RE: Understanding Price projections using Std Dev.

["Phil Bailey" <baileyp@xxxxxxxxxxx>]

Barry,

I'll try to offer some direction although I don't use OptionVue and
I gave up on options a long time ago since the brokers have a 'floor trader'
biased commision.

Since a standard deviation is ASSUMING a Normal Bell shape distributed
function,
all other effects would have to be compensated for and adjusted in a derived
probability function.
The good news is all other distribution types are converted through standard
deviation formulas,
since that's all we truely understand. The bad news is it gets much more
complex and probabilities lose
the favorable Normal-probability small sample-size edge. Complicated
projections slip back into
binomial probabilities (non-parametric statistics): which implies a much
greater sample size for confident projections.

Factors would include time to expiration, trends in all time frames,
and even Presidential 4 year cycles. Option statistics are extremely
complex. Like all trading education,
there are volumes written on coincidental manifistations of a single
misguided individual.
Just all the n to the x option factors will fill up a 500 page volume that
tells you absolutely nothing.

I would seek out the best literature/information on the subject matter,
print the various option data, and then try
to form your best model. And it won't be a simple std dev model by itself.
Options are one of the most complicated
and manipulated medias to trade with there are. Good luck.


Phil


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To: <omega-list@xxxxxxxxxx>
Subject: Understanding Price projections using Std Dev.
From: "Barry Silberman" <Barry@xxxxxxxxxxxxxxxxx>
Date: Sat, 12 Mar 2005 10:04:38 -0500
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As is my normal state of affairs, I am confused about price projections
using Standard Deviations.  Perhaps those with a much more extensive
knowledge of statistics could shed some light.

Below is an indicator I wrote that a helpful fellow from EasyLanguage
consulting said was accurate, but I would like to understand the concept
better.

I was trying to duplicate an indicator on OptionVue that represents the 1
standard deviation of price applied to a risk graph.  It represents the 68%
likelyhood that price will stay within certain upper and lower boundaries
during the life of the option.   I have been told by OptionVue that they use
implied volatility and days to expiration in their calculation.

My indicator does not use "days to expiration" in any way.

So my questions are basically:

1-  when one is looking for the upper and lower range that represents a 68%
probability that price will stay within that range, must you consider a time
frame
     (namely "days to expiration") for that answer.    Or, when one
determines the 68% probability price will stay within a range of two values,
that determination
     says nothing about a time frame for which that projection is considered
to be accurate?

2-  is it more theoretically correct to determine a std deviation using say
the last year's worth of price data under the theory of "reversion to the
mean," rather than a
     short term measure of volatility.  Specifically, if one was interested
in say a 30 day option, would one be more correct in using a long-term or
short-term
     measure of volatility?

If the more accurate method of determining probability of price projections
does require an input for "days to expiration," could someone suggest the
change in code that would be required.

Thanks for any help.

Here is the code:

inputs: Price( Close ), Length( 250 ), NumDevsUp(1 ), NumDevsDn( -1 );
variables: SDev(0), LowerBand(0), UpperBand(0) ;

SDev = StandardDev( Price, Length, 1 ) ;
UpperBand = close + NumDevsUp * SDev ;
LowerBand = close + NumDevsDn * SDev ;

if CurrentBar > length then begin
 Plot1( LowerBand, "LowerBand" ) ;
 Plot2( UpperBand, "UpperBand" ) ;
end;