crack spread price

5) Volatility analysis of the components

The last section of the present research will conclude the investigation on the components of the crack spread. The previous study showed that RBOB gasoline is the most volatile component of the strategy but, in order to quantitatively define which of the 3 asset classes influence crack spread prices the most, it is necessary to run a correlation analysis:

Crack Spread: Correlation Matrix

The correlation matrix is divided into 2 groups. The first group consists of 3 sets of bars containing the distribution of the correlation coefficients calculated by running the analysis between one component against the other: RBOB vs ULSD, WTI vs RBOB and WTI vs ULSD. The second group, instead, presents the distribution of the overall numerical relationships between each component and the crack spread itself: WTI vs Crack Spread, RBOB vs Crack Spread and ULSD vs Crack Spread. Medium correlation bars will be used as a proxy for long term correlation because they provide an assessment of the average connection among the variables under examination. The chart suggests that all components are well linked to each other, in fact, the RBOB/ULSD correlation is +0.70 while the WTI/RBOB rapport is +0.67. The strongest coefficient is observed for the WTI/ULSD pair and in this case the figure is as high as +0.86. The crack spread strategy, instead, displays a robust relationship with RBOB gasoline (+0.78), a very weak one with respect to diesel (+0.30) and an almost non–existent link to WTI (–0.09). The high correlation coefficient between gasoline and crack spread is due to the high volatile nature of the RBOB market. The high volatility in gasoline prices, in fact, is likely to produce sudden and more frequent changes to the price of the crack spread. It is important to mention that the strong linear relationship detected by the correlation analysis, between RBOB and crack spread , has been confirmed also by the regression analysis. In fact, the adjusted–R squared values obtained by regressing every single component against the crack spread concluded that RBOB gasoline is indeed the asset class that influences the price of the crack the most. Numerically speaking, the computed adjusted–R squared for the WTI/Crack Spread regression was 6.75% and it confirms the extremely low linear relationship between WTI and crack prices. The adjusted–R squared for the ULSD/Crack Spread regression was 24.65% and, even in this case, the weak connection is confirmed. The adjusted–R squared for the RBOB/Crack Spread regression was 45.5% and it robustly confirms the relevant linear relationship between gasoline prices and the price of the crack spread.

The Baltic Dry Index

The Baltic Dry Index measures shipping activity of raw materials around the world. In particular, the BDI provides a very efficient way to quantify and evaluate the strength of the global demand for commodities and raw materials. The Baltic Dry Index is compiled, on a daily basis, by the Baltic Exchange, and it is built thanks to the information gathered from the largest dry bulk shippers worldwide. Specifically, the Baltic Exchange collects the prices applied by dry bulk shippers for more than 20 shipping routes all over the world. The BDI is actually an average of 4 different components: The Baltic Capesize Index, The Baltic Panamax Index, the Baltic Supramax Index and the Baltic Handysize Index. What these indices are and what do they track? The fluctuations of the aforementioned indices are based upon the activity of 4 different types of ships: the Handysize, the Supramax, the Panamax and the Capemax. Let’s analyze them one at the time in order to highlight the difference among them:

1) Handysize Ships: they account for approximately 34% of the global fleet and can carry 15,000–35,000 dead weight tons of cargo

2) Supramax Ships: they account for approximately 37% of the global fleet and can carry 45,000–59,000 dead weight tons of cargo

3) Panamax Ships: they account for approximately 19% of the global fleet and can carry 60,000–80,000 dead weight tons of cargo. Panamax ships are the largest ships allowed through the Panama Canal

4) Capemax Ships: they account for approximately 10% of the global fleet and can carry more than 100,000 dead weight tons of cargo and they are too big to pass through the Panama Canal

As previously mentioned, every index is specifically created to keep track of the commercial activity connected to the 4 most important type of ship and this is precisely why the BDI is built upon them. Mathematically, the Baltic Dry Index is calculated using the following formula:

BDI = ((CapesizeTCavg + PanamaxTCavg + SupramaxTCavg + HandysizeTCavg) / 4 ) * 0.113473601)

The 0.113473601 is the multiplier introduced to standardize the calculation while the TCavg refers to the Time Charter average. Time Chartering is simply one of the ways to charter a tramp ship. Specifically, the charter will “book” the ship that best fits the size of the cargo for a specific period of time and for a pre–defined route. The hiring fees are expressed on a “per–ton” basis because they are structured on the amount of dead weight tons that are transported each month. It is important to point out that the transactional costs (bunker fuel and storage), on time chartering, are covered by the charterer itself. Consequentially, the TCavg is simply a quantification of the average cost for shipping raw materials on a established route on one the four dry bulk carriers.

The Baltic Dry Index is a very important leading indicator for worldwide business sentiment for several reasons:

1) It is difficult to modify because it is based on pure demand/supply changes which are, in turn, calculated on real orders

2) It is considered to be a leading indicator because orders are usually booked many months in advance (at least 2-3 months because of high intensity traffic in canals and potential port congestions. Think about the ship traffic concentration in the Panama and Suez canals)

3) It is a reliable index because business activities underlying the calculation are all legally certified, financed and paid upfront (none would hire a Panamax ship without having a paid order in place and none would place an order without actually needing raw materials and commodities)

4) It is a very good macroeconomic indicator, as far as shipping raw materials is concerned, because building a dry bulk carrier (whether it is a Handysize, a Supramax, a Panamax or a Capemax ship is irrelevant) takes many years. Therefore, their limited availability makes the tracking easier and more reliable because it means that the largest quantities of shipped raw materials have to necessarily be transported on one of those ships and, consequently, they are being accounted for in the calculation

The Baltic Dry Index is fairly straightforward to understand. In fact, higher or lower fluctuations simply imply a net increase or decrease in the demand for commodities and raw materials. Furthermore, the BDI is an efficient way to measure commodities’ demand because, given the fact that ships are limited and takes years to build, the amount of cargo that needs to be shipped will largely influence the oscillations of the index. The first chart of the present research displays the performance of the Baltic Dry Index since the 30th of June 2009 until the 27th of December 2013:

Baltic Dry Index

The above reported graph shows that the blue line (the actual BDI) has never fully recovered since the peak touched in November 2001 (4,661 points) while the lowest level ever touched was in February 2012 (647 points). It is evident that the 2011–2012 time interval has been rather flat in terms of shipping activity and demand for raw materials because both the quarterly and semi–annual trend lines oscillated laterally for many consecutive months. Nevertheless, the second half of the 2013 (from June onwards) showed an increased number of business activity but the recovery was far from being robust implying that the shipping of raw materials is still lower than it was before the credit crunch. The next chart will attempt to provide clue regarding the volatility of the index on different time perspectives:

Baltic Dry Index: Volatility Spectrum

First of all, it is important to mention that the Baltic Dry Index has a mean reverting volatility which tends to be confined within the 20%–40% thresholds. Also, the above reported graph suggests that the divergence between the volatility in the mid–term (red curve) and the volatility in the long–term (white curve) tends to be lower than the difference between short–term and medium–term volatilities. Volatility analysis is crucial in order to understand the fluctuations of the BDI which is why the next chart has been specifically created to show the volatility distribution of the volatility spectrum over the time period June 2009 – December 2013:

Baltic Dry Index: Volatility Distribution

The above reported chart displays the distribution of the volatility of the Baltic Dry Index in the short–term (ST VOL), medium–term (MT VOL) and long–term (LT VOL). The High, Mid–High, Medium, Mid–Low and Low sections correspond to the different volatility segments in the volatility spectrum. The sections will be now examined one at the time:

High: The ST volatility touched its highest point at 63.51% while MT and LT volatilities reached their respective tops at 62.41% and 54.20%

Mid–High: Mid–High volatilities are higher in the LT and MT than in the ST. In fact, mid–high volatility is 37.64% in the LT, equals 36.45% in the MT and it is approximately 35.05% in the ST

Medium: Medium volatility is higher in the long period than in the short one. Specifically, LT medium volatility is 32.25%, MT medium volatility is 30.36% and ST medium volatility is 27.12%

Mid–Low: Mid–Low volatilities are, even in this case, higher in the LT than in the ST. Specifically, LT mid–low volatility fluctuates around 25.51%, MT medium–low volatility is usually 24.31% while ST’s one is 19.19%

Low: LT volatility touched its lowest point at 13.83%, MT volatility minimum point was reached at 10.67% while in the ST the volatility has never got lower than 6.45%

The distribution analysis of the volatility spectrum has indeed provided very useful information. In fact, the calculation shows that mid–term and long–term volatilities, if we exclude the High segment, tend to systematically be more elevated than short–term volatility. Conversely, the highest spike in volatility was actually achieved in the short–term, although this volatility segment proved to be the least volatile of all. Why is that? What does this entail? The reason behind such phenomenon is the following: long and medium term volatilities are higher than the short term one as a consequence of the fact that the mean reverting pressure is lower in the LT and MT than in the ST. Specifically, such phenomenon implies that long–term and medium–term volatility explosions are more persistent and more ample, in terms of magnitude, and consequentially need more time to be “reabsorbed”. Besides, the fact that the highest volatility point ever reached by the Baltic Dry Index (63.51%) was actually achieved in the short–term implies that, in this segment, volatility explosions can be immediate and rather large although they tend to mean revert quickly. The strong persistency in medium and long term volatility explosions and the higher propensity to a quicker mean reverting process in the short–term volatility can be better understood by looking at the following serial correlation plot:

Baltic Dry Index: serial correlation

The chart shows 4 blocks. Each parallelepiped indicates the serial correlation amongst BDI data. The first on the left displays daily serial correlation, the second one from the left refers to weekly serial correlation, the third one from the left shows the monthly serial correlation while the last one refers to quarterly serial correlation. The interpretation of the above reported chart is fairly simple: the data from Baltic Dry Index tends to have a stronger bond in the very short term, a weak link on a weekly basis and they seem to have no relationship as far as monthly and quarterly data are concerned. The significant spread, between the first and the last two parallelepipeds, proves the point that short term volatility explosions tend to mean revert rather quickly and that actual data do not have any strong relationship in the long–term. The serial correlation plot also gives insights about the nature of the Baltic Dry Index itself: poor serial correlation in the long–term is a clear reflection of an ever–changing demand level for commodities and raw materials. The final chart highlights any inter–market relationship between the Baltic Dry Index and the most important asset classes in the world:

Baltic Dry Index: Correlation Matrix

The correlation matrix emphasizes the rapport with 3 markets in particular: Euro, American Treasury Bonds and German Bunds. The correlation between the Baltic Dry Index and Euro futures, over the 2009–2013 period, is without a doubt, the most robust (+0.47). The strong bond with the Single currency is predominantly due to the fact that approximately the 40% of the shipping transactional costs are due to bunker fuel. Bunker fuel (also known as fuel oil) is priced in US dollar but, since Euro is the largest currency component of the Dollar Index, every fluctuation in the European coinage will cause significant changes in the hiring fees charged to ship raw materials around the world. Consequently, the Baltic Dry Index, which accounts for the hiring fees charged for Handysize, Supramax, Panamax and Capemax ships, will be inevitably influenced by such variable. The remaining asset classes that display a good, although negative, correlation to the BDI are the 2 government debt world benchmarks: American T–Bonds and German Bunds. The reason American (-0.43) and German (-0.38) sovereign debt securities have an inverse link to the BDI is probably: hedging. In fact, the risk caused by taking positions on Baltic Dry Index futures and options contracts, traded through the Baltic Exchange, is usually counterbalanced via stable government bonds. The low correlation among the BDI and the so–called risky assets (DAX, WTI Crude Oil and Mini S&P500) is predominantly due to business cycles and commercial demand (commercials need to place their orders for raw materials 2–3 months ahead in order to account for production time, manage risk and ensure a continuous flow of commodity supply). On the other hand, the fact that Japanese Yen and Gold do not have a strong relationship with the BDI implies that fund managers, commercials and commodity traders prefer hedging their BDI market exposure using the aforementioned treasury markets.

HyperVolatility Researches related to the present one:

The US Dollar Index

Oil Fundamentals: Upstream, Midstream, Downstream & Geopolitics

Oil Fundamentals: Reserves and Import/Export Dynamics

Oil Fundamentals: Crude Oil Grades and Refining Process

The Oil Arbitrage: Brent vs WTI

The HyperVolatility Forecast Service enables you to receive statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial

HyperVolatility – End of the Year Report 2013

The HyperVolatility End of the Year Report 2013 has been completed.

The report has an interactive Table of Contents, therefore, you can simply click on the asset class you are interested in and jump straight to the analysis.

The first copy is read–only while the second file is a printer–friendly version of the research.

The HyperVolatility End of the Year Report 2013 can be downloaded FOR FREE at the following link (no registration required):

HyperVolatility End of the Year Report 2013

HyperVolatility End of the Year Report 2013 (PRINT)

The 1st part of the report examines the performances of the most important asset classes in the world (equities, currencies, bonds and commodities) in 2013.

The asset classes that have been object of our annual research are the following

Equity futures: DAX, E–Mini S&P500, FTSE/MIB

Treasury Bonds futures: German Bund, American Treasury Bonds

Currency futures: Euro, Japanese Yen

Commodity futures: WTI Crude Oil, Gold

Volatility Indices: VIX  

The 2nd part, instead, presents the macroeconomic scenario in USA, Europe and BRICS economies (Brazil, Russia, India, China and South Africa). The analysis focuses on important indicators such as GDP growth, inflation, Debt–to–GDP ratio, unemployment rate, inflation rate and credit rating.

A Chinese and an Italian version of the aforementioned research will be uploaded in the upcoming hours.

Oil Fundamentals: Upstream, Midstream, Downstream & Geopolitics

Crude oil is a scarce resource which means that at some point the existing oil wells will be exhausted. The current estimations, given the actual extraction and consumption rates, sustain that the black gold will be available for another 40 years but any increase in the demand would reduce the aforementioned projections. The USA has a Strategic Petroleum Reserve which has been specifically created in order to face shortages in the supply, however, rising oil prices and new technologies are pushing towards alternative source of energy.  Companies and businesses are considering potential substitute for crude oil and the alternative energy sources, that are increasingly becoming popular, are biofuels (like ethanol), hydrogen fuels, fuel cells, solar energy, nuclear power (even though nuclear power is not an environmentally friendly solution) and wind power.

Nevertheless, the demand for refined products is still very high and each oil derivative has its own market and its own price driver. A perfect example of divergence in price drivers for refined products comes for Europe. In the 90s many European governments guaranteed tax incentives to all the drivers who would have bought diesel–powered cars because diesel fuel emits less greenhouse gases than gasoline. Needless to say that such policy provoked a sharp augment in diesel prices but not in other oil derivatives.

Let’s now have a look at the oil industry as a whole.

First of all, it is worth mentioning that the oil industry is subdivided into 3 subsectors: upstream, midstream and downstream. The upstream involves the exploration and the extraction of crude oil, the midstream sector consists of transportation and storage while the downstream segment refers to the refining industry, marketing and distribution of refined products.

Upstream – The supply chain falls within the upstream segment. Here, the most important thing to determine is the capacity of the on–shore or off–shore site because this measurement identifies how big the oil well is and, consequently, the extraction rate. It is worth noting that major companies tend to retain a certain amount of unused capacity in order to face unexpected or sudden explosion in demand (usually caused by geopolitical issues).

Midstream – Once the extraction process is over, the oil “enters” the second segment: the midstream. This sector has to do, predominantly, with the transportation of the extracted petroleum liquids towards the refining centers. The transition can be processed using pipelines, trucks, barges or rail.

Downstream – Downstream operations are strongly connected with the refining industry because it is in this segment of the production chain that diesel, kerosene, jet fuel oil and all the other petroleum liquids get synthesized. Now, refining capacity is often closely related to demand for obvious reasons but not all refineries can deal with a broad range of crude oils so there are certain production boundaries. Nevertheless, the business is straightforward: refineries buy crude oil, they refine it and then sell the synthesized outputs. The income generated by refineries is measured with the so–called “crack spread” (there will be another study entirely focused on this product).

The cost of crude oil is not solely influenced by upstream, midstream and downstream operations. In fact, exogenous variables or unexpected events such as natural disasters, political turbulences and quality reduction of a specific oil well can push market players to increase their inventories. Consequentially, an augment in the short term demand and forward delivery would increase the cost of storage and, in turn, the cost of carry.

Amongst all of the exogenous factors that can alter oil prices, the geopolitical ones are certainly the most dangerous.

Conflicts and political instability in the Middle East have always had a remarkable impact on oil prices. Besides, African or Latin American countries, such as Nigeria and Venezuela, have often “hosted” violent riots that have increased the buying pressure on the oil market. Geopolitical issues create nervousness among market players and increase prices because internal riots, civil wars, unstable or corrupted governments could jeopardize the supply and limit the amount of oil available. Also, extreme forms of governments (fascism, communism, military controlled countries, etc) are not well seen by oil importing countries because dictators and/or non–democratically elected governments could threaten to limit the extraction or the export of oil.

The next chart provides a better clue on the relationship between geopolitical factors and oil prices:

Geopolitics - WTI Crude Oil Futures

The chart shows the fluctuations of WTI Crude Oil futures prices since July 1986 so far. The graph does not really need any comment because the arrows are self explanatory. Wars, civil wars, political turmoil, crises and cuts in the extraction rate have always added a significant pressure on crude prices which have been inevitably pushed higher. The only 3 big events, worth mentioning, that have depressed oil prices have been the Asian Economic Crisis in the mid 90s, the terroristic attack to the Twin Towers in September 2001and the Credit Crunch in 2008–2009.

Clearly, the Middle East is a vital geographical area for oil so any turbulence in this zone is strongly felt by market participants. Likewise, the other OPEC members do not always enjoy a great deal of political and civil stability (the OPEC members are  Algeria, Indonesia, Islamic Republic of Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, United Arab Emirates, Venezuela). The following chart shows the weight of each OPEC member in terms of number of daily extracted barrels:

Weights of OPEC members - June 2013

As previously mentioned, the chart displays the weight of each country expressed as a percentage of the total OPEC daily barrel production (the data are recent and they refer to the period January–June 2013). Saudi Arabia (29.68%), Iran (11.6%), Iraq (9.43%), Kuwait (9.17%) United Arab Emirates (8.78%) and Venezuela (8.63%) are the top 6 largest OPEC members. The fact that 5 out of 6 among the largest OPEC members are all located in the Middle East explains very clearly why this world region is so closely monitored by oil importing countries like United States, China, Japan, India and Germany.

If you are interested in trading oil or oil derivatives markets you might want to read the following HyperVolatility researches:

1) Oil Fundamentals: Reserves and Import/Export Dynamics

2) Oil Fundamentals: Crude Oil Grades and Refining Process

3) The Oil Arbitrage: Brent vs WTI

The HyperVolatility Forecast Service enables you to receive statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial.

The Volatility Smile

Option markets are multidimensional, in fact, options spreads can be created using different strikes, different maturities or different type of options (calls / puts). Besides, option traders cannot track instantaneous price changes on the entire option chain, hence, it is easy to derive that the most important factor in option trading is not the price of the option itself. The variable that has to be accurately tracked and monitored at all times is, in fact, what drives and determines the price of the option: volatility. In reality, there are different types of volatilities but the one extracted from option premiums is called implied volatility (the volatility extracted from futures prices is instead referred to as realized volatility) and its shapes and fluctuations are crucial to any market player involved in options trading. The present research will try to describe the dynamics of the implied volatility shape and to analyze its most common evolutions: Smile, Smirk and Forward Skew. In particular, the implied volatility figures used in the present examination have been extracted from front month WTI option premiums traded on the 30th of August 2013 which expire in October. All calculations and charts have been respectively performed and created with the HyperVolatility Option Toolbox. The following chart displays the so–called volatility smile:

Volatility Smile

(Source: HyperVolatility Option Toolbox)

As we can see from the above reported graph, the curve is higher at the extremes but rather low in the middle (bear in mind that the At–the–Money strike was $107.5). This is the typical shape for a front month implied volatility curve where the high demand for ITM calls and OTM puts as well as for ITM puts and OTM calls drives the volatility higher. In many cases, you will hear that the volatility for Out–of–the–Money options is higher but such statement is clearly incorrect because without further specification it implies that only OTM puts and calls experience such high volatility. The chart evidently shows that the volatility for ITM and OTM options is higher but, for obvious reasons, the volatility for the strike where a call option is In–the–Money will be almost as high as the volatility for the Out–of–the–Money put option and vice versa. Consequentially, saying that Away–from–the–Money options (both calls and puts) have a higher implied volatility than At–the–Money options is without a doubt the most correct statement. The most natural questions at this point would be: Why? What does a smile–shaped curve tell us?

The most obvious thing to say is that the volatility on AFTM options is higher because investors tend to trade them more often and they consequently push the volatility on the upside. The reason why investors buy wings is that ITM options have more intrinsic value than the ATM ones while OTM options have more extrinsic value than an option struck At–the–Money. Consequentially, the presence of an implied volatility smile–shaped curve is typical of more speculative markets. The smile suggests that, when large volatility shifts happen, many market players rush to buy OTM options for speculative reasons while ITM options are primarily purchased to stabilize portfolio gains. The next chart shows how the curve moves:

Volatility Smile - Dynamics

(Source: HyperVolatility Option Toolbox)

Many researches on implied volatility curve dynamics showed that the 75% of volatility changes can be defined by a total shift, up or down, of the entire curve as you can see from the chart. A further 15% of the movements consist of curve twisting (the right hand side of the curve goes deeper down while the left side gets pulled on the right or vice versa) while the remaining 10% is the product of a change in convexity (wings getting wider or tighter).

Smile–shaped curves are frequently found in equity index options, stock options and popular commodities / currencies (Euro, WTI, Gold, etc). Nevertheless, it is worth noting that the shape of the curve can even evolve over time. This concept can be better explained by looking at the following chart:

Volatility Smirk

(Source: HyperVolatility Option Toolbox)

The smirk is a particular volatility profile where ITM calls and OTM puts are priced with a much higher implied volatility. This phenomenon is commonly found in equity markets and risky assets. In this simulation the ATM strike is 116 and its volatility is 13.1% but OTM puts and ITM calls are much more expensive because they are trading above the 70% level. As previously mentioned, the implied volatility curve can change and evolve and a volatility smile can turn into a smirk if investors, traders and market players are expecting a market crash or if the plunge in price has already happened. Smirks are simply telling us that lower strikes are more traded than higher strikes and OTM puts as well as ITM calls are being heavily traded. If the market is heading south, a smile would easily evolve into a smirk because of the great buying pressure generated by market players rushing to buy OTM puts to protect their portfolios. The purchase of ITM calls, even during market crashes, makes sense because ITM call options have already an established intrinsic value and they have the highest probability to expire In–the–Money; in other words they are safer. However, in the event of market downtrends the evolution of a volatility smile into a smirk would predominantly be caused by the large buying volume on OTM puts.

The Volatility smile, nevertheless, can go through another metamorphosis whose final output is the so–called forward skew:

Volatility Forward Skew

(Source: HyperVolatility Option Toolbox)

The forward skew is nothing but a reversed form of smirk, in fact, the volatility here tends to become higher for ITM puts and OTM calls. This type of curve is more frequently found in commodity markets, particularly agricultural products, than equity indices or stock options. Even in this case the increase in volatility is provoked by an augment in demand for these options. However, the strong buying pressure concentrated on OTM calls is often the main cause of such shape. Let us break it down. Many players in commodity markets are commercials (mining and energy companies, grain / wheat / sugar / coffee producers) and therefore a disruption in the supply chain of a particular commodity can generate serious problems. A shortage in oil supply due to geopolitical variables, a disappointing crop due to a frost or to challenging meteorological conditions, continuous strikes in a particularly large mine are all factors that would force companies to buy as quickly as possible the commodity they need in order to lock in the order. Consequentially, the remarkable buying pressure on OTM calls would inevitably drive their price up and that is why the implied volatility of higher strikes is more elevated than others.

Let us now summarize the main concepts in order to avoid confusion:

1) An implied volatility smile means that Away–from–the–Money options have a higher implied volatility than At–the–Money options

2) Implied volatility smile–shaped curves are typical of highly speculative markets

3) Many researches on implied volatility curve dynamics showed that the 75% of all volatility changes consist of a shift, up or down, of the entire curve

4) Smile–shaped curves are frequently found in equity index options, stock options and the most popular commodities / currencies

5) The smirk is a particular volatility profile where ITM calls and OTM puts are priced with a much higher implied volatility

6) Volatility smile curves can turn into a smirk if investors, traders and market players are expecting a market crash or if the plunge in price has already happened

7) The forward skew is a reversed form of smirk, in fact, the volatility here tends to become higher for ITM puts and OTM calls

8) The forward skew curve is more frequently found in commodity markets (particularly in agricultural products)

9) The formation of a forward skew curve is often the consequence of a shortage in the supply chain due to transportation issues, geopolitical problems, adverse meteorological conditions, etc

The HyperVolatility Forecast Service enables you to receive statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial.

Oil Fundamentals: Reserves and Import/Export Dynamics

The present study belongs to the Oil Fundamentals project that the HyperVolatility team initiated a few weeks ago with the article “Oil Fundamentals: Crude Oil Grades and Refining Process”. Credit must be given to Liying Zhao (Options Engineer at HyperVolatility) for helping me to gather the necessary material.

This analysis will provide information regarding the demand and consumption of oil on a global scale and it will subsequently examine the import/export dynamics.

The first variables that will be observed are oil reserves. Oil reserves are those quantities of oil whose availability is documented by geo–physical and engineering studies of the oil–well under examination and whose extraction falls within the parameters indicated by current economic conditions (transactional and operational costs) and structural resources (equipment and technology at disposal). In other words, it is the oil whose presence has been proven and that can be extracted given the current level of transactional costs and machinery’s sophistication. According to recent researches, OPEC countries possess more than 70% of the world proven reserves but Venezuela and Saudi Arabia are the largest “containers” on the planet. There is a standardized and worldwide recognized way to look at reserves: the Reserves–to–Production Ratio. The R/P ratio is a fairly simple number which expresses, in terms of years, how long oil reserves for a specific country would last assuming that the current extraction rate would remain constant over the years. It goes without saying that the calculation for the R/P ratio is trivial because it is performed by simply dividing the oil reserves at the end of the year by the production for the year. The next chart displays the R/P ratio for all continents (the data have been provided by British Petroleum):

Oil Reserves to Production Ratio

The interpretation of the above reported graph is very straightforward: the numbers on the Y axis measures the years it would take to terminate all oil reserves starting from December 2012. For example, Europe and North America would take almost 22.3 and 38.6 years respectively to finish all reserves should the current production rate remains constant in the upcoming years. The African continent would employ 37.7 years, the Asia–Pacific region would need only 13.6 years while the Middle East has 78 years of proven reserves. On the other hand, the “best equipped” part of the world is constituted by Southern and Central American countries with almost 122 years of available oil. It is interesting to notice that the whole world, according to this study, would finish its reserves in the 2065. The reason petroleum liquids have been shrinking is obviously due to an ever increasing global demand which went from 32 – 33 million barrels per day at the beginning of the 70s to 83 – 84 millions in the 2011 – 2012 (the International Energy Agency forecasted that the global demand will increase to almost 92 million barrels per day in 2014). The largest oil consumers are without a doubt the countries with a high industrial development rate: USA and European countries. USA remains the largest single oil consuming country because it employs 25% of the total oil extracted on the planet but the current scenario is changing rapidly. In fact, China, Japan and India are now becoming key market players and their internal markets are heavily weighing on global demand and price levels. Let’s now focus on imports/exports dynamics.

Many countries both import and export large amounts of oil but there are some of them which consume more than what they can produce, so they have to import the rest, and others that use only a very small part of what they extract, so they can export more. The following chart shows the top 20 oil importing countries in 2012 (the data have been provided by CIA World FactBook):

Oil Import

It is evident that the United States are the largest single oil importing country in the world with more than 10 million barrels per day followed by China (5 millions), Japan (4.39 millions), India (3 millions) and Germany (2.67 millions). However, if we group together all the import figures for the European countries in the top 20 we see that the States (10 million b/d) import almost as much as Europe (13.8 million b/d). The chart highlights that many Asian developing countries, excluding China and India, are suddenly augmenting their demand and industrial productivity, in fact, South Korea, Singapore, Taiwan, Thailand and Indonesia import almost as much as the majority of Western European countries: 1.5 million barrels every day.

The next graph ranks the top 20 oil exporting countries (the data have been provided by CIA World FactBook):

Oil Export

The top 5 oil exporters in the world are Saudi Arabia (7.63 million b/d), Russia (5 millions), Iran (2.52 million b/d), Arab Emirates (2.39 million b/d) and Norway (2.18 million b/d). The chart clearly highlights the superiority of Middle East countries in the role of global oil suppliers (almost 16.8 million b/d). The only 2 outsiders are Russia and Norway: the former is the only serious competitor that Saudi Arabia has while the latter is ranked at the 5th place because of the Brent Blend oil whose wells are placed in the North Sea. It’s worth noting that the USA does not export its oil (apart from a small part of Alaskan oil) and this is precisely why the States ranks so low. The ranking, however, has lately changed because Russia used to be the biggest oil exporter in the world for many years but a recent change in the policy adopted by the OPEC has re–shaped the oil supply scenario.

So far we have looked at importers and exporters and we know who buys and who sell the most but at this point an obvious question arises: Who buys from Whom?

There are 3 blocks of large buyers: USA, Europe and Asia (Asia means China, India and Japan) and their suppliers are the following:

1) USA imports oil from South and Central America, Middle East and Canada

2) Europe imports oil from Russia, Middle East and North Africa (Libya, Angola, etc)

3) Asia imports oil from the Middle East, Africa and, to a lesser extent, from smaller Asian countries (South Korea, Singapore, etc)

The inter–market connections are high and they range from Europe to Middle East and from Africa to Asia. Nonetheless, the aforementioned list is important to understand why OPEC countries are so important: the oil supply market is literally dominated by OPEC members

The presents study terminates here but the HyperVolatility team invites you to read our previous researches entirely focused on oil and commodity markets:

“The Oil Arbitrage: Brent vs WTI”

“Commodities and Currencies: Inter – Market Analysis”

Oil Fundamentals: Crude Oil Grades and Refining Process

“The Pricing of Commodity Options”

“Commodity Volatility Indices: OVX and GVZ”

The HyperVolatility Forecast Service enables you to receive statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial.

Oil Fundamentals: Crude Oil Grades and Refining Process

First of all, I would like to give credit to Liying Zhao (Options Engineer at HyperVolatility) for helping me to conceptualize this article and to gather the necessary information to develop it. There will be other articles describing the physical side of the crude oil market so this is simply “the first gear of a more complex apparatus”.

The present analysis is not a quantitative research on the oil market and its aim is to list the most important aspects to consider before investing or trading the black gold. Consequently, the focus will primarily be on the petroleum physical market and on how the oil industry works. The HyperVolatility team spends a great deal of time analyzing and trading commodity markets, hence, crude oil positions have always had a considerable weight in our portfolio. Also, the great attention towards commodity markets generated by the credit crunch and the consistently high volume on crude oil futures and options are some of the reasons that convinced us to put together a general guideline for those who choose to venture into energy markets and in particular fossil fuels.

First of all, it is worth mentioning that there are almost 250 different types of crude oils in the world but the ones that are mentioned the most are primarily 2: the American West Texas Intermediate and the European Brent Blend (which is now the global benchmark).It is not unusual to hear financial journalists talking about other crude oils like the Nigerian Bonny, the Arab Light (Saudi) or the Dubai (UAE); nevertheless, the spotlight is almost exclusively on WTI and Brent. The reason these markets, particularly the Brent, have so much media coverage is due to their importance when pricing other crude oils worldwide. Again, the Brent is the nowadays global benchmark (although the WTI used to have this role) so every oil producer or buyer will have to know its price; the question is why?

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):

Grade of Crude Oil

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:

– 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

– 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) 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”

“The Pricing of Commodity Options”

Commodity Volatility Indices: OVX and GVZ

“Commodities and Currencies: Inter – Market Analysis”

The HyperVolatility Forecast Service enables you to receive statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial.

Commodities and Currencies: Inter-Market Analysis

Commodity markets are becoming increasingly important in today’s financial scenario. Many investors, traders and fund managers are now shifting their attention towards asset classes such as Crude Oil, Natural Gas, Gold, Silver, Wheat, Coffee or Sugar. Furthermore, the credit crunch brought a great deal of market instability and many equity indices, as well as single stocks, have been abandoned by or lost their appeal to many market players. There are several liquid commodities in the world but the present research will concentrate on, arguably, the heaviest in terms of volume: WTI Crude Oil, Gold and Henry Hub Natural Gas. It is important to point out that the European oil benchmark, the Brent Crude, has been deliberately omitted from the aforementioned list, although very liquid, but it will be the focus of another research. This research will present the results of an inter–market analysis where we compare and contrast the performances of above mentioned 3 commodities against a basket of currencies. The currency exchanges that have been selected are either very liquid (Euro, Japanese Yen, British Pound, Swiss Franc) or related to countries whose trade balances heavily rely on commodities and this is the case for South Africa, Canada, Australia and New Zealand (some market players can also refer to these exchanges as commodity currencies). The research has been conducted using a database consisting of daily data ranging from October 2011 until the 25th of June 2013. Consequently, our study aims to capture the medium–to–long term relationship that WTI Crude Oil, Gold and Henry Hub Natural Gas futures have with those exchanges. The first commodity that will be examined is the WTI Crude Oil:

WTI Crude

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:

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:

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)

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 Pricing of Commodity Options”

“Gold Market Performance in 2012”

“The Oil Arbitrage: Brent vs WTI”

“Trading Gold and Silver: A Realized Volatility Approach”

The HyperVolatility Forecast Service enables you to receive statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial.

Commodity Volatility Indices: OVX and GVZ

The present research will examine the most popular commodity volatility indices proposed by the CBOE: the OVX and the GVZ. It is important to point out that this study is the second component of a bigger research whose first part is entitled “Equity Volatility Indices: VIX, VXN, VXD, RVX” . The analysis will follow the structure presented in the first half and it is aimed to provide pivotal volatility levels. (The HyperVolatility Forecast Service provides market projections for the VIX, Gold, WTI crude oil and many other asset classes. Send an email to info@hypervolatility.com and get a free 14 days trial). Before getting started it is worth reminding that the OVX is a volatility index based on the performance of the options written on the USO (United States Oil Fund) while the GVZ is calculated on options written on the SPDR Gold Shares Trust. The next chart displays the ranking of both commodity volatility indices:

Commodity Volatilities: Distribution Ranking

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:

Volatility of Commodity Volatilities

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:

Volatility of Commodity Volatility: Distribution Ranking

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 the research “The Oil Arbitrage: Brent vs WTI” very helpful

The Pricing of Commodity Options

The present research will prove particularly useful to option traders. The analysis proposed by the HyperVolatility Team will explain, in a few bullet points, how the most popular commodity options pricing models behave and what the practical divergences in terms of prices are. The present study is very valuable to anyone interested in trading options because most trading platforms allow the trader to choose the model via which the theoretical value of the options will be calculated and consequently shown (the HyperVolatility Forecast Service provides market projections for many asset classes. Send an email to info@hypervolatility.com and get a free 14 days trial). The pricing models that will be analyzed are the Barone–Adesi –Whaley, the Bjerksund & Stensland (the 2002 version), the Black–76, the Binomial Tree and the classic Black–Scholes–Merton one. The models have been tested against each other and the following charts graphically show the divergence of 1 pricing model with respect to all others. The research has been performed assuming that the underlying asset (S) is a WTI crude oil futures contract, that the volatility (σ) is 20%, that the interest rate (r) is 0.5% and that the Cost of Carry is 0 (which is normal when dealing with commodity options).

As previously mentioned, the study will examine 1 pricing model at the time and, in order to avoid confusion and make things simpler, we decided to list the most important aspects below each graph:

Barone Adesi Whaley model

1) The Barone-Adesi-Whaley model overprices options when compared to other formulas. The pricing spread with respect to other models is on average between 0.06% and 0.08%

2) The Barone-Adesi-Whaley prices tend to get closer to other models as the expiration increases

3) The Barone-Adesi-Whaley model, on average, tends to overprice options with respect to the Binomial Tree (~ 0.16% higher) for short maturities. The trend is higher for out-of-the-money options and particularly for put options

4) The prices derived from the Bjerksund & Stensland model are always lower than Barone-Adesi-Whaley prices. The difference is bigger for 1 month options (~ 0.16%)

5) The Black-76 performs as well as the Black–Scholes–Merton model, however, their results overlap and that is why the Black-76 curve is not visible

6) The difference with the Black–Scholes–Merton model becomes larger as the expiration increases but it is not higher than 0.1%

 

Bjerksund & Stensland model

1) The Bjerksund & Stensland model under–prices options in respect to other models. On average the difference ranges between 0.05% – 0.06%

2) The under–pricing tends to reduce as the expiration increases

3) The Bjerksund & Stensland  model produces prices which are lower than the Barone–Adesi–Whaley one for any expiration

4) The Black–Scholes–Merton model approximates to the Bjerksund & Stensland one from the 8th month onwards

5) The Black–76 performed as well as the Black–Scholes–Merton model and that is why the overlapped curve cannot be seen  in the chart

6) The Binomial Tree approach shows the highest differential with respect to the Bjerksund & Stensland model. The divergence in pricing oscillates around 0.15%

 

Black-76 model

1) The Black–76 model over–prices options only with respect to the Bjerksund & Stensland one (almost 0.05%)

2) The divergence between Black–76 and Bjerksund & Stensland attenuates when longer expirations are approached

3) The Barone–Adesi–Whaley model prices are slightly higher than Black–76 ones and the discrepancy augments with the passage of time (between 0.08% and 0.1% for 10 months and 1 year expiring options respectively)

4) The Binomial Tree approach, if we exclude the short term, delivers higher prices than the Black–76 model but the divergence oscillates around the interval 0.03% – 0.04%

5) The Black–76 model performed as well as the Black–Scholes–Merton one and that is why the BSM curve is flat to 0. Needless to say that the Black–Scholes–Merton curve suggests that there is no difference in pricing

 

Binomial Tree model

1) The Binomial Tree under–prices options with respect to other models in the short term (around 2.5%) but the divergence is much lower for longer dated derivatives

2) The Barone–Adesi–Whaley model and the BSM model perform as well as the Black–76 one therefore their curves are hidden in the chart

3) The Bjerksund & Stensland model provided higher prices for short dated options but in the long term the Binomial Tree approach shows a slight over–pricing tendency with respect to the former. However, the spread is no higher than 0.04% – 0.05%

 

Black Scholes Merton model

1) The performances of the Black–Scholes–Merton formula with respect to other pricing models match perfectly well with the outcome generated by the Black–76 model

2) The above reported chart is identical to the graph extrapolated for the Black–76 model, in fact, the green curve does not move from the 0 axis

If you are interested in trading options you might want to read also the HyperVolatility researches entitled “Options Greeks: Delta, Gamma, Vega, Theta, Rho” and “Options Greeks: Vanna, Charm, Vomma, DvegaDtime”

The HyperVolatility Forecast Service enables you to receive the statistical analysis and projections for 3 asset classes of your choice on a weekly basis. Every member can select up to 3 markets from the following list: E-Mini S&P500 futures, WTI Crude Oil futures, Euro futures, VIX Index, Gold futures, DAX futures, Treasury Bond futures, German Bund futures, Japanese Yen futures and FTSE/MIB futures.

Send us an email at info@hypervolatility.com with the list of the 3 asset classes you would like to receive the projections for and we will guarantee you a 14 day trial.

Go back to top