Stable Distributions



1.4 Simulation of α-stable variables

1.4 Simulation of α-stable variables

The complexity of the problem of simulating sequences of α-stable random
variables results from the fact that there are no analytic expressions for the
inverse
F-1 of the cumulative distribution function. The first breakthrough
was made by Kanter (1975), who gave a direct method for simulating
Sα (1, 1, 0)
random variables, for
α < 1. It turned out that this method could be easily
adapted to the general case. Chambers, Mallows, and Stuck (1976) were the
first to give the formulas.

The algorithm for constructing a standard stable random variable X ~ Sα(1,β,0),
in representation (1.2), is the following (Weron, 1996):

generate a random variable V uniformly distributed on ( π, π ) and an
independent exponential random variable
W with mean 1;

for α 6= 1 compute:

si{ s    sin{α(v + Bα,β)}

α,β ∙    {cos(V)}1 /α    '

cos{V α ( V + Bα,β ) }

.           W           .

(1 -α )

,

(1.6)

where

arctan(β tan πα )

Bα,β =  ---------------2   ,

α

2    2 παa    1 / (2α)

Sα,β =   1 + β2 tan2 Ç—) I

for α = 1 compute:

X=


πW cos Vλ ɪ
+ βv Л


(1.7)


Given the formulas for simulation of a standard α-stable random variable, we
can easily simulate a stable random variable for all admissible values of the
parameters
α, σ, β and μ using the following property: if X ~ Sα (1 ,β, 0) then

{σX + μ,          α = 1,

(1.8)


σX + 2 βσ ln σ + μ, α = 1,



More intriguing information

1. On Evolution of God-Seeking Mind
2. The Shepherd Sinfonia
3. The name is absent
4. Neighborhood Effects, Public Housing and Unemployment in France
5. Towards Teaching a Robot to Count Objects
6. NATURAL RESOURCE SUPPLY CONSTRAINTS AND REGIONAL ECONOMIC ANALYSIS: A COMPUTABLE GENERAL EQUILIBRIUM APPROACH
7. The name is absent
8. Strategic Investment and Market Integration
9. PRIORITIES IN THE CHANGING WORLD OF AGRICULTURE
10. The name is absent