The CBOE publishes a SKEW index, which is
SKEW = 100 - 10*S, so from the index itself we can get
S = (SKEW - 100)/10.
I just want to do some preliminary analysis of distributions using
I have this python code from another SO question:
from scipy import linspace from scipy import pi,sqrt,exp from scipy.special import erf def pdf(x): return 1/sqrt(2*pi) * exp(-x**2/2) def cdf(x): return (1 + erf(x/sqrt(2))) / 2 # e = location # w = scale def skew(x,e=0,w=1,a=0): t = (x-e) / w return 2 / w * pdf(t) * cdf(a*t)
Can I get a Distribution using this skew parameter? The wiki page mentions the skew variable has to be in the range (-1,1).
Edit: I just needed to read the scipy.stats package more closesly -- it's well documented what shape, location, and scale are required for each distribution.
Edit 2: If SKEW is the 3rd statistical moment, VIX is the variance, what probability distribution can be completely specified by these two parameters? The lognormal is completely specified by variance and location. What are the alternatives? Can I parameterize the lognormal with these two distributions?