HyperVolatility – End of the Year Report 2014

The HyperVolatility End of the Year Report 2014 is finally ready and this year we have added more asset classes.

You can browse the report using the interactive Table of Contents which allows you to jump straight to the analysis you want to read.

The HyperVolatility End of the Year Report 2014 can be downloaded FOR FREE at the following link (no registration required):

HyperVolatility End of the Year Report 2014

HyperVolatility End of the Year Report 2014 (PRINT)

The 1st part examines the performances of the following asset classes throughout the 2014:

Equity Indices: Mini S&P500, DAX 30

Treasury Bonds: German Bund, US 10–year Treasury Bonds

Currencies: Euro, Japanese Yen, British Pound Sterling

Commodities: WTI Crude, Brent Crude, Gold

Volatility Indices: VIX Index

The 2nd part analyzes the macroeconomic scenario in USA, Europe, Australia, Japan and BRICS economies (Brazil, Russia, India, China and South Africa). The economic indicators that have been considered for the study are the following:

GDP Growth Rate

Unemployment Rate

Inflation Rate

Debt–to–GDP Ratio

Credit Rating

Please, email all your questions at

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 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 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 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 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 Oil Arbitrage: Brent vs WTI

It is no secret that the most important crude oils in the world are the European Brent (extracted by 15 oil fields located in the East Shetland Basin in the North Sea) and the American WTI which is extracted in the US and delivered at the Cushing in Oklahoma. It is well known that the Brent Crude oil has become the global benchmark and it is used to price crude oils worldwide. Although they are extracted in geographically distant locations the chemical composition of WTI and Brent is not exceptionally different because both of them are considered to be “sweet oils” which means that both contain a low concentration of sulphur: 0.37% for the Brent and 0.24% for the WTI. This small introduction is necessary to understand that the supply/demand forces driving price fluctuations are dissimilar and the discrepancy is even clearer if we add that the Brent is exported in the whole Europe and worldwide while the WTI does not leave the US.

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:

Brent and WTI futures

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:

Brent / WTI spread

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:

Brent / WTI correlation

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:

Brent / WTI spread price distribution

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:

Brent / WTI correlation distribution

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

In our HyperVolatility Forecast Service we dig deeper through news and calculations. We provide financial forecasts based on volatility analysis and statistics that you will not find in a retail trading platform. 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 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 2011

Dear All, we are pleased to announce you that the HyperVolatility End of the Year Report 2011 has been finally completed and it can be downloaded for free by clicking the following link:


In the first part, the study is focused on equity markets, currency and commodity futures. Each analysis is divided into 2 parts: in the first one we go through the overall performance of the particular asset under examination whilst in the second one we focus on intraday and close-to-close volatilities which have been calculated using the TGARCH model.

The second part is entirely centred on the macroeconomic factors and their influence on financial markets. We try to pull together the big picture by singularly studying the most important exogenous variables which affected financial prices over the 2011. This examination has been carried on the major economies in the world in order to keep an eye on the global status of the economy.

Please read carefully our Legal Disclaimer. To give you an idea of what you can expect we report here the table of contents:

1) Legal Disclaimer; 2) Euro Futures; 3) British Pound Futures; 4) Swiss Franc Futures; 5) Japanese Yen Futures; 6) (WTI) E-Mini Crude Oil Futures; 7) Gold 100 Futures; 8) Yen, Australian Dollars and Commodities; 9) DJ EuroStoxx50 Futures; 10) E- Mini S&P500 Futures; 11) German Bund Futures; 12) Correlation Matrix Analysis; 13) Correlation Matrix Appendix; 14) Unemployment Rate; 15)Inflation Rate; 16) Gross Domestic Product; 17) Debt to GDP; 18) BRIC Economies: a brief summary; 19) Macroeconomic Data

Go back to top