Why Bother?

Why try to figure out a way to capture dividends? You make pennies. But they are high quality pennies. Any time you can lock in a return, even a small one, that is very high quality money. We will do the return calculation at the end to see if it is worth it.

Today’s Target

Phillip Morris (PM) goes ex-dividend tomorrow, 24th September, paying $0.58 in a few weeks. So today is the come date. In the money call owners who want the stock will call it away today. And call owners who fear a drop in the value of the call will sell their call today.

So I thought that I would buy stock and sell calls so that the stock would be called away. I would get the time premium in the call. So I need a call that is in the money and has a small enough time premium to make it worthwhile for the call owner to call it away.

Which Call To Sell?

This is the Oct in the money call option chain, 7:30 this morning, before the market opens. (Click on the picture to enlarge.)

The 48, 47, and 46 calls are trading with the dividend payment already accounted for. Notice that their implied volatility is lower than the historical volatility of the stock. The deeper in the money calls’ bid and ask bracket PM’s price, 48.93. they have no time value left. So there is nothing to do here.

Let’s look at the Nov option chain for the in the money calls.  (Click on the picture to enlarge.)

We can make a table to make it easier to compare.

To me, it looks like the Nov calls with strikes 46 and lower will be called away. The 47s and 48s have too much time premium and so are too expensive to be called.

As a further check that this is the baseline implied volatility level, we look at Dec too.  (Click on the picture to enlarge.)

Expected Return

So buying a share of PM and selling a 46 Nov call would cost $48.93 – $3.2 = $45.78 and if it is called away, we make $0.27 time premium. So the return is approximately one half of one percent. The risk is that the stock isn’t called away.

I will report back later.

As noted in the recent tweet, we had a strong downside momentum day on Monday, with Demand closing at 15 and Supply at 214. The general pattern following such downside momentum days is a bounce one day later followed by subsequent weakness, although this pattern is affected by larger-term market regimes.

Given the significant expansion in the number of stocks registering fresh 20-day lows, I went back to October, 2002 (when I first began archiving the 20-day high/low data) and examined what happens in the S&P 500 Index (SPY) after 20-day new lows make a 20-day new high. The next day, SPY averages a gain of .18% (91 occasions up, 59 down), versus no change for the rest of the sample (818 up, 712 down). Five days after a surge in 20-day lows, SPY averages a gain of .47% (88 up, 62 down), versus .02% for the remainder of the sample (836 up, 694 down).

It’s thus not unusual to get a bounce following significant market weakness. I will be watching to see how we trade relative to yesterday’s pivot level, as well as the usual sentiment gauges of NYSE TICK and volume transacted at bid/offer, to gauge the likelihood of sustaining a bounce on the day today. Alternatively, inability to sustain buying sentiment below the pivot would sustain the downtrend.

10:17 AM CT – I added the above chart of the NYSE TICK with a green 20-minute moving average to show how sentiment has weakened during the trading day, with more stocks transacting on downticks than upticks. Noticing that shift was key to anticipating the move below the overnight low. Should we continue with bearish sentiment, the next target would be S1. The fact that it’s taken us a while to even approach S1 suggests to me, however, that we could see more of a range environment, trading back into the overnight range, in advance of the Fed announcement and Wednesday’s numbers. I’m watching TICK closely to handicap that scenario (but not become wedded to it!)

With the S&P 500 Index e-mini futures market (ES) unable to hold above the 915 level stressed last week, we’re now seeing selling toward last week’s lows, continuing the market’s trend shift. We also continue to see intermarket themes dominate: the strong U.S. dollar versus the euro and weak commodities accompanying stock selling. Meanwhile, the Market Delta chart shows that we’re building volume overnight in the 908-909 level, with the volume-weighted average price higher at 911.50. Inability to rally above those levels will target last week’s lows below 900.
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The trade is taken if there is a bearish engulfing candle that is bouncing off of a barrier. They discuss their stop-loss, which is 10 pips (ten hundredths of a cent), that is equal to their target profit. Part 2 is five minutes long.

They further discuss the trade here in part 3, four minutes forty seconds.

The last two minutes they discuss other trade criteria.

Further to this morning’s pre-market post, Volatility in Context with VIX at Pose-Lehman Low, today’s 0.35% drop in the SPX means that it has now been four days since the S&P 500 index has moved more than 0.35%.

The range-bound trading is taking a heavy toll on historical volatility (HV), with today’s action pushing the 10 day HV in the SPX down from 21.54 to 18.10 – the lowest reading since September 3, 2008.

The graphic below attempts to put the current historical volatility levels into the context of the past 2 ½ years. Note that the current 10 day HV of 18.10 fits right in the middle of the range for this measure during 2007 (a year of very low volatility) and the pre-Lehman portion of 2008. In fact, given the recent historical record, I would be quite surprised to see 10 day HV fall any farther than the current level for at least another month or two.

Of course the VIX can continue to decline in the absence of falling volatility, but at some point historical volatility begins to provide some semblance of a floor below which the VIX is unlikely to remain.

On the other side of the coin, investors should also be aware that it has now been 26 sessions since the VIX was above the 35 level. If there is a catalyst (such retail sales numbers, housing data, industrial production statistics, Treasury auction results, the FOMC meeting in two weeks, etc.) that will change the volatility equation, then it is reasonable to look to 35 – not 40 or 50 – as the target for a VIX spike.

Finally, with volatility expectations shrinking almost on a daily basis, those who may be interested in speculative buying VIX out-of-the-money calls might find them a lot cheaper than anticipated – and perhaps a lot cheaper than they will be in another week or two.

[graphic: VIXandMore]

Edge has many fascinating essays written by giants in their field, well worth browsing.

Nassim Taleb wrote Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets and The Black Swan: The Impact of the Highly Improbable.  These first two I have read and found very interesting.  The Edge essay excerpted here  summarizes some points from The Black Swan . Dynamic Hedging: Managing Vanilla and Exotic Options (Wiley Finance) was his first book on managing an options book.

1) Avoid Optimization, Learn to Love Redundancy. Psychologists tell us that getting rich does not bring happiness—if you spend it. But if you hide it under the mattress, you are less vulnerable to a black swan. Only fools (such as Banks) optimize, not realizing that a simple model error can blow through their capital (as it just did). In one day in August 2007, Goldman Sachs experienced 24 x the average daily transaction volume—would 29 times have blown up the system? The only weak point I know of financial markets is their ability to drive people & companies to “efficiency” (to please a stock analyst’s earnings target) against risks of extreme events.

Indeed some systems tend to optimize—therefore become more fragile. Electricity grids for example optimize to the point of not coping with unexpected surges—Albert-Lazlo Barabasi warned us of the possibility of a NYC blackout like the one we had in August 2003. Quite prophetic, the fellow. Yet energy supply kept getting more and more efficient since. Commodity prices can double on a short burst in demand (oil, copper, wheat) —we no longer have any slack. Almost everyone who talks about “flat earth” does not realize that it is overoptimized to the point of maximal vulnerability.

Biological systems—those that survived millions of years—include huge redundancies. Just consider why we like sexual encounters (so redundant to do it so often!). Historically populations tended to produced around 4-12 children to get to the historical average of ~2 survivors to adulthood.

Option-theoretic analysis: redundancy is like long an option. You certainly pay for it, but it may be necessary for survival.

By “optimize” I think Taleb means trying to squeeze all the return out a trade by using maximum leverage – a dangerous practice.

2) Avoid prediction of remote payoffs—though not necessarily ordinary ones. Payoffs from remote parts of the distribution are more difficult to predict than closer parts.

A general principle is that, while in the first three quadrants you can use the best model you can find, this is dangerous in the fourth quadrant: no model should be better than just any model.

How do you know if you are in the fourth quadrant? That is where you are subject to the appearance of Black Swans, rare events that can wipe you out. Since such an event may happen only once every ten years, you don’t know when it can strike. So Taleb never sells naked options. He is always a buyer or spreader.

3) Beware the “atypicality” of remote events. There is a sucker’s method called “scenario analysis” and “stress testing”—usually based on the past (or some “make sense” theory). Yet I show in the appendix how past shortfalls that do not predict subsequent shortfalls. Likewise, “prediction markets” are for fools. They might work for a binary election, but not in the Fourth Quadrant. Recall the very definition of events is complicated: success might mean one million in the bank …or five billions!

The real problem with the analysis of Long Term Capital Management was that they did not look far enough into history. Taleb argues that it doesn’t matter how far back you look, it will not tell you the limits of possible events in the future.

4) Time. It takes much, much longer for a times series in the Fourth Quadrant to reveal its property. At the worst, we don’t know how long. Yet compensation for bank executives is done on a short term window, causing a mismatch between observation window and necessary window. They get rich in spite of negative returns. But we can have a pretty clear idea if the “Black Swan” can hit on the left (losses) or on the right (profits).

The point can be used in climatic analysis. Things that have worked for a long time are preferable—they are more likely to have reached their ergodic states.

5) Beware Moral Hazard. Is optimal to make series of bonuses betting on hidden risks in the Fourth Quadrant, then blow up and write a thank you letter. Fannie Mae and Freddie Mac’s Chairmen will in all likelihood keep their previous bonuses (as in all previous cases) and even get close to 15 million of severance pay each.

This is a typical problem of misalignment of interests. Traders at hedge funds or prop desks have an incentive to take large risks since if they win, they get paid big; if they lose, they have to find another job, but the payoff is very asymmetric.

6) Metrics. Conventional metrics based on type 1 randomness don’t work. Words like “standard deviation” are not stable and does not measure anything in the Fourth Quadrant. So does “linear regression” (the errors are in the fourth quadrant), “Sharpe ratio”, Markowitz optimal portfolio, ANOVA shmnamova, Least square, etc. Literally anything mechanistically pulled out of a statistical textbook.

My problem is that people can both accept the role of rare events, agree with me, and still use these metrics, which is leading me to test if this is a psychological disorder.

The technical appendix shows why these metrics fail: they are based on “variance”/”standard deviation” and terms invented years ago when we had no computers. One way I can prove that anything linked to standard deviation is a facade of knowledge: There is a measure called Kurtosis that indicates departure from “Normality”. It is very, very unstable and marred with huge sampling error: 70-90% of the Kurtosis in Oil, SP500, Silver, UK interest rates, Nikkei, US deposit rates, sugar, and the dollar/yet currency rate come from 1 day in the past 40 years, reminiscent of figure 3. This means that no sample will ever deliver the true variance. It also tells us anyone using “variance” or “standard deviation” (or worse making models that make us take decisions based on it) in the fourth quadrant is incompetent.

Kurtosis measure how fat the tails of a probability distribution are. That is, how many large moves does oil or US dollar or a stock make. That 70 – 90% of the kurtosis comes from one day in the past forty years is fascinating. Does it mean that it can be ignored? That the normal distribution is actually better than we thought? Taleb would scream: NO!

7) Where is the skewness? Clearly the Fourth Quadrant can present left or right skewness. If we suspect right-skewness, the true mean is more likely to be underestimated by measurement of past realizations, and the total potential is likewise poorly gauged. A biotech company (usually) faces positive uncertainty, a bank faces almost exclusively negative shocks. I call that in my new project “concave” or “convex” to model error.

Do not confuse absence of volatility with absence of risks. Recall how conventional metrics of using volatility as an indicator of stability has fooled Bernanke—as well as the banking system.

9) Beware presentations of risk numbers. Not only we have mathematical problems, but risk perception is subjected to framing issues that are acute in the Fourth Quadrant. Dan Goldstein and I are running a program of experiments in the psychology of uncertainty and finding that the perception of rare events is subjected to severe framing distortions: people are aggressive with risks that hit them “once every thirty years” but not if they are told that the risk happens with a “3% a year” occurrence. Furthermore it appears that risk representations are not neutral: they cause risk taking even when they are known to be unreliable.

It does sound more frequent to say “3% per year” rather than “once very thirty years”. I had to do the calculation to see that it was the same. P = 3% / year = .03 / 252 = 3 / 25,200 = 3 times in 10 years.

Mergers create special situations for traders.

The premium you get for selling options is larger if the distribution is wider.  The wider the distribution, the more the stock price varies.  So you want to sell the widest distributions only if the risk warrants it.

Two Separate Peaks

Since there is a possibility that the merger doesn’t go through, there are two widely separated peaks to the probability distribution of the stock price.  One peak is centered on the price distribution for BUD as a stand alone company, the other peak is near the price the acquirer is willing to pay for the company.  The purchase price is usually above the market price of the stock, to entice shareholders to part with their shares.

Because there are these two peaks, that makes the option look like an option with a very fat distribution.  That makes the premium very high.  But you are paid for the possibility that the merger doesn’t happen and the target, BUD in this case, falls back to its original price.  Then you will be put with the stock.  If it looks like the merger is going through while your option is decaying you earn a good premium.

So it is safest to do this early in the process, before there might be funding problems or the acquirer feels buyer’s remorse for paying too much.

BUD

On November 4 and 5th I sold some BUD options for $1.45 and bought them back November 12th for $0.55.  This was before I set automatic stops to buy in the options.  Still a good return for a week’s exposure.

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