inventing Probability Distributions

motivated by email from Peter U.
I've never been enthusiastic about the common assumptions that stock returns are distributed normally or lognormally or ... whatever.
(See this or
this.)
For example, the normal and lognormal distributions look like Figure 1a.
The normal density distribution is described by:
[1]
and the lognormal by:
[2]
Figure 1b gives a fit to S&P 500 returns.
>Looks pretty good to me!
Just wait.
Note that, in the lognormal distribution,
log(x/G) = log(x)  log(G) = log(x)  m,
hence involves a variable of the form: u = (something  m)/S.
>So does that normal guy, right?
Yes. Pay attention.
The cumulative distribution (being the area beneath the density function f(x)),
is always an increasing curve which varies from 0 to 1 (or 0% to 100%).
So here's an interesting idea:
 Let K(u) be an increasing function of u.
 Define z = A + B K(u).
 Pick A and B so z varies between 0 and 1.
 Set u = (x  m)/S
and you've got yourself a cumulative probability.

Figure 1a
Figure 1b

>Example?
Here are a few increasing functions for z :
hyperbolic tangent 
hyperbolic sine 
tangent 
inverse hyperbolic tangent 
inverse hyperbolic sine 
inverse tangent 
logarithm 
logratio 
inverse hyperbolic sine 
... and a few curves of the form
>That inverse hyperbolic sine ... you've got it twice.
So I have. It's a good one. Anyway, here are the curves:
using the hyperpolic tangent
using the inverse hyperpolic sine
using the inverse tangent
>Are these useful?
I have no idea, but if you don't like using a normal or lognormal you might like to use your own invention.
In general, you have a bunch of parameters to select: A, B, m and s so you pick these to fit historical data.
>What's this Johnson stuff?
N.L. Johnson, in 1949, generated a bunch of probability distributions with several parameters (like our A, B, m and s) and ...
>And you want to generate a few yourself, eh?
Well, I'd like to describe the Johnson curves, but first we note the following:
 If z is a normally distributed random variable, then it's described by the function: e^{z2/2}
 If z = (xm)/s, this describes is a normal distribution for the random variable x, with mean m and standard deviation s.
 If z = (log(x)  m)/s, this would give a lognormal distribution for x.
 We note that (xm)/s and (log(x)  m)/s are both increasing functuions of x.
 If we introduce any increasing function we might expect to get some sort of distribution.
We fiddle with a normal distribution to generate various other distributions
>Fiddle?
Yes, we stretch and slide and generally distort a normal distribution, like so:
 First, consider a normal distribution, N, described by:
 Then we introduce a magic function of our choosing (which we'll call J):
z = A + B J(u) where u = (xm)/s.
 If we plot N(z) versus z, we'd get the familiar normal distribution,
but if we plot N versus u we'd get something else
... depending upon our choice of J
 Now consider N versus x (where u is just the xvariable, moved left or right and scaled)
>Examples?
Here's a few where we choose the increasing functions sinh(u), log(u/(1u)) and log(u).
>They're increasing?
Yes. I showed them to you earlier.
The Johnsonmodified normal curves then look like this:
J(u) = inverse hyperpolic sine
J(u) = log(u/(1u)) ... which requires m < x < m + s
J(u) = log(u) ... which requires x > m
Okay, guess what the charts would look like for J(u) = u?
>Uh ... I give up.
J(u) = u ... that's your standard, garden variety normal distribution
Note that, if
z = A + B J((xm)/s)
then, solving for x, we get:
x = m + s J^{1}((zA)/Bs)
where J^{1} is the inverse of the Jfunction.
For example, if J(u) = sinh(u), then we'd get:
z = A + B sinh((xm)/s) and x = m + s sinh^{1}((zA)/B)
The problem, of course, if to identify the parameters A, B, m and s so that whatever curve we choose, it matches
historical data as closely as possible. That usually means matching the first four moments.
>Huh?
We note that, if z is a random variable (running, say, from infinity to infinity) with density distribution f(z),
then the first four moments are given by:
and
and
and
>Is that hard?
We'll see, but in the meantime, here's a fit to 30 years S&P 500 monthly returns:
>What's so good about that?
Compare to a normal distribution fit:
See? The Johnson curve has them fat tails.
To play with these curves, you can fiddle with a spreadsheet which looks like this:
You can type in some stock symbol (a la Yahoo)
... then vary A, B, m and s to see how well you can fit a Johnson (or other)
distribution. (There are up and down buttons to control these values ... to make life easier.)
>Or other?
Yes. You define some J(u), click the button and your J(u) is inserted into the spreadsheet.
You also get the cumulative distribution associated with your chosen J(u), so you can specify some return
and get the probability that the stock return will be less than that.
In the picture above, a 1.5% return is selected. The probability is 98%, using the lousy distribution shown .
It's interesting to see how this changes with your choices of the four parameters !!
After you play with the UP and DOWN buttons, you can click on a couple of buttons and the spreadsheet will attempt to find the
"best" parameters.
>Complicated!
Well ... there's an Explain sheet.
To download the spreadsheet, click on the picture, or, if that don't work, RIGHTclick and Save the target link.
Note: the spreadsheet may be different than shown above
click for Part II
