Wednesday, January 4, 2017

VIX ETPs Flash Some Green in 2016

Last year I shocked quite a few investors and media outlets with the publication of Every Single VIX ETP (Long and Short) Lost Money in 2015.  My intent was not to tar and feather the VIX exchange-traded products landscape, but to highlight the fact that in an environment characterized by sharp VIX spikes and other volatility extremes, the power of volatility compounding price decay can overwhelm both long and inverse ETPs. 

In sharp contrast to across-the-board losses in 2015, the performance of VIX ETPs in 2016 was much more balanced and in line with historical norms.  While there were some sharp VIX spikes, the combination moderate volatility, above-average contango and persistent mean reversion translated into a sharp down year for the long VIX ETPs and a strong up year for the inverse VIX ETPs.  The more complex multi-leg, long-short and dynamic VIX strategy ETPs were closest to breaking even for the year, with half of these posting modest gains and half posting small losses.

In the graphic below, I have plotted the performance of all twenty VIX-based ETPs with respect to leverage and maturity, using leverage on the y-axis and maturity on the x-axis.  This group includes five VIX strategy ETPs that have no easily discernible point on the leverage-maturity grid.  Depending on how finely you wish to split hairs, these twenty ETPs account for anywhere from fourteen to eighteen unique ways to trade volatility long and short, across various maturities and according to a wide variety of strategic approaches. 


[source(s): VIX and More]

On the plus side, while both XIV and SVXY were up over 80% during calendar 2016, this performance falls short of the 2012 and 2013 numbers, where each ETP gained more than 100% in both years.  Similarly, while losses of over 93% for UVXY and TVIX must sound like a worst-case scenario for these two products, losses were over 97% in 2012 and just slightly better – at -92% – in 2013.  In terms of consistent winners, while their numbers have been more modest, the most consistent gainers in the VIX ETP space have been ZIV, TRSK and SPXH.

Two new VIX ETPs entered the fray in 2016:  VMIN and VMAX.  While these products have not yet attracted the interest of investors that I believe is warranted (VMAX and VMIN Poised to Be Most Important VIX ETP Launch in Years), there is still time for investors to discover these products.  For the record, VMIN was launched on May 2, 2016 and outperformed both XIV and SVXY from the launch date until the end of the year, racking up an impressive 80.5% return in just eights months of trading.  Going forward, I would expect VMIN to regularly be the top performer in any period in which the inverse ETPs post positive returns.

For those who may be wondering, the VIX index was down 22.9% for the year, while the front month VIX futures product ended the year with a loss of 18.3%.

As is typically the case, contango was a significant performance driver during the course of the year.  Contango affecting the front month and second month VIX futures averaged a relatively robust 8.3% per month during the year (the highest since 2012), while contango between the fourth month and seventh month was slightly above average at 1.8% per month.

During the course of the year, five VIX ETPs were shuttered.  These include VXUP and VXDN, XVIX, CVOL and VQTS.  The biggest factors in the demise of these products was a lack of volume and assets.  In the case of VXUP and VXDN, the product complexity and cumbersome array of distributions also helped to quell investor enthusiasm.  Last but not least, I elected to drop XXV and IVOP from this list as these zombie ETPs both have less than 1% exposure to their underlying volatility index due to the lack of daily rebalancing.  As a result, these have become almost entirely all-cash vehicles, with a dash of volatility.  (For those who are curious about these instruments, follow the links above, click on the link to the prospectus and do a keyword search for “participation.”)

As an aside, for those who may be wondering, the flurry of recent posts is not an anomaly.  There is a lot to be said about the VIX, volatility, ETPs, market sentiment and many of my other areas of interest. With the the-year anniversary of the VIX and More blog just three days away, this seems like a good time to dive head first back into the fray.

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For those who may be interested, you can always follow me on Twitter at @VIXandMore


Disclosure(s): net short VXX, VMAX, UVXY and TVIX; net long XIV, SVXY and ZIV at time of writing

Monday, January 2, 2017

The 2016 VIX Futures Term Structure: Extraordinarily Average

Two days ago, in The Year in VIX and Volatility (2016), I made no mention whatsoever of the VIX futures term structure.  Traders of the full range of VIX products (futures, options and ETPs) hopefully know by now that the entire VIX product landscape is based -- and priced -- off of VIX futures and one of the most important aspects of VIX futures is the shape of the term structure.

Long story short:  as the graphic below shows, the 2016 VIX futures term structure (double red line) was closer to its historical average (wide gray line) than any prior year since the launch of VIX futures in 2004, with the average term structure over the course of the year demonstrating a relatively modest upward sloping term structure, also known as contango.


[source(s) CBOE, VIX and More]

By way of explanation, the graphic above shows the average (mean) normalized term structure for each year since the VIX futures were launched. In normalizing the data, I have set the average front month VIX futures contract to 100 and have expressed the averages of the second through seven months as multiples of the front month.  Note that the terms structure lines are dotted and somewhat wavy for 2004 – 2006, due to the fact that the CBOE did not implement a full complement of consecutive monthly futures until October 2006.

In terms of takeaways, since I have not posted this graphic in two years, note that the term structure for 2015 was slightly flatter than average.  Looking back a couple more years, note that 2012 and 2013 saw the steepest term structure on record.  In the thirteen-year history of VIX futures, only two years saw a downward sloping term structure, also known as backwardation2008 and (barely, depending upon how one measures) 2009.

During the course of 2016, the VIX futures term structure moved into backwardation on four separate occasion and closed in backwardation on a total of 37 days – with 31 of those 37 days running consecutively from January 4th to February 16th.  These four instances and 37 days are just slightly below the average year, as can be seen in the graphic below.


[source(s) CBOE, VIX and More]

Last but not least, the average term structure for the year as well as the frequency and magnitude of the contango-backwardation dance is a strong determinant of the annual performance of the VIX ETPs and in my next post I will detail why 2016 was unlike the previous year, where Every Single VIX ETP (Long and Short) Lost Money in 2015.

Related posts:


For those who may be interested, you can always follow me on Twitter at @VIXandMore


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

Saturday, December 31, 2016

The Year in VIX and Volatility (2016)

The consensus called for a big uptick in volatility in 2016 and while there was a lot of drama, the VIX spikes were relatively manageable and short-lived.  The VIX opened the year at 22.48 and ended the year at just 14.04.  For the full year, the median VIX was 14.31, while SPX historical volatility for the full year ended up at a mere 13.12.

That being said, there were five distinct VIX spikes in the graphic below, listed according to chronology:
  • Fears related to slowing growth in China (January)
  • A plunge in crude oil prices to $26.05/bbl. for WTIC, as investors grappled with the possibility that Cushing storage facilities would be exhausted (February)
  • The surprise Brexit vote result in favor of the U.K. leaving the E.U. (June)
  • A cocktail of nearly simultaneous shocks from Fed President Rosengren (suddenly sounding hawkish), Jeff Gundlach (interest rates have bottomed) and the European Central Bank (no additional stimulus) puts pressure on stocks (September)
  • Increasing uncertainty leading up to the U.S. election (November) 

In all five instances, the VIX moved up sharply, but in defiance of historical precedent, the volatility index moved down almost as sharply as it moved up.  In fact, some of the biggest extremes for the year came in the form of volatility crushes, where the VIX had an unprecedented series of sharp downward one-day move.  Checking the record books, the only previous year that the VIX posted three top 20 one-day declines was 2007 – and clearly investors were in denial that year.  This year the Trump election caused the sixth largest one-day drop in the history of the VIX, whereas the Thursday before and Tuesday after the Brexit vote triggered the eighteenth and tenth largest one-day VIX declines.

On the other side of the ledger, some of the upward moves in the VIX made the record books as well.  The day following the Brexit vote saw a 49.3% VIX spike – the fifth highest one-day spike on record.  What was even more surprising was the Rosengren/Gundlach/ECB cocktail noted above triggered a 39.9% spike (eleventh highest in history) in what seemed to be a relatively calm market environment in September.  It turns out the VIX was just getting warmed up for greater things, including a record nine consecutive up days leading up to the November election.

Even with these extremes, the highs and lows in the VIX were rather middling, with the VIX peaking at 32.09 on January 20th and hitting an annual low of 10.93 on December 21st.

The graphic below captures these and other highlights from 2016:



[source(s): StockCharts.com, VIX and More]

Included in the non-VIX highlights are a 5000+ year low in interest rates in Europe and Japan (where negative interest rates prevailed) as well as a thirteen-year low in the price of crude oil.  On the geopolitical front, political craziness of one kind or another abounded in Brazil, South Korea, Turkey, Italy, Colombia and South Africa, among other locations.  Terrorism also left its footprint again in 2016 and Zika also created considerable political and social turmoil.  In the financial realm, European banks had a very difficult year and begin 2017 on shaky footing.

While the year ended on a relatively quiet not, I suspect 2017 will have much more in the way of new surprises, including swans of many dark hues.  Next week I will resume the VIX and More fear poll and find out what the consensus is for volatility and its causes in the coming year.

Finally, since 2011, I have been maintaining a proprietary Macro Risk Index that measures volatility and risk across a broad range of asset classes, including U.S. equities, foreign equities, commodities, currencies and bonds.  In 2016, the Macro Risk Index was trending down most of the year, punctuated by significant spikes in February (crude oil) and again in June (European currencies). 

How did 2016 measure up to expectations?  I sum up the year in My Low Volatility Prediction for 2016: Both Idiocy and Genius.  Also worth investigating are a pair of Barron’s articles from one year ago laying out two opposing perspectives on volatility in 2016.  For the case for rising volatility and what to do about it, try Jared Woodard’s Prepare for Rising Volatility in 2016.  I provide the contrarian point of view in The Case Against High Stock-Market Volatility in 2016.

Have a happy, healthy and profitable 2017!

Related posts:


For those who may be interested, you can always follow me on Twitter at @VIXandMore

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

Friday, December 30, 2016

My Low Volatility Prediction for 2016: Both Idiocy and Genius

A year ago, Steve Sears of Barron’s asked me to pen a guest column for The Striking Price and use the opportunity to opine on how I saw the volatility landscape unfolding in 2016.  Without thinking about it too much, I was fairly certain I was going to devote the column to the many threats that had the potential to spiral out of control during the course of the year, but before I had an opportunity to start translating my thoughts into writing, other pundits started weighing in with their predictions for 2016 and without exception, everyone who ventured a guess on the direction of volatility was adamant that volatility would be substantially higher in 2016 than 2015.

Not wanting to follow the herd and always on the lookout for a more provocative point of view, I decided to fade the consensus, rip up the script in my head and adopt a contrarian outlook:  The Case Against High Stock-Market Volatility in 2016.  The column began as follows:

“Looking at all the market predictions for 2016, one thing is certain: Almost all of the pundits agree that volatility will be up, making a bet on rising volatility one of the year’s most popular trading ideas.

But, as is the case with much of the investment landscape, when most of the pundits agree about how the future will unfold, it pays to investigate the contrarian point of view.

As to volatility, the contrarian perspective is particularly compelling for 2016 because volatility is notoriously hard to predict; investors have a habit of dramatically overestimating its future level; and, when it comes to forecasting the causes of volatility, “experts” and investors alike have a penchant for fighting the last war.”

Then came January.  For those who have tried to put it out of their memory, January was one of the worst first months on record, with the S&P 500 Index falling 7.3% for the month.  The bearish trend continued into February, as fears related to China and crude oil had investors selling en masse.  By the time stocks found a bottom on February 11th, the S&P 500 Index was down 11.4% -- by some measures the worst beginning for stocks in history.  Volatility, of course, was spiking and the VIX had already topped 30.00 on three separate occasions just seven weeks into the year.

My prediction of lower volatility:  complete idiocy.

But the year was not over and we still had to grapple with Brexit, the crazy and unpredictable election season in the U.S., a Fed interest rate hike and persistent political turmoil in places like Italy and Brazil.  Amazingly, stocks showed a tremendous amount of resiliency and all the VIX spikes were given the Whac-A-Mole treatment as VIX mean reversion emerged as a key theme during 2016.

Now that the year is (almost) in the books, it turns out my contrarian low volatility prediction was spot on and the rest of the pundits ended up on the wrong side of a crowded losing trade, assuming one was patient enough to take a full-year perspective.  Genius?  Probably not, but definitely more right than wrong, despite my having to wear a dunce cap for the first two months of the year.

The graphic below shows the annual average VIX and historical volatility going back to 1990.  Note that while the average VIX fell from 16.67 to 15.83 this year, there was an even larger drop in realized or historical volatility, which fell sharply from 15.53 to 13.14.

[source(s):  CBOE, Yahoo, VIX and More]

As far as takeaways are concerned, there is the obvious lesson regarding the herd mentality and crowded trades.  Additionally, there are also issues regarding how investors frame a problem or potential problem.  For example, when one expects an increase in volatility they are more likely to be overprepared for that development and/or overreact when there are initial signs of an increase in volatility.  Ironically, if investors load up on SPX puts or VIX calls, then this makes it much more difficult for panic to filter into the market.  This leads to a theme that has been repeated often in this space:  VIX spikes are notoriously difficult to predict and it is also more difficult to anticipate a change in volatility regimes than many believe.

Last but not least, as the graphic above shows, predictions of future volatility almost always overshoot realized volatility, which is why in the last 27 years only the extreme turmoil in 2008 saw realized volatility higher than the VIX over the course of a full year.

As for 2017, when it comes to volatility, expect the unexpected.

Related posts:


For those who may be interested, you can always follow me on Twitter at @VIXandMore

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

Thursday, December 29, 2016

Average VIX and Volatility for Last Fourteen Presidents

What kind of VIX is appropriate for the Trump Administration? 

For investors in general and volatility traders in particular, this is one of the more interesting questions going into 2017.  Should the VIX be higher or lower in the context of a Trump Administration relative to the Obama Administration?  How much economic policy uncertainty is there in Trumponomics?  How will various geopolitical issues wax and wane in the context of a Trump-Tillerson foreign policy agenda?

While these questions are difficult ones, what is not difficult is looking in the rear-view mirror for some historical context, so that is exactly what I did, calculating the historical volatility for each presidency going back to the Hoover Administration.  In order to take advantage of stock data prior to the 1950s, one has to make use of the DJIA rather than S&P averages.  While VIX data is even more interesting, the VIX was not launched until Bill Clinton’s inauguration and historically reconstructed data from the CBOE only extends back to George H. W. Bush’s presidential term.

The results of the number crunching are included in the chart below and show Herbert Hoover’s historical volatility of 42.87 more than double that of the runner-up, Franklin Delano Roosevelt who posted a historical volatility of 20.88.  The only other president to top the 20 level in terms of historical volatility was George W. Bush at 20.28.  At the other end of the spectrum, the least volatile presidency was that of Lyndon B. Johnson, where HV averaged an amazingly low 9.12.  Following LBJ on the low end are Dwight Eisenhower at 10.70 and Harry Truman at 12.20.


[source(s):  CBOE, Yahoo, VIX and More]

Among recent presidents, three of the last four presidencies (George W. Bush is the exception) have seen middling volatility, with Barack Obama 6th of 14 as of today’s data, while Bill Clinton is 7th and George H. W. Bush in 8th place.

Since the eye canot help but see trends and patterns whether they exist in real life or not, I am obliged to observe that since the LBJ presidency there is a pattern of higher highs and higher lows.  Could volatility by presidential term be trending up?  I am certainly not ready to go that far.

In terms of key takeaways, it is worth noting that the median historical volatility (combining data from Bill Clinton and George H. W. Bush) indicates that a middle-of-the-road presidency can expect historical volatility of 14.65 and a VIX of 18.91.  As far as the VIX is concerned, the 18.91 number aligns nicely with current VIX futures quotes for May and June 2017.

Related posts:


For those who may be interested, you can always follow me on Twitter at @VIXandMore


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

Sunday, November 20, 2016

Post-Election Risk Trending Up in Treasuries and the Euro, Down in U.S. Stocks

You can always tell when the crowd gets long the VIX and ends up on the wrong side of the trade.  “The VIX is broken!” becomes an oft-repeated refrain, as does “The markets are rigged!” and the usual list of exhortations from those who are in denial.  The current line of thinking is that the world must be much more dangerous, risky and uncertain as a result of a Trump victory, yet the VIX is actually down 31.4% since the election – ipso facto the VIX is broken.

While I have more than a small soft spot in my heart for the VIX, I will be the first to point that taking an Americentric, equity-centric view of the investment landscape is dangerous and naïve.  More often than not, the issues that end up having a strong influence on the VIX are born on foreign soil and/or in other asset classes.  Just look at the recent history in China, Greece, Italy, currencies and commodities to name a few.

When it comes to looking at implied volatility indices as a risk proxy, I prefer to survey the landscape across asset classes, geographies and sectors, which is why I have developed tools such as a proprietary Macro Risk Index (more on this shortly) that look at risk across asset classes, geographies and sectors.

In the graphic below, I have isolated a handful of volatility indices that cut across asset classes and geographies to show how these have moved in the eight days following the election.  Note that Treasuries (TYVIX) and the euro (EVZ) have been trending steadily higher since the election as uncertainty related to the future of inflation and interest rates in the U.S. has risen, while the relationship that the Trump Administration will have with our NATO allies and the European Union is also somewhat murkier. 

Gold implied volatility (GVZ) initially moved sharply higher following the election, but has since receded, as gold prices fell swiftly after the election, but have since stabilized.  Meanwhile, emerging markets saw dramatic selling immediately following the election, but have bounced during the course of the past week as fears and implied volatility (VXEEM) have subsided.  Last but not least, the moves in crude oil and crude oil implied volatility (OVX) have been the least remarkable of the group


[source(s):  CBOE, VIX and More]

In aggregate, the picture is a mixed one in terms of implied volatility, risk and uncertainty.  As is often the case, risk has become elevated in certain asset classes, such as Treasuries and the euro.  In other areas, such as U.S. equities – and their VIXian barometer – there are winners and losers, with the result that a net bullish outlook has moved equity implied volatility lower.  This is not to say that a Trump Administration – whose cabinet members and policy priorities are largely unknown at this juncture – will not increase risk in some areas.  More risk is certainly on the horizon and if history is any guide, an Americentric, equity-centric view of the investment world is likely to be slow in identifying those risks.

Related posts:


For those who may be interested, you can always follow me on Twitter at @VIXandMore


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

Tuesday, November 8, 2016

Top VIX Crushes in History

Yesterday’s sharp downward move in the VIX gave me a reason to tweet that the volatility crush as seen in the SPX and VIX was among the top 25 in history.  Upward pressure on the VIX toward the end of the session dropped the VIX down to the 30th largest VIX decline in history, but along the way the Twitterati raised a number of questions about volatility crushes and the VIX as a measurement tool of broad market volatility crushes.

Since I have never seen any data related to the VIX and volatility crushes before, I thought this might be an opportunity to present some of my data and talk about the findings.  In the chart below, I have captured the 25 largest one-day declines in the VIX since 1990 and have presented data showing the forward performance of the SPX in periods from one to 100 days and I have also added some brief commentary regarding the causes.  In some cases I link the volatility crush to a previous VIX spike and use some explanatory shorthand in the process.  For instance, the top crush day, May 10, 2010, followed 2 days after the 21st largest VIX spike (“2d+ #21”).


[source(s):  CBOE, VIX and More]

Of course, most of these volatility crush days coincided with huge jumps in the SPX, but there are some interesting exceptions, not the least of which was the 0.04% decline in the SPX back on April 11, 1990.  That just happens to be the only day for which I cannot find any obvious explanatory catalyst – to the extent that a 0.04% in stocks can actually have a catalyst – but given the proximity to the upcoming Gulf War, my guess is that some sort of news related to Iraq played into this event.

Note also how many of these volatility crush instances follow other important market-moving high fear events one or two days later, in true mean reversion fashion.  Examples on this list range from the flash crash, Greece, Lehman Brothers and Bear Stearns to several VIX all-time highs, Russian political and economic crises, the Boston marathon bombing, etc.

Things get even more interesting if you compare the top 25 VIX crushes to the top 25 VIX spikes in history (for an identical table recapping the top 25 spikes refer to Last Two Days Are #5 and #6 One-Day VIX Spikes in History.)  For starters, the top 25 VIX spikes all move up at least 31%, while none of the top 25 VIX crushes managed to eclipse the 30% decline level.  Also note the differences in the mean reversion predictive value of spikes versus crushes.  In general, the performance of the SPX following VIX crushes is modestly lower than that of the SPX in general.  On the other hand, the performance of the SPX following VIX spikes is generally better than the SPX in general – much more so if the September 29, 2008 outlier is dropped from the data set.

Another point that I think is worth making speaks to the overall changes in the volatility space.  The critical data point is that 11/25 of the top 25 VIX crushes happened since the beginning 2010, while 14/25 of the top 25 VIX spikes have occurred during the same period.  This means that we have had as much in the way of big volatility moves in the list seven years as in the previous twenty years of VIX data.  In other words, the volatility landscape is changing and the rise of VIX futures and VIX ETPs are no doubt an important part of that change.

For those who may be interested, you can always follow me on Twitter at @VIXandMore

Related posts:



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

Sunday, November 6, 2016

VIX Sets New Record with Nine Up Days in a Row

Over the course of the past few days I have been tracking the slow grind upward in the VIX on Twitter, noting that it had been up seven, eight and eventually nine (as of Friday) days in a row.  As the VIX is a mean reverting animal, I find it interesting that until Friday, the VIX had never risen for nine consecutive days in 27 years of VIX data.  Perhaps even more interesting, during the same period, the VIX had fallen nine days in a row on nine separate instances and even managed to fall ten days in a row on three occasions.  For those who may be wondering, this is yet another data point supporting the idea that VIX mean reversion is more robust following a sharp VIX spike than a sharp VIX decline.

Whenever the VIX makes an unusual move, I am bombarded by variations along the lines of, “That’s nice, but what does it mean for the markets?"  As much as the doomsayers hate to hear this, fear is almost always a great fade, particularly when you have a little patience.  Rather than talking about the matter in theoretical terms, however, I thought I would let some numbers do the talking.  In the table below, I have assembled the fifteen instances in which the VIX has been up at least seven days in a row and have calculated the mean and median performance in the VIX for seven different intervals ranging from one day to 100 days.


[source(s):  CBOE, VIX and More]

Not surprisingly, the mean and median performance of the VIX following these 15 streaks and 1-100 days is uniformly negative.  The data set includes data from Thursday and Friday, which show increases in the VIX and render the one-day performance relatively weak when compared to the rest of the measurement periods.  That being said, mean reversion is evident from the first day all the way through the five-month period that forms the most distant measurement date in this table.

Once again, these findings are consistent with dozens of similar tables presented in these pages over the years that show fading a VIX spike is, on average, an excellent trade opportunity, assuming elevated levels of volatility will persist.

Returning to theoretical territory, if you think about it, what is the type of environment that is likely to cause the VIX to move higher every day for a week and a half or so?  Typically it is event risk in the form of a known event on the calendar that investors obsess about and become increasingly anxious about as it draws ever nearer.  Think of Fed meetings (The VIX and the Pre-FOMC + Post-FOMC Trades), Greece’s elections or key Parliament votes, Congressional votes related to the fiscal cliff, etc.  [See A Conceptual Framework for Volatility Events for more background and context.]

Contrast fretting about the scheduled event risk with something that comes out of nowhere, like the yuan devaluation, Ebola virus, Fukushima, various terrorist incidents and even the Arab Spring.  These dark gray swans blindsided investors and caused a sudden sharp VIX spike – the kind whose steepness cannot be sustained over the course of 1 ½ weeks.

A Twitter reader asked about volatility crushes and their timing.  In a nutshell, a volatility crush is the opposite of a volatility spike and generally happens after a scheduled event is over.  In addition to the macro events listed above, one often sees a volatility crush following an earnings report.  For reasons discussed in A Conceptual Framework for Volatility Events, a volatility crush is much less likely to occur in the context of an unscheduled event with no notice and an uncertain duration.

Today we saw an excellent example of a volatility crush following the announcement by James Comey that the recently discovered batch of emails contained no new evidence in the Hillary Clinton private email server case, reaffirming that there would be no criminal charges against Clinton.  Front month (November) VIX futures are down 12% on this news.

For those who may be interested, you can always follow me on Twitter at @VIXandMore

Related posts:



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

How to Play a Volatility Spike (Guest Columnist at Barron’s)

Yesterday, I was pleased to once again have an opportunity to pen a guest column for Barron’s, pinch hitting for Steve Sears in his The Striking Price column with How to Play a Volatility Spike.  If my math is correct, this is the nineteenth time I have been a guest columnist in this fashion.  I always try to keep my subject matter linked to current events, but the irony is that when the signal comes to grab my bat and make my way to the on-deck circle, invariably the markets are hit with a bout of volatility.  The result is that as a “volatility guy” I often end up talking about what to do in an environment of elevated volatility, as was the case in Seizing Opportunity from Stock Market Volatility, Be Greedy While Others Are Fearful, Calm Down and Exploit Others’ Anxieties and There’s Opportunity in Uncertainty.

My thesis in the Barron’s article is fairly simple and should not come as a surprise to those who have been paying attention to what I have been saying in this space over the course of the past decade:  VIX spikes are typically a gift from the mean reversion gods.  The trick, of course, is in the timing of the mean reversion – and perhaps whether to bother to make the distinction between mean reversion and median reversion.

In the chart below, one can see VIX data going back to 1990, with the mean of 19.71 added as a dotted red line.  Even a quick glimpse at the graphic reveals just how difficult it is for an elevated VIX to stay elevated, regardless of the cause.


 [source(s):  StockCharts.com, VIX and More]

The Barron’s article talks about some trading opportunities that arise from VIX spikes and includes a bull put spread trade idea involving QQQ

I have remarked on a number of occasions in the past that whenever Steve Sears reaches out to me to pen a guest column, inevitably some invisible market force snaps to attention and arranges for a volatility spike.  Either Steve has some amazing insight into the future of volatility (not unthinkable for a guy who was a driving force behind the creation of the ISEE Index) or some other unseen forces are toying with me.  If this happens one more time I am going to start to wonder if I have an obligation to publicly disclose future column requests…

In the meantime, tune out as much of the election hysteria as you can and consider all the gifts from the mean reversion gods that looked too risky to accept at the time.

Related posts: 

A full list of my (19) Barron’s contributions:





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

Wednesday, November 2, 2016

VIX Median Reversion and Five-Year Moving Averages

When people talk about the VIX you often hear them refer to mean reversion, which refers to the tendency of the VIX to be pulled inexorably in the direction of its long-term mean.  With 27 years of data from the CBOE (including some historically reconstructed data), it is possible to calculate the lifetime VIX mean, which happens to be 19.71 at the present.

As an options trader, however, I am wary of giving too much weight to outliers when it comes to predicting the most likely outcome in another options expiration cycle or two.  For this reason, I am more interested in knowing the lifetime VIX median, which is only 17.84.  The median is the 50th percentile while the mean just happens to be in the 60th percentile.  The discrepancy is due to the fact that VIX values are not normally distributed.  Instead, VIX values exhibit a positive skew (a topic for a future post), due to the fact that there are a handful of VIX historical extremes in the 50s, 60s, 70s and 80s.  Meanwhile, the middle 50% of VIX values (the 25th to 75th percentiles) range from 14.04 to 23.98.

So…if the VIX median is so important, why is it that we never hear about it or about median reversion?  Good question.  I touched on that subject on “Drilling Down on VIX Mean Reversion” in the January 2013 issue of Expiring Monthly:  The Option Traders Journal.  As I see it, anyone who is focusing on means in a skewed distribution is necessarily assuming a normal distribution and statistics related to normal distributions when no such distribution or relevant statistical analysis exists.

I mention all of this because yesterday was one of those periodic bursts of activity for me on Twitter.  In some Twitter conversations, we discussed the median VIX vs. the mean VIX and there was a request for a chart of a five-year moving average of the median VIX.  Since I have never seen such a chart – or any VIX median chart for that matter – I present below a chart of the five-year moving average of the median VIX, using data going back to 1990. 



 [source(s):  VIX and More, CBOE]

Note that the current five-year median VIX is 15.07, while the five-year mean VIX is 16.63.  For the full history of the VIX, going back to 1990, the lifetime median VIX is 17.84, 9.5% below the lifetime mean VIX of 19.71.  What does all this mean?  Mostly that one should be careful using statistics that are associated with a normal distribution when analyzing the VIX.  Perhaps more importantly, VIX traders should also think at least as much about median reversion as mean reversion.

As an aside, while I have not been active on the VIX and More blog as of late (this is about to change soon, starting today), I have been active in various other media incarnations.  Last Friday, for instance, I was a guest on the Volatility Views weekly podcast hosted by Mark Longo of The Options Insider.  Tomorrow at 2:00 ET, I will be a speaker on a webinar, Trading VIX to Hedge Market Risks: What You Need to Know, with Tom Lydon of ETF Trends, Greg King of REX Shares and Vinit Srivastava of S&P Dow Jones Indices.  On the print side, this Saturday I will also be a guest columnist at Barron’s, pinch hitting for Steve Sears.  Last but not least, if you wish to follow me on Twitter, where I have been active for ten (!) years, you can find me at @VIXandMore.

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

Wednesday, May 18, 2016

Economic Data Surprise Index Shows Continued Weakness

Today we get another glimpse into the behind-the-scenes machinations of the “data dependent” Federal Open Market Committee (FOMC) with the release of the minutes from the April 26-27 meeting.

While the Fed has a dual mandate of maximum employment and price stability, lately there has been considerable discussion about the how much the Fed should let global considerations factor into Fed policy.  Clearly, the pace of economic growth in China or the stability of euro zone has a significant downstream effect on economic activity in the United States.  Additionally, with 48% of revenues from the S&P 500 companies coming from international markets, policy formulation in an increasingly interconnected global economy is becoming more complicated with each advance in technology, communications and logistics.

Given this backdrop, just how does the data look?  For the past seven years I have been publishing an economic data surprise index that aggregates U.S. economic data relative to consensus expectations across areas such as employment, the consumer, housing/construction, manufacturing and inflation.  The chart below aggregates data across all these areas and shows data peaking relative to expectations during October 2014.  Since that peak, however, economic data relative to expectations deteriorated sharply, falling to an all-time low during the middle of January 2016 that was matched again at the end of last month. 



[source(s):  VIX and More]

If the Fed is indeed data dependent, then there is no avoiding the conclusion that aggregate data relative to expectations has been a disaster for the past 1 ½ months.  There are some signs of stability forming in the current environment and clearly the strength of the dollar and the price of crude oil will have a great deal to say about economic data going forward.  Then again, international events such as the Brexit vote and the evolution of negative interest rate policies of central banks across the globe may trump all domestic U.S. economic data.

[Readers who are interested in more information on the component data included in this graphic and the methodology used are encouraged to check out the links below. For those seeking more details on the specific economic data releases which are part of my aggregate data calculations, check out Chart of the Week: The Year in Economic Data (2010).]


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Disclosure(s): none

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