Posts tagged with volatility

risk, however measured, is not positively related to (rational) expected returns. It goes up a bit as you go from Treasuries, or overnight loans, to the slightly less safe BBB bonds, or 3 year maturities. But that’s it, that’s all you get for merely taking the psychic pain of risk.

Just as septic tank cleaners do not make more than average, or teachers of unruly students do not make more than average, merely investing in something highly volatile does not generate automatic compensation. Getting rich has never been merely an ability to withstand some obvious discomfort.

Two interesting ideas here:

  • "trading time"
  • price impact of a trade proportional to exp( √size )

Code follows:

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I like this concept of “low volatility, interrupted by occasional periods of high volatility”. I think I will call it “volatility”.

Daniel Davies

via nonergodic


(PS: If you didn’t see it before: try plotting this in R:

vol.of.vol <- function(x) {
    dpois(x, lambda=dpois(x, 5)

… and so on, to your heart’s content.


Fun, right?)

Whilst reading John Hempton’s post on shorting $HLF I decided to follow along in quantmod.

Long story short, HerbaLife sells weight-loss supplements through a multilayer marketing business model which Bill Ackman contends is an unsustainable, illegal pyramid scheme. Ackman calls it “the only billion-dollar brand no-one’s ever heard of” and Hempton posts some very unflattering Craigslist marketing adverts:




thus undermining its credibility.

I should mention that I got some internet ads from Pershing Square Capital Management when I googled for herbalife information. In other words the shorts are spreading the word around to the common man to jump on this short! Destroy this pyramid scheme! You could read this as similar to a penny-stock email, but I view it simply as credible self-interest: I already put my shorts on for what I believe are rational reasons. It’s obviously in my self-interest to convince you to do the same but I do in fact believe that $HLF will and should go down and you know I do because I put my money where my mouth is. Whether that’s an ideal confluence of honesty, moral high ground, and selfishness—capitalism at its best—or some overpowerful hedgies using their marketing dollars to bring down a solid company, I’ll leave up to you.


Anyway, on to the quantmod stuff.

Here’s how to generate the 2007–present view:


require(quantmod); getSymbols('HLF'); setDefaults(chartSeries, up.col="gold", dn.col="#2255aa", color.vol=FALSE); chartSeries(HLF)

Now here’s the interesting part.

(…”Ackman” should read “Einhorn” in red there…)

You can notice in red that trades per day (volume) have risen to 10, 20 times normal levels during 2013—which maybe we can attribute to the “buzz” generated by Pershing Square, @KidDynamite, Bronte Capital, and whoever else is calling $HLF a pyramid scheme.

median(Vo(HLF)) tells me the halfway split between “high” and “low” trading volume for this stock. It’s roughly 2 million trades per day. Then with quantmod I can plot those hi-lo subsets with chartSeries(subset(HLF, Vo(HLF)<2e6)); chartSeries(subset(HLF, Vo(HLF)>2e6)) to get a visual on “calm days” versus “heavy days”. That’s something you can’t do with Google Charts.

Here’s calm (under 2 million trades/day)


upper half of heavy trading days (over 2 million/day)


and what I’ll call “pirate days” (over 10 million trades/day)—with plunderers swarming the stock, battling with swords between their teeth


wherein it’s visually clear that very heavy trading skewed blue over gold—i.e. $HLF closed lower than it opened on that day: the heavy trading volume was taking the price downward.

But more precisely what happened on those days? This is a good job for the stem-and-leaf plot. Notice, by the way, that reality here looks nothing like a bell curve. Sorry, pet peeve. Anyway here is the stem plot of heavy trading days:

> hi.volume <- subset(HLF, Vo(HLF)>1e7)
> stem(Cl(hi.volume)-Op(hi.volume))

  The decimal point is at the |

  -14 | 1
  -12 | 
  -10 | 
   -8 | 2
   -6 | 1554
   -4 | 542
   -2 | 430
   -0 | 988761851
    0 | 345667780388
    2 | 058699
    4 | 1
    6 | 5

I love stem plots because they give you precision and the general picture at once. From the way the ink lies you get the same pic as the kernel density plot( density( Cl(hi.volume) - Op(hi.volume) ), col="#333333" , ylab="", main="Volume at least 10 million $HLF", yaxt="n", xlab="Price Movement over the Trading Day"); polygon( density( Cl(hi.volume) - Op(hi.volume) ), col="#333333", border="#333333" )
but you can also see actual numbers in the stem plot. For example the ones to the right of +0 are pretty interesting. Slight gains on many of those pirate days, but not enough to bash back a 14-point loss on a single day.

The classic red/green colouring scheme for trading screens seems too alarmist.

Conceptually, the red/green distinction makes sense as corresponding to stop/go in traffic signals. But traffic signals need to be neon and striking in a hectic 3-D environment where it’s paramount for everyone to definitely not-miss the stop command.

But in a sheltered 2-D environment where goals commonly include to master emotion, to control passive reactivity, to keep a long-term head in the middle of short-term volatility, and to digest (calmly) massive amounts of information en simultáneo, neon red/green seems too grating.

yellow and blue trading screen (GVZ)

I made the above picture with R of course, like this:

    reChart(up.col="light blue", dn.col="yellow")

(GVZ is the gold volatility index.)

It’s not a perfect colour scheme—I would use Lab to do better—but it already improves on #FF0000 versus #00FF00.


One theory of the evolution of trichromacy in primates says that

  • red/green dichotomy tells us whether meat or fruit is rotten or ripe (especially in dappled light)
  • blue/yellow dichotomy tells us how cool/warm something is
  • light/dark (value) is the most basic kind of vision.

If we take that as a starting point, a less alarmist colour scheme for trading software could use the blue/yellow dichotomy to indicate whether a security price went up or down. Use a neutral chroma for “small” moves (this depends upon one’s time-frame, but properly the definition of “big move” should be calibrated to an exponential moving average with some width depending on one’s market telescope). Intensity of the move could be signalled with lightness, so that most figures on a screen are a readable lightness of a neutral colour, but “big moves” are tinged with convexly more chroma and very-convexly more lightness.


The definition of “up/down” might be refigured as whether the trader is short/long the security in question, or perhaps redness/greenness could be used in conjunction with the “market view” of cold/hot, to indicate whether a security is moving for/against one’s strategy. That too could be seen as overly alarming, but a (pseudo)convex coding of red-ness might again solve the problem again, only invoking the “panic mode” when there’s really something to worry about.


This is how much people love to talk about and speculate on $AAPL.

The CBOE puts out a volatility index specifically on Apple stock. (Google has one too.)


XIV does not get you short the VIX. Not even close.

Look at that vol! What is going on, world?

Cities, counties, and states are dangerously close to defaulting on their bonds — so they say.

The problem is widespread because tax revenues (income & even property) are as volatile as the local economy, but spending needs to be stable. Streets must be plowed and fires must be put out regardless of what income levels & property values did last year. Demand for government services ratchets up as well: wealthy people typically want more from their government, and that demand doesn’t decrease when their incomes do.

On the other hand, municipalities can’t really “save up for inevitable hard times because that would amount to extra taxes. Paying to renovate the sewers is valid; holding this year’s residents’ money for seven years and investing it according to the State’s bureaucratic investment criteria is invalid.


But there does seem to be an obvious solution to the problem. If you can’t save, buy insurance. And if you can’t buy insurance, hedge. States could lever up a short against themselves or against things that correlate to high tax revenue for them. If the price is right, then states will pay only a little in good years to cover a lot of loss in bad years.

For example, the State of Oregon could short Portland’s Case-Shiller index, and short stocks of AFMS, Henningsen, LIME Financial, Samaritan, and Walsh Construction— or any correlate of tax revenue. The State could also be direct: call up a bank and ask them to write a swap directly on tax revenue outcomes. We all know the bulge bracket loves to synthesise new products to sell.

The state in question could buy enough “insurance” to mitigate e.g. 70% of revenue loss under a pessimistic scenario, or choose the amount of insurance by another benchmark.

We know there is a long side to this bet because

  1. people buy municipal bonds
  2. people buy and improve houses where they live
  3. people are always trying to make more money

Insurance is justifiable to taxpayers where government “savings” are not. In my experience watching a liberal city government’s debates, “insurance” is a trump: it’s seen as “something you must have”. Even if that view were uncommon, after seeing California declare a literal emergency because of its budget problems, surely voters would agree there is a real danger to insure against.


Am I missing something here? Or are future State fiscal-ratchet problems actually preventable?