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The rate of unemployment in Italy – a well predicted rise

Staff and Wire Reports — Sat, 04 Feb 2012 17:33:00 +0000

A new estimate of unemployment rate in 2011 is now available for Italy. In December 2011, it almost touched 9.0%. Here we validate our model of unemployment as a function of the change in labour force.

We introduced the model of unemployment in Italy in 2008 with data available only for 2006. The rate of unemployment was near its bottom at the level of 6%. The model predicted a long-term growth in the rate unemployment to the level of 11% in 2013-2014.

The agreement between the measured and predicted unemployment estimates in Italy validates our concept which states that there exists a long-term equilibrium link between unemployment, u_t, and the rate of change of labour force, l_t=dLF/LFdt. Italy is a unique economy to validate this link because the time lag of unemployment behind l_t is eleven (!) years.

The estimation method is standard – we seek for the best overall fit between observed and predicted curves by the LSQR method. All in all, the best-fit equation is as follows:

u_t = 5.0l_t-11 + 0.07 (1)

As mentioned above, the lead of l_t is eleven years. This defines the rate of unemployment many years ahead of the current change in labour force. Figure 1 presents two versions of unemployment as defined by the U.S. Bureau of Labor Statistics (BLS) and the OECD. We describe the estimates provided by the OECD (labour force estimates also obtained from the OECD) but have to emphasise that the divergence before 1994 makes it difficult to find a unique model for both agencies.

Figure 2 presents the observed unemployment curve and that predicted using the rate of labour force change 11 years ago and equation (1). Since the estimates of labour force in Italy are very noisy we have smoothed the annual predicted curve with MA(5). All in all, the predictive power of the model is excellent and timely fits major peaks and troughs after 1988. The period between 2006 and 2011 was predicted almost exactly. This is the best validation of the model – it has successfully described a major turn in the evolution of unemployment near its bottom. No other macroeconomic model is capable to describe such dramatic turns many years ahead. As four years ago, we expect the peak in the rate of unemployment in 2013-2014 at the level of 11%.

The evolution of the rate of unemployment in Italy is completely defined ten year ahead. Since the linear coefficient in (1) is positive one needs to reduce the growth in labour force (see Figure 3) in order to reduce unemployment in the 2020s. For the 2010s everything is predefined already and the rate of unemployment will be high, i.e. above 9%.

Figure 1. The rate of unemployment in Italy as measured by the BLS and OECD.

Figure 2. Observed and predicted rate of unemployment in Italy.

Figure 3. The rate of growth in labour force.

Employment Situation: the effect of population controls and seasonal adjustment

Staff and Wire Reports — Sat, 04 Feb 2012 16:34:00 +0000

When interpreting labor statistics one should be very careful with Januaries. This is the month when major changes to the population estimates (including so called population controls, i.e. the distribution of population over age/sex/race) are introduced. Briefly, all corrections to the overall population and its components gathered during the previous year, or ten years after decennial censuses, are introduced in January as a step in the relevant times series by the Bureau of Labor Statistics. The Bureau explicitly explains this trick in its documents.

Figure 1 shows how big were these corrections in January 2012. We have displayed the first differences of several time series. The population corrections (the updated population controls) are applied to the civilian population (16 years and over), CP. The level of labor force, LF, the employment, E, and the number of unemployed, UE, is recalculated accordingly as the portions of the CP measured in the household surveys, e.g. the rate of unemployment. The 2012 correction is the highest since 2003, when the 2000 census was inserted in the time series. The adjustments to the population estimates change these rates only slightly. For example, the unemployment rate does not change and the employment-population ration rose due to the adjustment to the population controls. In January 2012, the civilian noninstitutional population rose by 1685000 including 1510000 due to the change in the population controls. This number is higher than in 2003, when the 2000 census was included in the CP estimates.

Another issue is the rate of unemployment. The population controls do not change this rate much. Figure 2 compares the rates as obtained with and without seasonal adjustment. The NSA rate for January is 8.8% due to peaks in this rate in January 2010 (10.6%) and 2011 (9.8%). The NSA rate will be also above the SA rate in February as well.

Figure 3 shows the number of unemployed. In January 2012, it increased by 849,000 in absolute values and decreased by 330,000 in seasonally adjusted representation. The absolute growth is partially related to the change in population estimates (controls) and partially to the seasonal adjustment.

Figure 1. The first differences of the civilian population (16 years and over), CP, the level of labor force, LF, the employment, E, and the number of unemployed, UE, time series. The 2012 correction is the highest since 2003, when the 2000 census was inserted in the time series.

Figure 2. The rate of unemployment, UER, as measured with seasonal adjustment, SA, and without seasonal adjustment, NSA. The NSA value for January is 8.8%.

Figure 3. The number of unemployed, UE, as measured with seasonal adjustment, SA, and without seasonal adjustment, NSA.

Suppressing Volatility and The Black Swan of Cairo

Staff and Wire Reports — Fri, 03 Feb 2012 17:30:00 +0000

First published in the May/June 2011 issue of Foreign Affairs, The Black Swan of Cairo: How Suppressing Volatility Makes the World Less Predictable and More Dangerous is a thought-provoking effort by co-authors Nassim Nicholas Taleb and Mark Blyth to advance the idea the efforts of policy-makers to smooth out the peaks and troughs of volatility actually has the unintended consequence of making the world a more volatile place.

I was reminded of the Taleb and Blyth article when I recently read Suppressing Volatility Makes the World More Dangerous, by Kurt Cobb of Resource Insights. Here Cobb extends the thinking of Taleb and Blyth and argues that not only do efforts to suppress volatility backfire in the economic and political realms, but also in areas such as agriculture and public health.

Of course, I could probably argue that Jeff Goldblum’s ranting against the instability of complex systems in Jurassic Park some two decades ago outflanked Taleb, Blyth and Cobb, but on a week when a low VIX seems to have many vexed, ruminating on the ideas of Taleb, Blyth and Cobb may help readers flesh out some insights into what may lie ahead. Along the same lines, I believe the links below might also contain some provocative and related thought starters.

Disclosure(s): none

Slaying the Natural Gas Contango Dragon

Staff and Wire Reports — Thu, 02 Feb 2012 17:57:00 +0000

Yesterday’s post on Natural Gas, Contango and UNG appears to have generated a fair amount of interest across a broad base of readers, so for an encore I have decided to forego the typical collection of dazzling Liszt miniatures and skip directly to more on the ways to benefit from the persistent contango and negative roll yield in natural gas.

Starting with the graphic below, I have plotted the performance of natural gas (red line) and three natural gas ETPs since June 16, 2011:

United States Natural Gas Fund (UNG) – blue line
United States 12 Month Natural Gas Fund (UNL) – green line
UBS ETRACS Natural Gas Futures Contango ETN (GASZ) – pink(ish) line

The reason the graph begins in June 2011 is that it marks the launch of GASZ; the other two ETPs have a much longer track record.

First, note that UNG does not attempt to minimize its exposure to contango. Like many other futures-based ETPs, its objective is to hold a one-month weighted average constant maturity in its portfolio and it does this by buying second month futures and selling front month futures. UNL, launched after UNG, was an attempt by the same issuer to minimize contango by holding twelve months of natural gas futures contracts on the assumption that contango is likely to be steepest at the front end of the futures curve and flatter in the more distant months. As the chart below shows, the recent performance differential between UNG and UNL has been minimal.

The UBS ETRACS product, GASZ, takes a completely different approach and is based on a natural gas futures spread index that shorts the front month and is long some of the more distant months. In other words, this ETP is specifically designed to take advantage of contango. According to UBS:

“The ISE Natural Gas Futures Spread™ Index, through a series of investments in natural gas sub-indices, effectively provides short exposure in front month natural gas futures contracts and long exposure in mid-term natural gas futures contracts. This is achieved by taking a 100% long position in the components of the ISE Short Front Month Natural Gas Futures™ Index, which provides short (or inverse) exposure to the ISE Long Front Month Natural Gas Futures™ Index and an aggregate 100% long position in the components of the ISE Twelfth Month Natural Gas Futures™ Index, ISE Thirteenth Month Natural Gas Futures™ Index and ISE Fourteenth Natural Gas Futures™ Index (33.33% per index), which provides long exposure to the mid-term Henry Hub Natural Gas Futures (NG) futures contracts. The index is rebalanced monthly before the Sub-Indices’ roll process to maintain the 1:1 ratio.”

For more information, check out the GASZ web site and prospectus.

The results, at least as seen in the chart below, show that the GASZ approach has some promise insofar as the last eight months are concerned. To be fair, GASZ is very thinly traded and has yet to inspire a broad group of investors, but here is an approach that is not likely to be correlated with any strategies investors are currently running and has been racking up profits in a sideways (at least for equities) market.

Of course investors can always short UNG, but I believe that in much the same manner that ZIV is undeservedly neglected as an inverse VIX futures contango play, so is GASZ overlooked for the same reasons. These are two ETPs with a lot of potential that deserve a broader audience.

Finally, as a side note, UNG announced late yesterday that it will undergo a reverse 1-4 split following the market close on February 21. Here is a product that is down more than 40% in each of the last three years and is already down more than 21% in 2012. Don’t be surprised if this is not the last reverse split.

[source(s): United States Natural Gas Fund]

Disclosure(s): long GASZ and ZIV, short UNG at time of writing

Natural Gas, Contango and UNG

Staff and Wire Reports — Wed, 01 Feb 2012 20:06:00 +0000

I have talked at length in this space about the contango and negative roll yield issues that plague VXX. Periodically these discussions trigger a question from a reader about the impact of contango on some of the other ETPs.

Just to be clear, as far as ETPs are concerned, contango and backwardation issues are limited solely to those products which hold futures in their portfolio. The large majority of futures-based ETPs are in the commodity space, but in theory at least, any security for which there are futures could end up with a futures-based ETP. Fortunately, ETFdb keeps a handy list of these products at their Futures-Based ETF page.

The main reason why I talk so much about contango in the context of VIX-based ETPs is that the VIX products have a tendency to produce huge levels of negative roll yield (at a rate of 11% per month at the moment in the front two months of the VIX futures) relative to the other products.

Outside of the VIX product space, contango is probably most notorious in crude oil and natural gas – and the two most popular ETPs for these commodities, USO and UNG. Still, contango in these products is generally much smaller than it is with VXX, but right now contango is unusually high in UNG. While contango (front two months) in USO is only 0.4% right now, it is actually at 11.1% per month in UNG.

Note that unlike VXX, which has a daily roll, UNG rolls its entire portfolio over the course of four days per month. Better yet, UNG publishes a schedule of their roll dates, reprinted below, thought it does come with the disclaimer “Roll Dates are projected and subject to change without notice.”

So…while it has already been a great year for those who are short natural gas, it is possible that persistent contango will make short UNG positions even more profitable going forward.

Finally and perhaps most importantly, it is critical to keep in mind that steep contango does not happen willy nilly. Instead, contango is essentially a reflection of where the market expects prices to be headed (net of the cost of carry) in the future. Looked at in this context, UNG contango of 11.1% means that the reason shorts are receiving a 11.1% benefit from the negative roll yield is that the market anticipates prices will rebound 11.1% or so over the course of the next month. Contango and roll yield are not a free lunch by a long shot, but over the long term, if risk can be properly managed, positions that benefit from contango should be able to finance at least a few lunches.

[source(s): United States Natural Gas Fund]

Disclosure(s): short VXX and UNG at time of writing

Comparison of Economic Projections Provided by Federal Reserve Board and Congressional Budget Office. Both Are Inconsistent

Staff and Wire Reports — Wed, 01 Feb 2012 08:36:00 +0000

TheFRB and CBO have recently projected the evolution of key macroeconomic variables including real GDP and the rate of unemployment. In our blog, we have developed a very accurate model linking the rate of unemployment in the US to the rate of real GDP (per capita) growth: (A series of posts has resulted in a working paper.) The following relationship (Okun’s law) has been estimated:

du = -0.465dlnG + 1.113, (1)

When integrated between t0 and t, equation (1) can be rewritten in the following form:

u(t) = u(t0) -0.465bln[G/G0] +1.113(t-t0) + c (2)

Without loss of generality, we assume t0=0. The intercept c≡0, as is clear for t=t0. Instead of integrating (2), we calculate cumulative sums of the annual estimates of du and lnG with appropriate initial conditions. The cumulative sum of du’s is the time series of the unemployment rate. Figure 1 depicts the measured and observed curves for the period between 1958 and 2011. The agreement is excellent and has been obtained by a formal statistical method (LSQR).

The FRB and CBO explicitly projected the growth rate of real GDP, rGDP, and the rate of unemployment, UE, through 2014. Table 1 lists the most probable rates for 2012 to 2014, with the FRB providing broader ranges of expected values with specially highlighted central tendencies. We have calculated the average values for the most probable ranges. CBO gives much higher rates of unemployment for 2012 and 2013 but lower long term rate, i.e. the rate projected to 2018-2022. At the same time, the growth rate of real GDP projected by CBO is lower for 2012 and 2013.

Table 1

Year	2011	2012	2013	2014	Long term trend
FRB rGDP	-	2.4	3.0	3.7	2.45
CBO rGDP	1.6	2.2	1.0	4.0	2.5
FRB UE	-	8.35	7.75	7.15	5.6
CBO UE	9.0	8.8	9.1	7.0	5.4

From (1) it follows that higher rates of GDP growth decrease the rate of unemployment. Since our model is based on real GDP per capita we have to reduce the growth rates in Table 1 by 0.8% per year, which is the growth in the overall population. This gives the estimates of the growth rate of GDP per capita. Using (2) we calculate the rate of unemployment which will correspond to the projected real GDP.

Figure 2 compares the unemployment rate in the US between 2012 and 2014 as projected by the FRB and CBO and predicted from their relevant projections of real GDP per capita. The FRB and CBO have wrongly projected the pair unemployment/GDP which is driven by Okun’s law. The best match is observed between the unemployment projection made by CBO and the GDP projection by the FRB.

In this post, we do not state that any of these projections is right or wrong. We just show that, when interpreted jointly, the projections of UE and GDP are not consistent with each other. In other words, the growth rate of real GDP projected by the FRB and CBO cannot provide the projected rates of unemployment. One may check these projections in 2015.

Figure 1. The observed and predicted rate of unemployment in the USA between 1958 and 2011.

Figure 2. Comparison of the unemployment rate in the US between 2012 and 2014 as projected by the FRB and CBO and predicted from the projections of real GDP per capita.

CBOE Adds Options to Emerging Markets Volatility Index (VXEEM)

Staff and Wire Reports — Tue, 31 Jan 2012 23:57:00 +0000

Earlier this month, the CBOE launched futures on the CBOE Emerging Markets ETF Volatility Index (VXEEM) and barely three weeks later, VXEEM options began trading today.

For more information on VXEEM options, which are based on the popular EEM emerging markets ETF, a good first stop is the CBOE’s VXEEM options product specification page. Of particular note is the fact the options expiration cycle is the same for VXEEM options as it is for the futures products. Additionally, VXEEM futures and options have the same expiration cycle as VIX futures and options, meaning that they will expire on Wednesdays (February 15, March 21, April 18 and May 16), with the options last traded on the Tuesday immediately following the expiration. For more information, check out the CBOE’s VXEEM splash page and information circular.

In the graphic below, courtesy of LivevolPro.com, I have collected closing data for some of the primary U.S. volatility indices, including those which are volatility indices for ETPs and single stocks. The indices are sorted from highest to lowest and provide a good sense of the market’s perceptions of relative risk across various stocks, groups of stocks (sectors and geographies) and asset classes.

Partly due to today’s earnings announcement, VXAZN, the volatility index for Amazon (AMZN) tops the list, with volatility indices for silver (VXSLV), Goldman Sachs (VXGS) and gold miners (VXGDX) rounding out the top four. VXEEM ranks eighth of the twenty volatility indices at 27.97 and currently carries a 43.8% premium to the VIX. Is that 43.8% premium too high? Too low? With VXEEM options (and futures) now you can not only express your opinion, but benefit financially if you are correct.

[source(s): LivevolPro.com]

Disclosure(s): Livevol is an advertiser on VIX and More

Little bit of beginners luck with my targets

Staff and Wire Reports — Tue, 31 Jan 2012 04:30:00 +0000

Below is the chart I used today when tweeting price projections for DX (dollar futures). I think all targets I called hit, so hats off to beginners luck. The last one I called surprised me the most since I made the projection about 4 or 5 hours before it hit. The timing on the projection was for DX to hit 79.17 before the close, a little after 3 (central). DX actually closes an hour later and hit 79.185 before it closed. Along the way I was questioning the validity of the pattern, but I couldn’t see anything else that would show a divergence so I just turned off the screen and set a limit order for 79.17 to close the short position (shorted at 79.30 & 79.34 around noon—paper trades, I’m new at these harmonic patterns). When DX opened later that evening it gapped down, hitting my “conservative” projection of 79.17.

Tweets from today with price projections:
https://twitter.com/bngesg/status/164005644788187136
https://twitter.com/bngesg/status/164008716256026624
https://twitter.com/bngesg/status/164016282226790402
https://twitter.com/bngesg/status/164031170831597568 https://twitter.com/bngesg/status/164055992059965440
https://twitter.com/bngesg/status/164075400039895040
https://twitter.com/bngesg/status/164107978050514944
https://twitter.com/bngesg/status/164151912122421249
DX chart with M patterns I was using:

Interesting look at the 30min…looks like DX is completing the BC leg of a W (bearish crab/bat), but depending on if it moves up now or later it could actually be a M (bullish crab/bat). Some folks can pick up on the direction of each leg, but I’m only feeling comfortable with the last leg of the pattern to enter a position, or at least half way through the BC leg.

Follow the rules: Cut losses quickly; Don't overtrade; Take profits/sell into strength; Don't chase, watch for the pullback; Have a plan and stick to it
View my other posts/give me some advice at http://part-timepennystocktrader.blogspot.com/. Follow me on twitter @BNGESG.

A Monthly Comparison of VXX and VXZ

Staff and Wire Reports — Mon, 30 Jan 2012 20:05:00 +0000

Three years after their launch, VXX has about four times as many assets as its mid-term sibling, VXZ. When it comes to public relations and media column inches, I imagine the ratio is more like 50-1 in the favor of VXX. In many ways VXZ is the unloved stepchild of the duo.

I recently opined that when it comes to the inverse variants of these two ETPs, ZIV is Undeservedly Neglected. I believe the same case holds for VXZ. One of the great difficulties in trading VIX-based ETPs is that while the potential returns are enormous, when things move in the wrong direction, a bad trade can spiral out of control and trigger an extremely painful loss with surprising speed. This is a large part of what makes VXZ more attractive than VXX. Even though VXZ is relatively volatile, with a current 30-day historical volatility of 39.5, it pales in comparison to the 70.1 30-day HV of its rocket-fueled short-term brother, VXX. In this case, the slower the train wreck, the more easily it can be avoided and position risk becomes much more manageable.

In addition to the lower volatility, VXZ is also much less susceptible to the contango and roll yield issues that plague VXX. In fact, on average VXZ is only subjected to about 1/3 of the negative roll yield that impacts VXX, which is a large part of the reason why the long-term performance of VXZ is much superior to the numbers put up by VXX. To illustrate this point, the chart below shows the month-by-month performance data for VXX and VXZ going back two years.

In summary, if you are impatient and you like action, VXX is the better bet, but if you have some patience and want better odds, VXZ is often a better long volatility play.

[source(s): ETFreplay.com]

Disclosure(s): long ZIV and short VXX at time of writing

A Monthly Comparison of VXX and VXZ

Staff and Wire Reports — Mon, 30 Jan 2012 20:05:00 +0000

In summary, if you are impatient and you like action, VXX is the better bet, but if you have some patience and want better odds, VXZ is often a better long volatility play.

[source(s): ETFreplay.com]

Disclosure(s): long ZIV and short VXX at time of writing