# [R-SIG-Finance] Random numbers with positive skewness

Mon Jul 20 16:22:25 CEST 2009

```Why not just use the vols you seem to have?
I suppose you could sample from the empirical distribution of the vols,
or you could guess a distribution, say the gamma, estimate the
parameters, and sample from that.
Any skewed density with support the positive reals would probably be as
i.e., equally unrealistic.

David L. Reiner, PhD

-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of James Toll
Sent: Friday, July 17, 2009 10:34 PM
To: r-sig-finance at stat.math.ethz.ch
Subject: [R-SIG-Finance] Random numbers with positive skewness

Hi,

I've been debating how to go about acquiring the historical data
necessary to backtest an indexing idea and I finally decided that
maybe I should first try it out on some randomly generated time series
data. So, for starters, I need to generate 100 time series to
represent 100 different equities, like, for example, the OEX.  To take
the place of 10 years of closing prices, I thought I could simply
generate 2520 price relatives using something along the lines of this:

x <- rnorm(2520, mean = 1, sd = 0.02)

But obviously, it's not likely that each of the components of a cap
weighted index of 100 equities is going to have price relatives with
an SD of 0.02.  So I'd like to be able to vary the SD for each.  I
thought I could just as easily randomly generate a vector of SD's for
use in generating each time series like so:

y<-rnorm(100, mean = 0.025, sd = 0.007)

The problem I'm running into is that when generating the SD's for each
of the 100 time series my wild guess is that the mean might be
somewhere between 0.02 and 0.03, and I think the SD might be somewhere
around 0.007, but I don't think a normal distribution really works at
all.  I think I need a lot of positive skewness to the distribution.

BTW, all of these wild guesses are simply based upon my experience as
an option market maker (which may be worthless to this task), and
there are lots of equities that normally trade between 30 and 50
volatility, but then I've also traded tech stocks with vols around 80
and 90.  So basically I think the bulk of the distribution of SD's is
between 0.02 and 0.03, they taper off on the left side around 0.01,
maybe a little lower, but then on the right side the long tail goes up
to around 0.06.  If my assumptions / conclusions are totally off base
please feel free to tell me.  This is definitely my first attempt at
any kind of backtesting.

Is there some other method of generating random numbers that will
allow me to control the skewness of the distribution?  Thanks.

James

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