The January Barometer

The January Barometer, sometimes also referred to as January effect (although the January effect is a different thing), is the theory according to which market performances during the month of January can be used to predict the trend for the rest of the year. The January Barometer theory is often summarized by the saying: “As goes January, so goes the year”. The practical implications are rather straightforward: if the asset class under examination has a positive return in January, the theory suggests that it will be a bullish year while a negative performance should predict a bearish trend for the upcoming months. The January Barometer theory is based on the assumption that many fund managers and institutional investors, particularly those who are interested in medium–to–long term investments, at the beginning of each new year, will tend to place their positions already discounting their view on the next 6 to 12 months. Consequentially, the theory suggests that, should the view be negative, portfolio managers will position themselves on the short side of the market at the beginning of January. Otherwise, should they think the price action will go up in the next 6 to 12 months, they will go long. Hence, the theory is based on the fact that the buying or selling pressure generated by the amount of money allocated in the market by big players in January should move the price in the direction of their forecasts. This is the theory but as Albert Einstein stated: “In theory, theory and practice are the same. In practice, they are not”. The present research aims to investigate and study the reliability of the January Barometer theory in order to assess, under a probabilistic point of view, what are the chances to actually earn consistent profits if applied to financial markets. The research has been carried out on major equity indices in the world that have been, in turn, subdivided for geographical location: North America (S&P500, NASDAQ Composite), Europe (DAX30, FTSE100), AustralAsia (Nikkei225, ASX200) and Emerging Markets (Hang Seng, Bovespa, BSE Sensex). The dataset that has been used consists of index prices ranging from January 2004 until December 2014 implying 11 years worth of data. The back testing analyses that will be exposed and commented are simply aimed to understand if January’s market returns matched, if not in size at least in sign, with yearly ones. Hence, the combination negative return in January/negative yearly return as well as the combination positive return in January/positive yearly return will all be calculated as success cases. On the other hand, all non–matching returns will be treated as a failure of the theory. The results of the analyses will be presented and explained by geographical location, hence, the first equity indices that will be analyzed are the North American ones:

The above reported table shows, in the first column, January’s lognormal returns calculated for the S&P500 while the second one lists the total return for each year. The right hand side of the table provides the same calculations but based on the NASDAQ Composite index. First of all, it is worth noting that, over the last 11 years, the average return for the S&P500 index has been +4.89% while the NASDAQ Composite yielded an average of +6.78%. Secondly, both equity indices have experienced similar fluctuations during the crises but the NASDAQ has consistently outperformed the S&P500 since the 2012 so far. The following chart plots the success/failure rates of the January Barometer theory applied to the aforementioned asset classes over the last 11 years:

The success rate for both S&P500 and NASDAQ Composite is 54.5% while the failure rate is 45.5%. These numbers simply mean that the JB theory held true for 6 years but it unsuccessfully predicted yearly returns in 5 occasions. Nevertheless, the 54.5% success rate definitely does not fall within the category “reliable strategies” but different results could potentially come from different markets. The next table presents the results of our analyses on the German DAX30 and the British FTSE100:

The tables evidently show that during the financial crises in 2008, the selected European markets performed as poorly as their American counterparties (although the British FTSE100 did not violate the –40% threshold). The last 3 years have gone quite well as far as returns are concerned although in the 2014 the British FTSE100 (–2.62%) underperformed the DAX30 (+3.06%). On average, the German index, over the last 11 years, has yielded a +7.26% return while long term investors in the British market managed to gain only +2.34%. The next graph plots the success/failure rates of the January Barometer theory applied on the aforementioned European asset classes:

The results are very different from the ones observed for the overseas equity indices. First of all, the JB theory applied to the DAX30 has higher failures (54.5%) than successes (45.5%). In particular, the January Barometer strategy has successfully predicted future returns 5 times but it has failed 6 times implying that even in this market it led to poor results. On the other hand, the JB applied to the British FTSE100 shows a 63.6% winning chance while the losing probability is only 36.4%. In this case, the returns registered in the first month of the year since 2004 proved to be good forecasters for yearly returns. Numerically, the JB strategy would have been profitable in 7 cases while it would have failed only in 4 occasions. The next table lists the historical returns for the AustralAsian region:

The equity indices that have been used as a proxy for the AustralAsian geographic region are the Japanese Nikkei225 and the Australian ASX200. The 2008 was a very negative year also in AustrlAsia, in fact, both yearly returns are very close to the –50% threshold. However, also the European credit crisis in 2011 has dramatically influenced the aforementioned indices: –20.58% for the Nikkei225 and –13.16% for the ASX200. The data for the average returns, calculated over the last 11 years, show that the Nikkei225 yielded a +2.89% while the Australian equity index returned an average of +3.57% to potential long term investors. Did the January Barometer strategy provide any extra gains? The next chart will attempt to answer this question:

The Japanese index displays the usual 54.5% / 45.5% split between success and failure rates implying that the JB strategy proved profitable only 6 times and failed in 5 cases. Conversely, the success rate for the ASX200 is 63.6% (7 successes) while the failure rate is only 36.4% (4 failures) implying that the JB strategy was definitely more profitable when applied to the Australian index than to its Japanese equivalent. The last table of the present research focuses on the past performances of a geo–economic, rather than merely geographic, area: emerging markets.

The equity indices that have been selected for this section are the Hang Seng (Hong Kong), the Bovespa (Brazil) and the BSE Sensex (India). The first thing to notice is that during the crisis the losses incurred by these markets have been larger than the ones observed for the indices we have mentioned so far. In fact during the credit crunch, with the exclusion of the Brazilian Bovespa that in 2008 had a negative return of “only” 46.25%, the Hang Seng yielded a –55.43% while the Indian return for the same year was a remarkable –70.64%. However, the scenario considerably changes if the 2011–2014 time interval is taken into account. In fact, the Indian equity index, even including the drop in the 2011, has consistently outperformed the other two. As far as long term average returns are concerned, since the 2004 so far, the Hong Kong’s Hang Seng yielded a +5.43%, the Brazilian Bovespa returned a +5.81% while the Indian BSE Sensex averaged an impressive +13.68%. Emerging markets average returns are clearly very high but the BSE managed to outperform all other equity indices considered in the present research (the German DAX30 ranks second with a +7.26% and it is immediately followed by the NASDAQ Composite index that yielded an average return of +6.78%). The next chart plots the success/failure rates obtained by running the January Barometer strategy on the Hang Seng, Bovespa and BSE Sensex:

The success rates for the BSE Sensex (54.5%) and Bovespa (45.5%) do not really seem to provide a hedge (although the strategy proved more profitable on the Indian equity index than on the Brazilian one). The most significant information that can be extracted from this analysis is the high failure rate on the Hong Kong’s index (63.6%). In particular, the January Barometer strategy has consistently failed to predict yearly returns on the Hang Seng index for 7 years while it proved successful only in 4 cases.

All in all, the analyses just conducted on the 9 equity indices considered in the study seems to point out that the JB strategy does not to provide any particular hedge for investors. Nevertheless while wrangling data some interesting patterns have emerged. The patterns that have been detected can be grouped in two categories: the winning pattern and the losing pattern.

Winning Pattern

The January Barometer strategy can only yield two mutually exclusive outcomes: it is either profitable or not (there is a chance that the yearly return could eventually be 0% but the associated probability, over 252 trading days, is so low that we have voluntarily excluded it from the scenario analysis). The winning pattern has been extracted by simply running a frequency analysis on the times the JB strategy proved to be profitable. The JB strategy basically states that January’s return should match the yearly one, however, it makes no difference between negative or positive returns. Consequentially, as stated at the beginning of the research, a positive January’s return in a positive year would be counted as a success but a negative January’s return in a negative year would be also counted as a success. The most important thing for the strategy to work is a match between returns. The questions this section is trying to give an answer to are: is there a pattern among success cases? Do success cases have anything in common? Do bullish success cases outnumber bearish ones or vice versa? Before showing the results, it is worth reminding that 11 years worth of data on 9 different asset classes have been filtered implying a total of 99 observations. 52 out of 99 observations fall into the winning pattern category while the remaining 47 are axiomatically assigned into the losing pattern one. The following pie chart attempts to summarize the results obtained from data mining the data associated to the first pattern:

The success cases, for the combination January’s positive return in a positive yielding year (green area called BULL), are 34 while the success cases, for the combination January’s negative return in a bearish year (red area called BEAR), are only 18. Clearly, the outcome of this frequency analysis is a consequence of the overall trend in each year but it seems that the JB strategy, when profitable, works best in positive performing years rather than in negative ones (winning pattern). In order to understand why this is the cases we have to proceed and mine the data for failure cases.

Losing Pattern

In the previous section it has been observed that, numerically speaking, the majority of success cases happened during the combination January’s positive return in a positive yielding year. It has also been stated that the JB strategy “works best in positive performing years rather than in negative ones”. However, in order to understand why this is case, the present section will analyze all failure cases. If the JB strategy did not work, it axiomatically implies that January’s return did not match the sign of the yearly one. Consequentially, there are only two scenarios to consider when analyzing failure cases: the return in January was positive but the yearly one turned out to be negative or the year started by yielding a negative return in January but it ended with a positive performance. The next pie chart plots all the failure cases and groups them into two categories: BEAR TO BULL (negative return in January but positive on a yearly basis) and BULL TO BEAR (positive return in January but negative on a yearly basis):

The above reported chart is evidently displaying that, among failures, there is a very frequent pattern: the vast majority of failures in the JB strategy are due to years starting out with a negative return but that subsequently yield a positive one (losing pattern). Numerically speaking, the BEAR TO BULL cases counted 39 observations while the BULL TO BEAR ones are only 8.

Conclusion

• The January Barometer strategy does not seem to provide consistent profits at least for the considered asset classes and the selected time frame
• The January Barometer strategy successfully predicted yearly returns in 52 cases (52.5% of the total observations)
• 34 of the 52 success cases come from the combination January’s positive return in a positive yielding year while only 18 come from the combination January’s negative return in a negative yielding year
• The January Barometer strategy failed to predict yearly returns in 47 cases (47.5% of the total observations)
• 39 of the 47 failure cases fall within the BEAR TO BULL category where January yielded a negative return but the year ended up with a positive performance
• Only 8 failure cases fall within the BULL TO BEAR category where January yielded a positive return but the year ended up with a negative performance

The present research can be expanded in many ways. In fact, potential research developments could come from increasing the datasets in order to allow 20 or 30 years worth of observations, from including more equity indices or by expanding the analysis to different types of asset classes such as treasury bonds, commodities and currencies.

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.

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.

The VIX Index: step by step

The VIX Index has been introduced by the CBOE in January 1993 and since then it has become the most popular and well known volatility index in the world. The VIX is an index extracted from S&P500 options and its calculation has been changed in September 2003 in order to obtain observations not linked or dependant from any model idiosyncrasy. The present study, inspired by the paper written and compiled by the CBOE, will show how to calculate the VIX Index in a step–by–step fashion. There will be ample explanations about how the index works and we will break down the formula in order to provide a better understanding of the function and weight of every component. Let’s start with the general formula for the VIX Index:

$\dpi{120}&space;\bg_white&space;\large&space;\sigma&space;^2&space;=&space;\frac{2}{T}\sum_\imath&space;\frac{\Delta&space;K_\imath}{K_\imath&space;^2}&space;e^&space;R^T&space;Q&space;(K_\imath&space;)-&space;\frac{1}{T}&space;\left&space;[&space;\frac{F}{K_0}-1&space;\right&space;]^2$                                     (1)

Where

$\dpi{120}&space;\bg_white&space;\large&space;\sigma$ = $\dpi{120}&space;\bg_white&space;\frac{VIX}{100}\Rightarrow&space;VIX&space;=&space;\sigma&space;x&space;100$

T = Time to expiration

F = Forward Index price (the ATM strike price)

K0=  First strike price below the Forward price F

Ki= Strike of the ith out–of–the–money option. A call option strike will be considered if Ki>K0 while a put option strike will be used if Ki=K0

ΔKi = it is the midpoint between the strike prices. Specifically, the strikes used will be on both the sides of Ki. In other words, ΔK for the lowest strike is just the difference between the lowest strike and the penultimate strike of the option chain while ΔK for the highest strike is equivalent to the difference between the highest strike and the strike immediately below the top one. The formula is the following $\dpi{120}&space;\bg_white&space;\Delta&space;K_\imath&space;=&space;\frac{K_\imath_+_1&space;-&space;K_\imath_-_1}{2}$

R = Risk free interest rate (the interest rate used is usually the Treasury Bond yield that more closely matches the option expiration date)

Q (Ki) = The midpoint of the spread between bid price and ask price for each option with a strike Ki

This is the formula that it is currently employed to calculate the most famous risk management index in the world. Before we provide any real example it is necessary to clarify some points. First of all, the VIX index is calculated using 2 expirations: the front month and the second front month contracts. In our study we will be using S&P500 option prices recorded on the 10th of October 2013 and therefore the front month options will be those expiring on the 18th of October 2013 (which will provide us with information about the volatility in the near term) while the second front month options will expire on the 15th of November 2013 and they will provide us with information about the volatility in the medium term. It is important to point out that near term options must have at least 7 days to expiration and when this criteria is no longer met the model automatically rolls to the next available contract. Let’s give an example, our front month options expire on the 18th of October while the medium term options will expire on the 15th of November, hence, on Friday the 12th the VIX will mechanically roll. At this point, the front month options used will be the ones expiring on the 15th of November while the second front month options employed in the calculation will become those expiring in December. This measure has to be adopted in order to avoid mispricing issues that commonly happen when options are about to expire. Another important detail to mention is that T (time to expiration) is calculated in minutes using calendar days, consequently, the time to expiration will be given by

$\dpi{120}&space;\bg_white&space;T&space;=&space;\frac&space;{M_c_u_r_r_e_n_t_&space;\hspace&space;{1&space;mm}_d_a_y&space;+&space;M&space;_s_e_t_t_l_e_m_e_n_t&space;\hspace&space;{1&space;mm}_d_a_y&space;+&space;M_o_t_h_e_r&space;\hspace&space;{1&space;mm}_d_a_y_s&space;}{Minutes\hspace&space;{1&space;mm}in&space;\hspace&space;{1&space;mm}a\hspace&space;{1&space;mm}&space;year}$                                  (2)

M current day= minutes remaining until midnight of the same day

M settlement day= minutes remaining from midnight until 08:30 am on SPX settlement day

M other days= total minutes in the days between current day and settlement day

If we assume that the data have been recorded exactly at 08:30 am on the 10th of October and that options will expire at 08:30 am of the 18th of October:

$\dpi{120}&space;\bg_white&space;T_1&space;=\frac&space;{930+510+1,920}{525,600}&space;=&space;0.0246575$

$\dpi{120}&space;\bg_white&space;T_2&space;=\frac&space;{930+510+3,540}{525,600}&space;=&space;0.1013699$

Needless to say that T1 and T2 refer to near term and medium term options respectively. The yield for 1 month Treasury Bills as of the 9th of October 2013 was 0.27% and therefore, given that our October options expire in 9 days while the November options will expire in 37 days, we will use this figure for both near and medium terms.

We now need to determine the F price of the index and in order to do so we take the strike price at which puts and calls have the smallest difference in absolute terms. The below reported table displays call and put prices with their absolute differential:

The red back–grounded figures are the strike prices at which calls and puts have the lowest difference, however, the two expiration dates have 2 different strike prices: ATM for October options is at 1,650 while ATM for November options is 1,655. In order to simplify calculations, and given the fact that the difference is extremely small, we will take 1,650 as F price because it is the front month contract. We can calculate F using the following formula:

$\dpi{120}&space;\bg_white&space;F&space;=&space;Strike&space;\hspace&space;{1&space;mm}Price&space;+&space;e^R^T&space;x&space;(&space;Call&space;\hspace&space;{1&space;mm}Price&space;-&space;Put&space;\hspace&space;{1&space;mm}&space;Price)$                                 (3)

Obviously, there will be two forward index values, F1 and F2 , the first for near term and the second one for medium term options respectively:

$\dpi{120}&space;\bg_white&space;F_1:&space;1,650+&space;e^(^0^.^0^0^2^7&space;\hspace&space;{1mm}^x&space;\hspace&space;{1mm}^0^.^0^2^4^6^5^7^5^)x&space;(23.75&space;-&space;22.7)&space;=&space;1,651.05$

$\dpi{120}&space;\bg_white&space;F_2:&space;1,650+&space;e^(^0^.^0^0^2^7&space;\hspace&space;{1mm}^x&space;\hspace&space;{1mm}^0^.^1^0^1^3^6^9^9^)x&space;(39.75&space;-&space;37.3)&space;=&space;1,652.45$

Now we have both  F1 and F2 , so we can identify K0 which is the strike price immediately below the forward prices. In our case, the closest strike right below F1 and F2 is 1,650. Consequentially, K0,1= 1,650 and K0,2 = 1,650.

The next step is to select all put options whose strike is lower than K0 and all call options whose strike is higher than K0. The selection excludes every option with a bid equal to 0 and it terminates when there are 2 consecutive strike prices that equal zero:

The above reported table explains very well what stated before. In the month of October the lowest puts are at 790 and 795 but the two red back–grounded options cannot be accepted in our calculation because there are two consecutive 0 in their bid prices and therefore they are our stopping point. In other words, no option with a lower than 800 strike price will be considered into the calculation of the VIX. The same principle applies to calls and in fact 2,095 and 2,100 are the highest call option strike prices that will be considered. The same procedure will be applied to November options. It is important to point out that the option chains will rarely have the same amount of strikes available because according to volatility fluctuations the number of strikes that will be priced by market makers will vary. It goes without saying that high volatility explosions will obligate market makers to price even Far–Away–From–The–Money options on both sides because the demand for these instruments will rise. Consequentially, the number of strikes that will be used for the purpose of calculating the VIX will vary according to volatility fluctuations and swings in S&P500 futures. The following table lists the options that will be used for calculating the volatility index:

The ATM strike is highlighted in blue. The put options in the near term contract that will be used start from strike 800 until strike 1,645 while the call options range from strike 1,655 until strike 2,100. The medium term strikes, instead, goes from 985 until 1,645 for put options and from strike 1,655 until strike 2,100 for calls. We now calculate the specific weight that every single strike will have in the calculation by using the following formula and we will take the 800 put as an example:

$\dpi{120}&space;\bg_white&space;\frac{\Delta&space;K_8_0_0&space;\hspace&space;{1&space;mm}Put}&space;{K^2_8_0_0&space;\hspace&space;{1&space;mm}Put}&space;e&space;^R^T^1&space;Q(800&space;Put)$                        (4)

Please bear in mind that Q is the midpoint between bid and ask while ΔK is the difference between the last option’s strike and the closest next strike, hence, in our case ΔK is (805 – 800) = 5. Let’s proceed with the calculation:

$\dpi{120}&space;\bg_white&space;\frac{5}{(800)^2}\hspace&space;{2&space;mm}2.71828&space;^(^0^.^0^0^2^7&space;\hspace&space;{1&space;mm}^x&space;\hspace&space;{1&space;mm}&space;^0^.^0^2^4^6^5^7^5^)&space;(0.05)=&space;0.000000390651$

The next table summarizes the contribution of each option strike to the overall computation of the VIX Index:

Now, the weights need to be added up and multiplied by 2/T1 for the near term and by 2/T2for the medium term and by performing this calculation we would have 0.074319519775 for the near term and 0.041553088 for the medium term.

The equation (1) that we presented at the beginning of this study is almost completed, in fact, the final part is the only one yet to be estimated. Let’s proceed:

$\dpi{120}&space;\bg_white&space;\frac{1}{T_1}&space;\left&space;[&space;\frac{F_1}{K_0}-1&space;\right&space;]^2&space;=&space;\frac&space;{1}{0.0246575}&space;\left&space;[\frac&space;{1,651.05}{1,650}-1&space;\right&space;]^2&space;=&space;0.000016423$

$\dpi{120}&space;\bg_white&space;\frac{1}{T_2}&space;\left&space;[&space;\frac{F_2}{K_0}-1&space;\right&space;]^2&space;=&space;\frac&space;{1}{0.1013699}&space;\left&space;[\frac&space;{1,652.45}{1,650}-1&space;\right&space;]^2&space;=&space;0.000021750$

We can now complete the calculation by subtracting the two members of equation (1):

Near Term σ21 : 0.074319519775 – 0.000016423 = 0.074303096

Medium Term σ22 : 0.041553088 – 0.000021750 = 0.041531338

The final VIX computation is given by the following formula:

$\dpi{120}&space;\bg_white&space;VIX&space;=&space;100&space;\hspace&space;{1&space;mm}&space;x&space;\hspace&space;{1&space;mm}\sqrt\left&space;\left&space;\{&space;T_1&space;\sigma_1^2&space;\left&space;[&space;\frac{N_T_2-N_3_0}{N_T_2&space;-&space;N_T_1}\right]&space;-&space;T_2\sigma&space;_2^2&space;\left&space;[&space;\frac{N_3_0&space;-&space;N_T_1}{N_T_2&space;-&space;N_T_1}&space;\right&space;]\right&space;\}&space;x&space;\frac&space;{N_3_6_5}{N_3_0}$

The second term of the equation is nothing but the computation of the square root of the 30 day average of σ21 and σ22which are subsequently multiplied by the first term 100.  The weights of σ21 and σ22 are less than 1 or almost equal to 1 when the near term options have less 30 days and the medium term options have more than 30 days to expiration. However, it is worth noting that when the VIX rolls both near and medium term options have more than 30 days to expiration. Let’s now conclude the VIX estimation:

NT1= minutes left before the settlement of near – term options

NT2= minutes left before the settlement of medium – term options

N30= number of minutes in 30 days

N365=  number of minutes in a year composed by 365 days

If we plug in the numbers we have the following outcome:

$\dpi{120}&space;\bg_white&space;\sqrt\left&space;\{&space;0.0246575&space;\hspace&space;{1&space;mm}x&space;\hspace&space;{1&space;mm}0.074303096&space;\hspace&space;{1&space;mm}&space;x&space;\left&space;[&space;\frac{53,280&space;-&space;43,200}{53,280&space;-&space;12,960}\right]&space;+&space;0.1013699&space;\hspace&space;{1&space;mm}&space;x&space;\hspace&space;{1&space;mm}&space;0.041531338&space;\hspace&space;{1&space;mm}x&space;\hspace&space;{1&space;mm}\left&space;[&space;\frac{43,200&space;-&space;12,960}{53,280&space;-&space;12,960}&space;\right&space;]\right&space;\}&space;\hspace&space;{1&space;mm}&space;x&space;\hspace&space;{1&space;mm}&space;\frac&space;{525,600}{43,200}$

This leads to the very last step: VIX = 100 x 0.209736087 = 20.9%

Now, the number we came out with is fairly accurate but it is limited to the moment in which the option prices have been “frozen”. Consequentially, the aforementioned procedure and calculation, in order to be precise, has to be automated and repeated every instant because any changes in option premiums will inevitable affect the final assessment of the VIX.

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:

(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: (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: (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: (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. Equity Volatility Indices: VIX, VXN, VXD, RVX The present research will go through the most popular equity volatility indices proposed by the CBOE. The aim of the research is to identify how they fluctuate and to quantify the magnitude of their movements. The actual study will be followed by a second one entirely focused on commodity volatilities and the examined asset classes will be Gold and WTI Crude Oil (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). The great attention towards volatility indices and the remarkable popularity gained by VIX futures are surely undeniable but what is the risk involved in these markets? What is the volatility index that best fits your trading style and portfolio needs? Do you know how the VIX performs compared to other volatility indices? The HyperVolatility team trades and analyzes volatility movements on a daily basis on different asset classes and the present study is a summary of some of our findings. The examined data sample goes from January 2011 until April 2013 and the present research will examine 4 equity volatility indices: the VXN (Nasdaq 100), the VXD (Dow Jones), the RVX (Russell Index) and the VIX (S&P500). The first chart displays the distribution ranking of each index and it is a good approximation of how volatile the aforementioned indices can be: The graph clearly indicates that, on average, almost all indices move within the 18% – 22% interval (the RVX is the only one above this range as its median is 23.43%). The lowest fluctuations, for all indices, are concentrated within the 9% – 12% range (the VXD is the only index showing a figure lower than 10%) while the wildest volatility explosions tend to group around the 45% – 48% area (the RVX again is the only outsider with a 58.84% peak). Statistically speaking, the most important intervals to consider are the 25%, the median and the 75% ones because they represent the most common spectrum of oscillation. Consequently, in case of low volatility most of the indices will tend to move within the 15% – 18% range (the RVX does not go below 20% though) while in high volatility environments equity volatility indices will tend to oscillate between 20% and 24% (the RVX is again the only outsider with 28.1%). The RVX is definitely the index showing the highest figures, which is normal given the fact that it tracks the volatility of less liquid stocks, but is it really the most volatile one? The answer is no because the fact that the RVX has an average value of 23.43% does not mean that it experiences the highest degree of fluctuations; it simply implies that the RVX is constantly higher than the others. In order to clarify this issue it is necessary to quantify the magnitude of the equity volatility indices’ movements. The next chart displays the volatility of the volatility indices. This time, however, the oscillations are expressed in monthly volatility because the CBOE indices are already annualized: The chart evidently shows that the most volatile equity index, amongst those proposed by the CBOE since 2011 so far, is the VIX and not the RVX. The second most volatile is the VXD and the least volatile is the RVX. How is this possible? Wasn’t the RVX the index showing the highest values? The explanation is simple. The VIX and VXD indices have a higher variability rate because a fairly good amount of moves tend to be large, fast and powerful. On the other hand, the RVX has a lower variability because it tends to fluctuate more often around its mean value and consequently the dispersion is lower. The next graph, which plots the ranking of the dispersion rate for the volatility of CBOE equity volatility indices, will help to expand more this concept: The graph confirms what previously stated: the VIX and the VXD are, on average, the most volatile indices. The 3rd index is the VXN while the RVX is the one that experiences the lowest degree of diffusion. The dispersion of the VIX and VXD is the largest even for small and large moves indicating that these 2 volatility indices move a lot more quickly than the others. Almost all indices do not go over the 30% threshold but when large market moves happen the monthly volatilities reach 50% and in some cases, like the VIX, 60%. To sum up, the VIX and VXD are the most volatile indices while the VXN and RVX ranked 3rd and 4th respectively (it is worth reminding that the sample analyzed goes from January 2011 until April 2013). Financial markets have different risk dimensions and equity volatility indices make no exception. The next table tries to provide empirical evidence to quantify 1 of those dimensions: the intraday risk Before getting started it is important to point out that the numbers reported in the table are volatility points. The intraday risk, on average, is around 1.35% for all equity volatility indices but the RVX reaches 1.5%. On the other hand, the table suggests that very large market moves can push equity volatility indices up or down by 13%, 14% or 15% in one single day. However, one question, given the above mentioned results, would be particularly appropriate: wasn’t the RVX the least volatile index? If that is true, how come the intraday risk is higher? The RVX index is more “dangerous” than other volatility indices as far as intraday risk is concerned. Nevertheless, even if the RVX fluctuates more wildly during trading hours its mean reverting pressure is higher in the medium term. In other words, the RVX is fairly volatile on a daily basis but it tends to mean revert towards its mean much more quickly than other indices. Furthermore, violent volatility bursts or aggressive drops tend to counterbalance themselves more effectively and in the medium term they result in a lower dispersion of the RVX with respect to its mean. The aforementioned phenomena explain why the RVX has a higher value in terms of volatility points but it is generally less volatile than the VIX or the VXD. Conclusions 1) On average all indices tend to oscillate within the 18% – 22% interval 2) In case of low volatility, indices will tend to move within the 15% – 18% range while in high volatility environments the equity volatility indices will likely oscillate between 20% and 24% 3) The RVX is definitely the index showing the highest value in terms of volatility points but it is the less volatile because the mean reverting pressure is higher in the medium term 4) The most volatile equity indices, amongst those proposed by the CBOE, are the VIX and the VXD 5) The dispersion rate for the VIX and VXD indices is the largest even for small and large moves which means that they oscillate more quickly and frequently than others 6) Intraday risk is around 1.35% for all equity volatility indices but the RVX reaches 1.5% 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. Options Greeks: Vanna, Charm, Vomma, DvegaDtime The present article deals with second order Options Greeks and it constitutes the second part of a previously published article entitled “Options Greeks: Delta,Gamma,Vega,Theta,Rho”. Before getting started it is important to highlight the great contribution that Liying Zhao (Options Analyst at HyperVolatility) gave to this report. All the calculations and numerical simulations that will be shown and commented are entirely provided by Mr Zhao. Second-order Greeks are sensitivities of first-order Greeks to small changes in different parameters. Mathematically, second-order Greeks are nothing else but the second-order partial derivatives of option prices with respect to different variables. In practical terms, they measure how fast first order options Greeks (Delta, Vega, Theta, Rho) are going to change with respect to underlying price fluctuations, volatility, interest rate changes and time decay. Specifically, we will go through Vanna, Charm (otherwise known as Delta Bleed), Vomma and DvegaDtime. It is important to point out that all charts have been produced by assuming that the underlying asset is a futures contract on WTI crude oil, the ATM strike (X) is 100, risk-free interest rate (r) is 0.5%, implied volatility is 10% while the cost of carry (b) is 0 (which is the case when dealing with commodity options). Vanna: Vanna measures the movements of the delta with respect to small changes in implied volatility (1% change in implied volatility to be precise). Alternatively, it can also be interpreted as the fluctuations of vega with respect to small changes in the underlying price. The following chart shows how vanna oscillates with respect to changes in the underlying asset S: The above reported chart clearly shows that vanna has positive values when the underlying price is higher than strike (in our case S>$100) and it has negative values when the underlying moves just below it (S<$100). What does that imply? The graph highlights the fact that vega moves much more when the underlying asset approaches the ATM strike ($100 in our case) but it tends to approximate 0 for OTM options. Consequently, the delta is very sensitive to changes in implied volatility when the ATM area is approached. However, it is important to point out that delta will not always increase if the underlying moves from, say, $80 to$100 because in many risky assets (stocks, equity indices, some currencies and commodities) the implied volatility is inversely correlated to the price action. As a result, if WTI futures go from $80 to$100 the implied volatility will probably head south and such a phenomenon would decrease vanna which, in turn, would diminish the value of delta.

Charm (or Delta Bleed): Charm measures delta’s sensitivity to a small movement in time to maturity (T). In practical terms, it shows how the delta is going to change with the passage of time. The next chart displays graphically the relationship between the aforementioned variables:

The chart suggests that, like in the case of vanna, the charm achieves its highest absolute values when the options are around the ATM area. Therefore, slightly in-the-money or out-of-the-money options will have the highest charm values. This makes sense because the greatest impact of time decay is precisely on options “floating” around the ATM zone. In fact, deep ITM options will behave almost like the underlying asset while OTM options with the passage of time will approach 0. Consequently, the deltas of slightly ITM or OTM options will be the most eroded by time. Charm is very important to options traders because if today the delta of your position or portfolio is 0.2 and charm is, for instance, 0.05 tomorrow your position will have a delta equal to 0.25. As we can clearly see, knowing the value of charm is crucial when hedging a position in order to keep it delta – neutral or minimize portfolio risk.

Vomma: Vomma measures how Vega is going to change with respect to implied volatility and it is normally expressed in order to quantify the influence on vega should the volatility oscillate by 1 point. The fluctuations of vomma with respect to S are shown in the next chart:

As displayed in the above reported chart out-of-the-money options have the highest vomma, while at-the-money options have a low vomma which means that vega remains almost constant with respect to volatility. The shape of vomma is something that every options trader should bear in mind while trading because it clearly confirms that the vega that will be influenced the most by a change in volatility will be the one of OTM options while the relationship with ATM options will be almost constant. This makes sense because a change in implied volatility would increase the probability of an OTM options to expire in-the-money and this is precisely why vomma is the highest around the OTM area.

DvegaDtime: DvegaDtime is the negative value of the partial derivative of vega in terms of time to maturity and it measures how fast vega is going to change with respect to the time decay. The next chart is a visual representation of its fluctuations with respect to the underlying asset S:

The above reported graph clearly displays that the influence of time decay on volatility exposure measured by vega is mostly felt in the ATM area especially for options with short time to maturity. The fact that DvegaDtime is mathematically expressed as negative derivatives makes sense because time decay is clearly a price that every options holder has to pay. In order to make things easier have a look at the plots of vega and theta because you will immediately realize that both volatility and time decay have their highest and lowest values in the ATM area. It goes without saying that ATM options have the highest volatility potential and therefore vega will be effected the most by the passage of time when the strike of our hypothetical options and the underlying price gets very close.

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.

Options Greeks: Delta,Gamma,Vega,Theta,Rho

First of all I would like to give credit to Liying Zhao (Options Analyst at HyperVolatility) for helping me to conceptualize this article and provide the quantitative analysis necessary to develop it. The present report will be followed by a second one dealing with second order Greeks and how they work.

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

Delta: Delta measures the sensitiveness of the option’s price to a $1 fluctuation in the underlying asset price. The chart displays how the Delta moves in respect to the underlying price S and time to maturity T: The chart clearly shows that in-the-money call options have much higher Delta values than out-of-the-money options while ATM options have a Delta which oscillates around 0.5. Call options have a Delta which ranges between 0 and 1 and it gets higher as the underlying approaches the strike price of the option which means that out-of-the-money call options will have a Delta close to 0 while ITM options will have a Delta fluctuating around 1. Many traders think of Delta as the probability of an option expiring in-the-money but this interpretation is not correct because the N(d) term in its formula expresses the probability of the option expiring ITM but only in a risk-neutral world. In real trading conditions higher Delta calls do have a higher probability to expire ITM than lower Delta ones, however, the number itself does not provide a reliable source of information because everything depends on the underlying. The Delta simply expresses the exposure of the options premium to the underlying: a positive Delta tells you that the premium will rise if the underlying asset will trend higher and it will decrease in the opposite scenario. Put options, instead, have a negative Delta which ranges between -1 and 0 and the below reported chart displays its fluctuation in respect to the underlying asset. It is easy to notice that as the underlying asset moves below the$100 threshold (the strike price of our hypothetical put option) the Delta approaches -1, which implies that ITM put options have a negative Delta close to -1, while OTM options have a Delta oscillating around 0. In practical trading the value of the Delta is very important because it tells you how the options premium is going to change in the case the underlying moves by $1. Let’s assume you purchase a 100 call options on crude oil with a Delta of +0.5 and the premium was$1,000.  If the option is at-the-money the WTI (the underlying asset) will be at $100 but if oil futures go up by$1 dollar to $101 the premium of your long call will move to$1,500. The same applies to put options but in this case the ATM Delta will be -0.5 and your long put option position will generate a profit if WTI futures move from $100 to$99.

Gamma: Gamma measures Delta’s sensitivity to a $1 movement in the underlying asset price and it is identical for both call and put options. Gamma reaches its maximum when the underlying price is a little bit smaller, not exactly equal, to the strike of the option and the chart shows quite evidently that for ATM option Gamma is significantly higher than for OTM and ITM options. The fact that Gamma is higher for ATM options makes sense because it is nothing but the quantification of how fast the Delta is going to change and an ATM option will have a very sensitive Delta because every single oscillation in the underlying asset will alter it. How Gamma can help us in trading? How do we interpret it? Again, the value of Gamma is simply telling you how fast the Delta will move in the case the underlying asset experience a$1 oscillation. Let’s assume we have an ATM call option on WTI with a Delta of +0.5 while futures prices are moving around $100 and Gamma is 0.08, what does that imply? The interpretation is rather simple: a 0.08 gamma is telling us that our ATM call, in the case the underlying moves by$1 to $101, will see its Delta increasing to +0.58 from +0.5 Vega (or Kappa): Vega is the option’s sensitivity to a 1% movement in implied volatility and it is identical for both call and put options. The below reported 3-D chart displays Vega as a function of the asset price and time to maturity for a WTI options with strike at 100, interest rate at 0.5% and implied volatility at 10% (the cost of carry is set to 0 because we are dealing with commodity options). The chart clearly highlights the fact that Vega is much higher for ATM options than for ITM and OTM options. The shape of Vega as a function of the underlying asset price makes sense because ATM options have by far the highest volatility potential but what does Vega really tell us in real trading conditions? Again, Vega (or Kappa) measures the dollar change in case of a 1% shift in implied volatility, therefore, an at-the-money WTI options whose value is$1,000 with a Vega of , say, 100 will be worth $1,100 if the implied volatility moves from 20% to 21%. Vega is a very important risk measure for options traders because it estimates how your P/L is going to change as a function of implied volatility. Implied volatility is the key factor in options pricing because the price of a single options will vary according to this number and this is precisely why implied volatility and Vega are essential to options trading (the HyperVolatility Forecast service provides analytical, easy to understand projections and analysis on volatility and price action for traders and investors). Theta: Theta measures option’s sensitivity to a small change in time to maturity (T). As time to maturity is always decreasing it is normal to express Theta as negative partial derivatives of the option price with respect to T. Theta represents the time decay of option prices in terms of a 1 year move in time to maturity and to view the value of Theta for a 1 day move we should divide it by 365 or 252 (the number of trading days in one year). The below reported chart shows how Theta moves: Theta is evidently negative for at-the-money options and the reason behind this phenomenon is that ATM options have the highest volatility potential, therefore, the impact of time decay is higher. Think of an option like an air balloon which loses a bit of air every day. The at-the-money options are right in the middle because they could become ITM or they could get back into the OTM “limbo” and therefore they contain a lot of air, consequently, if they have got more air than all other balloons they will lose more than others when the time passes. Let’s look at a practical example. Let’s assume we are long an ATM call option whose value is$1,000 and has a Theta equals to -25, if the day after both the underlying price and volatility are still where they were 1 day before our long call position will lose $25. Rho: Rho is the option’s sensitivity to a change in the risk-free interest rate and the next chart summarises how it fluctuates with respect to the underlying asset: ITM options are more influenced by changes in interest rates (negative Rho) because the premium of these options is higher and therefore a fluctuation in the cost of money (interest rate) would inevitably cause a higher impact on high-premium instruments. Furthermore, it is rather clear that long dated options are much more affected by changes in interest rates than short-dated derivatives. The below reported chart displays how Rho oscillates when dealing with put options: The Rho graph for put options mirrors what it has been stated for calls: ITM have a larger exposure than ATM and OTM put options to interest rate changes and long term derivatives are much more affected by Rho than in the short term (even in this case the 3-D graph displays negative values). As previously mentioned Rho measures how much the option’s premium is going to change when interest rates move by 1%. Hence, an increase in interest rates will augment the value of a hypothetical call option and the rise will be equal to Rho. In other words, the value of the call option will increase by$50 if interest rates move from 5% to 6% and our WTI call option has a premium of \$1,000 but Rho equals 50.

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

HyperVolatility – End of the Year Report 2012

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)

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)

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We do apologise for the interruption in the service but the increased amount of work we had to cope with prevented us from posting analysis on a weekly basis.

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Change #3) Since January 2012 onwards there will be a new page called “HyperVolatility Performance” where we will track the reliability of our forecasts and their profitability. Be aware that we will give you a range within which the price is likely to fall , like we have always done, rather than suggesting a single future forecast. We always provided a forecast range because it is very useful when placing profit taking  or stop losses targets and for this reason we will continue to do so. The “HyperVolatility Performance” page will show the rate of return for each asset and the total profitability of an hypothetical portfolio “HyperVolatility Portfolio” constructed by using each market considered in our analysis.

CHRISTMAS PRESENT AND CELEBRATING THE FIRST YEAR OF WWW. HYPERVOLATILITY.COM

We all are very grateful for your support and kind words and we will try, as much as we can, to keep the website updated. The first year of HyperVolatility has been a very intense one and a lot of work and time have been put into this project but it was worthwile because the number of readers increased to a rate that would have been very difficult to imagine one year ago:thank you!!!

We are now working on a project which will be published in a couple of weeks. The research is called “HyperVolatility End of Year Report 2011” and its aim is to provide you with a general understanding of what happened over the last 12 months. In other words, we are trying to put the big picture together for you. The project will cover all the markets that we currently analyse in a 1-by-1 mode in addition to a final overview of the macroeconomic factors that influenced the price action in 2011.

THE HYPERVOLATILITY END OF YEAR REPORT 2011 WILL BE PUBLISHED IN A PDF FORMAT. SHOULD YOU NEED A WORD FORMAT PLEASE SEND US AN EMAIL AT HYPERVOLATILITY@GMAIL.COM IN ORDER TO REQUEST ONE.

IT IS ALWAYS WORTH REMINDING THAT ALL OUR ANALYSIS AND TRADES ARE ASSUMED TO BE IN A SIMULATED ENVIRONMENT AND NO FINANCIAL RECOMMENDATION IS INTENDED. YOU TAKE FULL RESPONSABILITY FOR YOUR OWN FINANCIAL DECISIONS AND WE TAKE NO RESPONSABILITY FOR YOUR TRADING PERFORMANCE.