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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

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

HyperVolatility – End of the Year Report 2012

Dear All, we are pleased to announce you that the HyperVolatility End of the Year Report 2012 has been finally completed and it can be downloaded for free by clicking the following link:

HYPERVOLATILITY END OF THE YEAR REPORT 2012

As always, our analysis focuses on the most important financial markets in the world in addition to a complete and accurate examination of the macroeconomic indicators over the 2012. Therefore, the first part of the study is focused on equity markets, currency , commodity and government bond futures. The HyperVolatility Team performed for each asset class calculations regarding price fluctuations, market volatility, inter-market analysis and price distribution. All quantitative studies are accompanied by a chart and an easy to understand explanation.

On the other hand, the second part of the HyperVolatility End of the Year Report 2012 is entirely dedicated to macroeconomic factors, their fluctuations and potential influence on financial markets and global economy. The macroeconomic study focuses on 1 indicator at the time and inspects its oscillations over the 2012 in Western European countries, USA, Japan, Australia and the top emerging markets in the world (Brazil, Russia, India, China)

Please read carefully our Legal Disclaimer. The HyperVolatility End of the Year 2012 table of contents is the following:

1) Legal Disclaimer; 2) Euro Futures; 3) Japanese Yen Futures; 4) WTI Crude Oil Futures; 5) Gold 100 Futures; 6) E- Mini S&P500 Futures; 7) DAX Futures; 8) FTSE/MIB Futures; 9) German Bund Futures; 10) Treasury Bond Futures; 11) VIX Index; 12) GDP Growth Rate; 13) Unemployment Rate; 14) Inflation Rate; 15) Debt to GDP Ratio; 16) Credit Rating; 16.A) Appendix

A more ink-saving version of the HyperVolatility Enf of the Year Report 2012 is available upon request. Send us an email at info@hypervolatility.com

We take this opportunity to remind you that market projections and statistical analyses of the aforementioned classes can be obtained on a weekly basis thanks to the HyperVolatility Forecast Service (We guarantee you a 14 day free trial)

Trading Gold & Silver: A Realized Volatility Approach

First of all, I’d like to thank Nick Pritzakis for editing and revising the article.

Now, gold and silver are amongst the most heavily traded commodities in the world. Not only that, interest towards precious metals has been growing at an exponential rate. Many investors, institutional and retail…use them as a way to diversify their portfolios. Of course, this is an attempt to reduce their exposure to the equity markets, as well as hedge against the potential fear of inflation.
In fact, many market participants rush to buy precious metals, particularly gold, during sharp retracements in the equity market, as well as, when we’ve had political instability and the threat of war.
It’s no secret that gold and silver are “safe havens”, the financial parachute that investors and traders use during a crash landing.
But are they really safe? Are they still a good investment or just another bubble waiting to pop? You see, rather than blindly accepting what journalists and financial advisors tell us, we’ve decided to investigate these markets further, by using a more scientific approach called quantitative analysis.
The chart below displays the volatility fluctuations in gold futures over the last 2 years (January 2010 to 18th of April 2012). As you can see, there are two volatility estimators: close-to-close and the Yang Zhang estimator (“YZ”). The close-to-close is the volatility obtained by modelling closing prices each day. The Yang Zhang is the volatility extracted using high, low, close and opening prices and then weighted for the overnight risk.

 

There is significant evidence that the close-to-close volatility (left hand axis) tends to be higher than the YZ volatility (right hand axis). At first glance, we can observe that the average volatility for the market is 18% (for the close-to-close) while it drops to 7.5% (for the YZ volatility). By the way, the VIX averages around the 13%.

So what are these numbers telling us? Can we draw a verdict? Well, the overnight risk is greater than the intra-day one. In other words, gold prices are likely to experience big jumps from one day to another… and then trade within a narrow range during the day (everything else being equal).
And actually, we saw this last summer when we had a big price spike in gold… while the equity markets were getting crushed. And believe it or not, the volatility rose as gold prices were increasing.
Wait…What? But isn’t volatility connected to market crashes? Doesn’t volatility mean only confusion and uncertainty?
The quick answer would be” yes” but the correct one is “it depends”.

Sure, volatility tends to explode during market crashes. And, this type of relationship is called asymmetric effect (or leverage effect), and it’s particularly strong in equity markets.
However, in currencies and commodities the dynamics are a lot more complicated. You see, there is a tendency for volatility to pop as prices go up. Now, at this point, it’s a typical feature, not only in the gold and silver market, but also in the Swiss Franc, Japanese Yen, T-Bonds, German Bunds and other government debt securities…just to name a few.
Here’s something else.
The chart suggests that the volatility in gold futures is mean reverting. Therefore, it will tend to collapse towards its long term average over time. This, of course implies that short volatility strategies can profit… if kept on long enough. On the other hand, long volatility strategies can potentially be profitable if entered when the close-to-close volatility touches the 10% level or when the YZ volatility is trading around the 4% threshold.
It’s important to note here… that volatility is dynamic. What’s worked in the past or is currently working now does not mean that it will continue to work. And as always, past performance is not indicative of future results.

Moving on. What about the Silver?

The chart shows some similarities with gold. For example, we did see an explosion in volatility last summer as silver prices were increasing. Now, if we analyze the difference amongst the close-to-close (left hand axis) and the YZ (right hand axis) volatility, we’ll find a pattern which we saw earlier from the gold market. Once again, the overnight risk is greater than the intra-day moves.

As you may know, the silver market is extremely volatile… a lot more then the gold market. In fact, the average close-to-close volatility is around 40% (left hand axis) while the YZ volatility fluctuates around the 17% level.

And actually, like gold, silver volatility tends to be mean reverting.

Also, last summer, the close-to-close volatility touched 85% …in July 2011 and November 2011 it touched 100%. Of course, long volatility strategies would have been pretty sweet had you put them on before these big moves.

Finally, we’ve looked at both markets individually; it’s time to look at them together.

Closing Thoughts:

1) The silver market is twice as volatile as the gold market.

2) Overnight risk is big, the majority of the large movements occur overnight…not intraday.

3) The volatility is mean reverting in both markets and it follows a symmetric effect (it increases with buying pressure)

4) The volatility in gold is smoother.

Strategy Analysis: For Option Traders


Now, these are not trading recommendations, but a basic guide under the present volatility regime. Remember, volatility is dynamic and past results are not indicative of future results.

1) Long straddles or strangles are favourable when the realized volatility is around 20% for the silver and 10% for gold

2) Iron condors and butterflies positions are favorable when the realized volatility achieves the 35% – 40% for silver and 15% – 20% for gold.

3) Long volatility strategies are favorable when kept for a short period of time.

4) Short volatility strategies may take up to a month and a half to show consistent returns.

5) Call options tend to benefit from a one-two punch. When the futures price rises, implied volatility tends to rise with it.

In this report we tried to provide a quantitative approach to trading gold and silver using realized volatility data. Of course, there are many ways you can trade them and other factors to consider.

 

 

 

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:

HYPERVOLATILITY END OF THE YEAR REPORT 2011

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

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