Archive for the ‘Probability’ Category

Realized Volatility and Market Behavior Since 1901

Friday, August 12th, 2011

Volatility seems high… extremely high. But is it really that high compared to a complete history?

IMAGE 20110811RVALL Realized Volatility and Market Behavior Since 1901

As everyone on earth is probably aware, volatility has been surging lately. Realized market volatility numbers are in the stratosphere, or at least it seems that way. To see how high volatility has been compared to history, I looked for periods when realized volatility of the Dow Jones Industrial Average has been as high, or even higher than the level it closed at on Thursday.  I chose realized volatility instead of implied volatility, because implied volatility has a limited history.  For instance, although I consider the VIX a very important indicator of future volatility expectations, it is not that valuable in severe market Crashes.  That’s because, fortunately, we haven’t had that many (1987, 2000-2002 and 2008-2009).  We just haven’t had enough extreme declines during VIX’s life to have a statistically significant data set from which to work.  Using realized volatility instead of VIX allows us to do testing going back over 100 years.

What I found was a mixed picture. When actual market volatility (as measured by the one-month standard deviation of log returns of the daily closing DJIA values) gets this high, more often than not the market makes a low or a short-term market bottom. The problem is, when you get extremely high volatility, sometimes the resulting market behavior can be very alarming.

Let’s look at some charts of DJIA (orange line) to visually analyze the period from from 1901 to present day. Periods where volatility was greater than or equal to Thursday’s closing volatility level are marked by yellow vertical bars.

The chart below, which dates from 1901 to 1919 shows where high volatility successfully coincided with a major market low in late-1903, late-1908 and right after the inception of World War I in 1915.  There were also period of high volatility in 1901, early-1907, and 1917. The problem with those instances is the market did not rebound (although it did stabilize for several months after each signal).

IMAGE 20110811RV1901 Realized Volatility and Market Behavior Since 1901

Click image to enlarge

The next chart dates from 1929 to 1946. The thing that should be obvious is that during the entire Great Depression drop from late-1929 through the first half of 1932, and the subsequent rebound from late-1932 through 1933, volatility was persistently higher than it is right now. The same thing happened on a smaller scale in 1938, 1939 and 1940.  During this time frame, extremely high volatility was not a sign that the market would rebound or that volatility would subside.  When volatility got high, it got even higher.  When the market fell a lot, it fell even further.

IMAGE 20110811RV1929 Realized Volatility and Market Behavior Since 1901

Click image to enlarge

The chart below which dates from 1961 to 1989 shows the typical pattern of high volatility coinciding with a market bottom held — right after the Crash.  The other thing to notice is how volatility spikes were non-existent even during the 1973-74 bear market.

 IMAGE 20110811RV1961 Realized Volatility and Market Behavior Since 1901

Click image to enlarge

During the period from 1997 to 2011, the pattern of extremely high volatility coinciding with market bottoms held.  In August 1998 during the Russian debt default (there’s that word debt again), right after the 9/11 attack, and during the Enron hearings in 2002 all had high volatility readings that coincided with significant stock market bottoms. The very notable exception was in 2008, where volatility rose significantly, but the stock market continued lower.

IMAGE 20110811RV1997 Realized Volatility and Market Behavior Since 1901

Click image to enlarge

The conclusion to this historical analysis is that when volatility reaches the level we’re seeing now, more often than not, a market low is in place.  Unfortunately, when the market does not turn around, the drop can be catastrophic, as in 1929-1932 and in 2008.

In other words, high volatility usually coincides with a market bottom, except when it doesn’t, and then the decline is REALLY BAD!

As far as which version we’re likely to experience during this turbulent time, the way I’m going to play it is to sell some volatility in here.  But when I do, I’m going to use the following risk strategy: my allocation is going to be even lower than the already low level it was going into 2011, and my stops are going to be wider than normal.

– Don

New Research Finds That Index Options Are More Expensive Than Corresponding ETF Options. What Does Don Think?

Wednesday, July 20th, 2011

There was an interesting discussion about new research in The International Journal of Business and Finance Research that made the rounds in the blogosphere last week here and here. The study looked at the difference in implied volatilities of index options compared to the implied volatilities of the options on the corresponding ETFs.  For instance, the authors compared the implied volatility of NDX options to implied volatility to QQQ options.  They did the same thing for SPX to SPY and DJX to DIA.

The researchers found that, during the period from 2003 to 2006, the implied volatility of the ETF options had a tendency to be slightly higher than the corresponding index options.  Delving deeper, they found that the deep-in-the-money options had the greatest discrepancy.  They also noticed that the discrepancy was most pronounced in the NDX to QQQ relationship.  Based on these two facts, deep-in-the-money QQQ options are the most overpriced relative to their comparable index options.

The researchers listed some possible reasons for the difference, such as bid-ask spreads and the net buying pressure argument.  They also said it may be that ETF options are simply more attractive instruments for hedging and speculation.

What caught my eye, however, was the first sentence of the second paragraph in their conclusion: “Because the underlying return distributions are the same…”

That statement piqued my interest because, believe it or not, the investigation of return distributions and their discrepancies from theoretical expectations just so happens to be what I specialize in!

So I decided to take a look at the paper a bit more deeply, to see if I could find a reason why the ETF options were more expensive than their index counterparts during that 4-year time frame.  I just completed the analysis yesterday.

The first thing I noticed, which was obvious to others besides me, was that the time period was very limited: 2003 to 2006.  Anything can happen in such a small sample.  More important, during that four-year period, volatility — both realized and implied — went in one direction: down.  VIX fell from a peak of 35 to less than 10 with just a couple of blips up.

Another possible factor was and still is the inherent difference in the way cash-settled index options and ETF options are settled.  ETF options are physical delivery, meaning that if you have an in-the-money option at the close on expiration Friday, you’re going to get a long or short position in the ETF shares, depending on whether the type of option you traded was a call or put and whether you bought the option or sold it.

Index options are completely different.  Their settlement is in cash.

But there are two other very important differences:

  1. Index options like those traded on the NASDAQ 100 Index stop trading the Thursday before expiration Friday.  Their settlement price is determined based on Friday’s open.  ETF options like those on the QQQ don’t stop trading till Friday’s close.  So ETF options have almost an entire extra day of action that index options don’t have.  Could that extra day be the source of the added volatility?
  2. Another thing that is unusual about index options is the way the “open” is determined.  It’s not the opening print of the index itself.  Instead, tt’s a settlement value calculated based on the opening prints of the constituent securities in the index.  These settlement values have their own prices and ticker symbols.  For instance, the ticker symbol for the settlement value of NASDAQ 100 Index options is NDS, not NDX.

The net result of these differences indicate that, perhaps, comparing the return distribution of NDX to QQQ may not have been the proper comparison, at least on options expiration day.  It could be that the return distributions on expiration day are vastly different … different enough to explain the implied volatility discrepancies.

To investigate this, I checked to see if there was a significant difference in the return distributions of NDS to NDX on expiration Friday.  [Because the researchers found no significant difference between NDX and QQQ, I only compared NDS to NDX and did not compare NDS to QQQ.]  I used the period in which the implied volatilities were compared — from 2003 to 2006 — for the initial analysis.  You can see the distribution comparison below.

 IMAGE NDS20032006 New Research Finds That Index Options Are More Expensive Than Corresponding ETF Options. What Does Don Think?

Notice any difference?  The biggest difference is that NDX has many more large moves than NDS on expiration Friday.  There are far fewer blue bars at the extreme left or right.  That tells us the return distributions are different — at least on expiration Fridays during that time period.

But what about a more complete data set?  After all, the analysis above is based on only 4 years of data during a period where realized volatility did nothing but drop.  To get a more complete data set, I added data going back to March 1999 through 2006.  This represents the entire time frame in which the study’s index data was measured.

IMAGE NDS19992006 New Research Finds That Index Options Are More Expensive Than Corresponding ETF Options. What Does Don Think?

As you can plainly see, at least on options expiration day, the return distributions show a very significant difference.  The close-to-close distribution (NDX) has far more large moves than the close-to-open distribution (NDS).  In fact, the NDX column to the extreme left is taller than any other NDX column.  Relative to NDS, NDX has extremely fat tails on expiration day.

Is that the source of the higher implied volatility for index options compared to ETF options?  I think so.  But it remains to be answered definitively.  The purpose of this post is not to provide a final answer.  Instead, I am posting this to generate ideas for future research.

For instance, one idea that popped into my mind this morning comes from a concept that I (along with nearly every professional options trader) was well aware of back in the 1980s and 1990s: when evaluating index options, you should never be using the index itself.  Instead, you should be using the device that traders use to hedge their options bets.  In the case of QQQ options, it’s the QQQ ETF.  For NDX options, however, you can’t hedge with the NDX index itself; you hedge with the NDX futures.  So perhaps investigators should look at the return distributions of NDX futures and NDS instead of NDX.

– Don

P.S. — Here’s the distribution with the complete dataset from April 1998 to current.

IMAGE NDS19982011 New Research Finds That Index Options Are More Expensive Than Corresponding ETF Options. What Does Don Think?

P.P.S. – One other note on future research.  Based on the graph above, I plan on looking at the current situation, rather than the way things were in 2003-2006.  My guess is that the implied volatility of the ETF options may not be as high relative to the implied volatility of the index options as it was in the past.

Implications of Higher Industry Sector Correlation on Options Strategies

Friday, July 15th, 2011

In the stock market run-up that occurred at the turn of the month a couple of weeks ago, we observed very high industry sector correlations.  Correlation is a measure of connectedness.  During the week of the big run up, each of the Select Sector SPDR ETFs moved in the same direction as SPY for 5 straight days.

Historically, it is unusual to get that kind of unbroken string of connectedness for such a long period of time.  And it’s really unusual for it to happen during a significant rally.

The unprecedented correlation has one market analyst nervous.  In a post on one of the Financial Times blogs, the analyst worries that all sectors moving in lockstep will lead to a market decline.  I’m not going to get into the reasoning behind the conclusion.  I’ll let the article speak for itself.

Another study, however, points to a different conclusion.  According to research done by the good folks at Bespoke Investment Group, the recent broad market rally showed the S&P 500 having all 24 industry groups finishing higher on four out of five days followed by the index having two straight days where all 24 declined.

Looking at past data points where these conditions existed, Bespoke found that huge moves in the stock market — either up or down – were the norm.

If Bespoke’s data is correct, it should lead option traders to consider delta neutral strategies, like straddles and strangles. These non-directional type option trades would seem to be a perfect fit, especially as we are in a relatively low implied volatility environment.  Potential for an explosive environment coupled with relatively inexpensive puts and calls spells OPPORTUNITY.

I did a little research myself to validate the conclusions made by each analyst group.  I found nothing to support the bearish opinion.  But I definitely found something regarding Bespoke’s finding that larger-than-normal moves have followed periods of high sector correlation.

The following chart shows the size of the market’s average movement during the period following a string of five days where each of the Select Sector SPDR ETFs moved in lockstep with the market and one another. The x-axis represents the varying degrees of correlation, or connectedness.

IMAGE 20110715CORR Implications of Higher Industry Sector Correlation on Options Strategies

Readings from when the industry groups moved in unison over an extended period are represented in the “Highly Connected” area at the right of the graph. The vertical axis represents the size of the average daily movement during the period following different levels of sector correlation.

Without getting into too much detail, you can clearly see that as correlation rises (readings on the right side of the graph), the magnitude of the market’s movement rises. What’s important to note is that this measures the movement in the period subsequent to measured correlation. In other words, rising correlation forecasts larger-than-normal price swings in the future.

This confirms what Bespoke found: that when industry groups move together, a stable and sleepy market is probably out of the question. But I also found something else – something completely unexpected.

At the other end of the graph we see a slight uptick. That is, when the connectedness is extremely low, the size of the market’s subsequent price swings tends to pick up. Not as much as when correlation is high. And it’s hard to tell if it’s significant to enough to justify the transaction costs of purchasing straddles.  But there is a tendency that when sectors are completely disconnected, the magnitude of market swings increase and that leads to higher index volatility.

– Don

P.S. – The orange line in the graph is the average of the size of daily market move since the beginning of 1999. The white line is a regression trendline.