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Showing posts with label mean reversion. Show all posts
Showing posts with label mean reversion. Show all posts

Thursday, February 25, 2021

The Evolution of the VIX (1)

 
Volatility is notorious for clustering in the short-term, mean-reverting in the medium-term and settling into multi-year macro cycles over the long-term.  I have chronicled each of these themes in this space in the past.

Apart from volatility, I have also taken great pains to talk about the movements of the VIX, which is one of the most famous instances of implied volatility and represents investor expectations about future volatility in the S&P 500 Index for the next thirty calendar days.  Surprising to some, the VIX and volatility (which generally refers to realized or historical volatility), while correlated, are very different animals.  Not only are these two very different, their evolutions have been very different as well.  Volatility, which has a much longer history, seems to exhibiting the same traits that it has exhibited throughout its lifetime, with relatively modest tweaks around the edges from time to time.

The same cannot be said for the VIX.  One thing about the VIX that has changed in the three decades or so of VIX data is the speed at which the VIX has moved up and down.  In a nutshell, VIX cycle times have shortened dramatically.  In other words, the VIX now has a tendency to spike much faster and mean-revert downward much faster as well.  This phenomenon has been ongoing for the past decade or so, but it became more pronounced following the Brexit craziness – or at least the first chapter of the Brexit craziness.

One way you can see how the changes in the VIX have differed from the changes in the volatility of the SPX is to look at volatility spikes.

In the first graphic, below, I show the number of days per year with 2% and 4% moves in the SPX going back to 1990.  Take note of the ebbs and flows in volatility and the clustering of volatility around the dotcom bubble and again around the 2008 Great Recession.

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

In the second graphic, I plot annual VIX spikes of 20% or more for each year going back to 1990.  Note that while visual inspection does not reveal any obvious trend in the SPX volatility data, the VIX spike data for the same period show a pronounced upward trend, reflecting the heightened sensitivity of the VIX to changes in volatility of the SPX.  In other words, even though volatility may be the same, the VIX is becoming more sensitive to volatility.  Another example that supports this point:  of all the one-day spikes in the VIX of 30% or more, 71% have happened in the past decade and only 29% are from the previous two decades.  The volatility landscape may or may not be changing, but the VIX is.

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

Further Reading:
Clustering of Volatility Spikes
Putting Low Stock Volatility to Good Use (Guest Columnist at Barron’s)
What My Dog Can Tell Us About Volatility
My Low Volatility Prediction for 2016: Both Idiocy and Genius
What Is Historical Volatility?
Calculating Centered and Non-centered Historical Volatility
Rule of 16 and VIX of 40
Shrinking VIX Macro Cycles
Chart of the Week: VIX Macro Cycles and a New Floor in the VIX
The New VIX Macro Cycle Picture
Recent Volatility and VIX Macro Cycles
VIX Macro Cycle Update
Was 2007 the Beginning of a New Era in Volatility?
VIX Macro Cycles
Last Two Days Are #5 and #6 One-Day VIX Spikes in History
2014 Had Third Highest Number of 20% VIX Spikes
Today’s 34% VIX Spike and What to Expect Going Forward
All-Time VIX Spike #11 (and a treasure trove of VIX spike data)
The Biggest VIX Spike Ever: A Retrospective
VIX Sets Some New Records, Suggesting Volatility Near Peak
Highest Intraday VIX Readings
Short-Term and Long-Term Implications of the 30% VIX Spike
VIX Spike of 35% in Four Days Is Short-Term Buy Signal
VXO Chart from 1987-1988 and Explanation of VIX vs. VXO
Volatility History Lesson: 1987
Volatility During Crises
Chart of the Week: VXV and Systemic Failure
Forces Acting on the VIX
A Conceptual Framework for Volatility Events

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

Disclosure(s): short VIX at time of writing

Monday, February 27, 2017

Ten Years Since the Biggest VIX Spike Ever

Ten years ago today, we witnessed that largest one-day VIX spike in the nearly three decade history of the VIX.  On that day, the VIX rallied from a prior close of 11.15 to 18.31 – a 64.2% gain.  The move came in conjunction with a 3.5% decline in the SPX (large, but nothing like what would follow during the next two years) and followed overnight concerns related to the Chinese government raising interest rates to discourage speculation.  The fears in China were largely responsible for a 8.8% loss in the Shanghai Composite Index and a 9.9% loss in the FTSE/Xinhua China 25 index that is the basis for the popular Chinese ETF, FXI.

In retrospect, the biggest VIX spike of all was a short-lived phenomenon whose fundamental and technical underpinnings turned out to pose no lasting threats.  As is often the case, traders who faded this move (and keep in mind there were no VIX ETPs available at that time) and bet on mean reversion cleaned up on that trade.

So, did this move in 2007 provide a hint as to what would follow in 2008?  As I see it, the timing was merely a coincidence.

It may not be a coincidence, however, that the biggest VIX spike in history helped to usher in the golden era of VIX spikes, with 15 of the top 22 one-day VIX spikes of all time having occurred during the past decade, as is reflected in the graphic below.  Of course, most of the spike in VIX spike activity was the result of the Great Recession and some of the “disaster imprinting” that followed such a severe shock to many investor psyches.

[source(s): VIX and More]

Some may look around at a VIX that is not too much different now than it was a decade ago and wonder what it might take to trigger another 64% jump in the VIX.  Certainly there is a huge policy uncertainty overhang at the moment, lots of political (and related economic) uncertainty in Europe and there are always some black swans lurking just out of our sightlines.

For now, however, will just have to live with that eerie, unsettling feeling that often accompanies low volatility and wait for another bump in the night before we reassess the volatility landscape.

Further Reading:


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


Disclosure(s): none

Monday, February 20, 2017

SPX 1, 2 and 3-Year Returns Following Top and Bottom Five (and Ten) VIX Average Annual Readings

On Saturday, I posted Putting Low Stock Volatility to Good Use (Guest Columnist at Barron’s) and used that opportunity to expand upon some of the points I raised in my February 18th column for Barron’s.  Specifically, I addressed the issue of the clustering of low volatility and used a graphic to show that when the VIX closes below 12, it tends to persist in these low readings, clustering for several years, before remaining above 12 for even longer periods during high volatility regimes.  

Another claim I made in the Barron’s article (Putting Low Stock Volatility to Good Use) that I thought might benefit from a little graphical support was my contention:

“VIX data suggests the low volatility provides a foundation for extended bullish moves in stock. Look at the five highest and lowest average annual VIX readings and calculate the performance of the Standard & Poor’s 500 index one, two, and three years after the VIX extremes. After one year, the S&P performance following the low VIX is about 20% higher than after the high VIX. For two years, the difference jumps to 40% and by the third year the cumulative performance differential is approximately 90%. Wariness aside, low volatility begets low volatility and is generally bullish for stocks.”

Now there are two ways to compare percentages and the best way for me to illustrate this is with an example.  If we are comparing 5% with 4% is the 5% value 25% higher than 4% or is it 1% higher?  You can make a case for either comparison, one of which is made with division and is more of a pure percentage calculation, while the other which is made by subtraction and is perhaps best thought of in terms of percentage points.  In the Barron’s article, I used the division/percentage method, which is the norm when comparing numbers that are not percentages in and of themselves.  This time around I will try to minimize confusion and use the subtraction/percentage points approach instead.

In the first of the two graphics below I have calculated the SPX 1-year, 2-year and 3-year returns following the years with the five highest average VIX values (2008, 2009, 2002, 2001 and 1998) with a dashed black line as well as the years with the five lowest average VIX values (1994, 1993, 2006, 2005 and 1994) with a solid double blue line.  In all three time frames, the better returns followed the lower VIX readings and I used a green area series to show the (percentage point) difference.

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

For comparison purposes, in the second graphic below I have plotted the same SPX 1-year, 2-year and 3-year returns following the years with the ten highest average VIX values as well as the years with the ten lowest average VIX values.  Once again, in all three time frames, the better returns followed the lower VIX readings, though in this instance the performance gap between the lower VIX readings and higher VIX readings is somewhat reduced.

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

I offer up these graphics because I maintain that there are many skeptics regarding not only the persistent clustering of low VIX readings, but also related to the lack of robust data showing the effect of mean reversion during low volatility regimes.  As I have noted previously, mean reversion is much more predictable and tradeable following a VIX spike than after a significant decline in the VIX.

Follow me on Twitter at:  @VIXandMore

Related posts:


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

Saturday, February 18, 2017

Putting Low Stock Volatility to Good Use (Guest Columnist at Barron’s)

With spring training just getting underway in Florida and Arizona, I think it is appropriate that I once again have an opportunity to pinch hit for Steve Sears in his The Striking Price column for Barron’s.  Today’s column is called Putting Low Stock Volatility to Good Use (my title suggestions always seem to end up on the cutting floor) and builds upon some of the ideas I presented three years ago in Low Volatility:  How to Profit from a Quiet VIX.

If my memory is correct, this is the twentieth time I have been a guest columnist at Barron’s in this fashion and in keeping with tradition, I always try to make the column topical, particularly when there are some aspects of volatility that have investors more perplexed than usual.  Lately, it has been the persistent low VIX readings (including the first sub-10 VIX print in a decade) in conjunction with a new administration and extremely high policy uncertainty that has been difficult for investors to digest.  While I too have dedicated a fair amount of effort to square low volatility with high policy uncertainty, my research related to volatility has made it easier to stick with the trend instead of trying to anticipate a market turn.

Specifically, in the Barron’s article I note:

“Statistically, it turns out that the vaunted mean-reverting aspect of volatility is much more likely to kick in with a high VIX than a low VIX. Similarly, low volatility tends to cluster and persist for extended periods, defying skeptics. Specifically, when the VIX dips below 12 for several months, the historical record shows it can be expected to continue with similar readings for two years or more.”

As Barron’s is not necessarily the best place to try to shoehorn original research into a short column, I thought I could use this space to expand upon some of the points I made.  Specifically related to the clustering of low volatility, the graphic below shows that when the VIX closes below 12, it tends to persist in these low readings, clustering for several years, before remaining above 12 for even longer periods during high volatility regimes. 

[source(s):  CBOE, VIX and More]

A corollary to the above is that while investors often focus a good deal of their VIX analysis on mean reversion, it is important to note that mean reversion is much more predictable and tradeable following a VIX spike than after a significant decline in the VIX.

There are some other interesting statistics and ideas in the Barron’s column that I will address in other posts shortly, not the least of which addresses the performance of the SPX in the years following extreme high and extreme low VIX readings.  Stay tuned.

Finally, since I enjoy being a pinch hitter so much, I thought I might highlight one pinch hitter for every new Barron’s column I write.  This time around I’d like to put the spotlight on Rusty Staub, who just happened to be at the zenith of his pinch-hitting duties when I moved to New York.  In the twilight of his career, the charismatic Rusty tied a National League record in 1983 with eight consecutive pinch hits and also tied the Major League record with 25 RBI from those (24) pinch hits.  Rusty finished his career with exactly 100 pinch hits and is currently 19th on the all-time pinch hit list.  I realize I have a long way to go to get to Rusty’s rarefied air, but 100 pinch hits is something to shoot for.

Related posts:

A full list of my (20) Barron’s contributions:





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

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.

Related posts:


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

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

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.

Related posts: 



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

Monday, August 24, 2015

Last Two Days Are #5 and #6 One-Day VIX Spikes in History

Many readers have commented that one of their favorite of my regular graphics is the table of VIX spikes of 30% or more that I update periodically in this space, along with the subsequent performance in the S&P 500 Index following these spikes.

This time around I have elected to add an additional column that identifies the catalysts involved (necessarily a subjective process) in each instance. When thinking about these catalysts, it might be helpful to compare the nature of the threat and the size of the VIX spike to changes in volatility during various high-profile historical events, an analysis I captured in Volatility During Crises. Another useful exercise is to think about the fundamental factors influencing each VIX spike in the context of A Conceptual Framework for Volatility Events, which I find particularly useful in helping to gauge just how large of a VIX spike a certain type of event might trigger.

Of course the table below has its own set of data nuggets, both fundamental and technical. One interesting statistic I find worth highlighting is the relatively high frequency of large VIX spikes that have occurred during the past five years. VIX data goes back 26 years and yet more than half of the VIX spikes in this table data are from the past five years. I think it is no coincidence that the VIX ETPs (initially VXX and VXZ) were launched in 2009 and the inverse VIX ETPs (XIV and ZIV) and leveraged VIX ETPs (starting with TVIX) were launched in the following year, when big VIX spikes suddenly became more common – much more so than during the 2008 financial crisis, the dotcom crash, etc. For additional information on the subject of more VIX spikes in spite of a generally lower volatility environment, check out 2014 Had Third Highest Number of 20% VIX Spikes.

History of 30 pct VIX Spikes w Catalysts 082415

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

As noted previously, based on the data for all VIX spikes in excess of 30%, the SPX has a tendency to outperform its long-term average over the course of the 1, 3 and 5-day periods following the VIX spike. Also worth noting that that 10 and 20 days following the VIX spike, the SPX has a tendency not only to underperform, but to decline. Further, while the huge decline following 9/29/08 VIX spike tends to dwarf the other data points, even when you remove the 9/29/08 VIX spike it turns out that the SPX still loses money in the 10 and 20-day period following a VIX spike. When the analysis is extended out 50 trading days, the SPX is back to being profitable, but performing below its long-term average. On the other hand, when the analysis includes 100 days following the VIX spike, the SPX is back to outperforming its long-term average.

In summary, this data suggests that following a 30% one-day VIX spike, there appears to generally be a tradable oversold condition in stocks that lasts approximately one week, followed by a period of another month or so in which the markets typically has difficulty coming to terms with the threat to stocks. This tendency makes today’s market action even more remarkable in that today was by far the worst performance of the SPX in a day following a 30% VIX spike.

Taking a longer-term perspective, looking out at least one quarter, all fears are usually in the rear view mirror and stocks are likely to have tacked on significant gains.

As noted many times here in the past, the data in this table supports the idea of both short-term and longer-term mean reversion, but calls into question the role of mean reversion in the 10-20 days following a VIX spike, where fundamental factors have a tendency to overwhelm a technically oversold condition in stocks.

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Disclosure(s): short VIX at time of writing; the CBOE is an advertiser on VIX and More

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