Geometric stable distribution

Geometric Stable
Parameters	α ∈ (0,2] — stability parameter β ∈ [−1,1] — skewness parameter (note that skewness is undefined) λ ∈ (0, ∞) — scale parameter μ ∈ (−∞, ∞) — location parameter
Support	x ∈ R, or x ∈ [μ, +∞) if α < 1 and β = 1, or x ∈ (−∞,μ] if α < 1 and β = −1
PDF	not analytically expressible, except for some parameter values
CDF	not analytically expressible, except for certain parameter values
Median	μ when β = 0
Mode	μ when β = 0
Variance	2λ² when α = 2, otherwise infinite
Skewness	0 when α = 2, otherwise undefined
Ex. kurtosis	3 when α = 2, otherwise undefined
MGF	undefined
CF	$\!\Big[1+\lambda^{\alpha}\|t\|^{\alpha} \omega - i \mu t]^{-1}$ , where $\omega = \begin{cases} 1 - i\tan\tfrac{\pi\alpha}{2} \beta\, \operatorname{sign}(t) & \text{if }\alpha \ne 1 \\ 1 + i\tfrac{2}{\pi}\beta\log\|t\| \, \operatorname{sign}(t) & \text{if }\alpha = 1 \end{cases}$

A geometric stable distribution or geo-stable distribution is a type of leptokurtic probability distribution. Geometric stable distributions were introduced in Klebanov, L. B., Maniya, G. M., and Melamed, I. A. (1985). A problem of Zolotarev and analogs of infinitely divisible and stable distributions in a scheme for summing a random number of random variables, Theory of Probability & Its Applications, 29(4):791–794. These distributions are analogues for stable distributions for the case when the number of summands is random, independent on the distribution of summand and having geometric distribution The geometric stable distribution may be symmetric or asymmetric. A symmetric geometric stable distribution is also referred to as a Linnik distribution. The Laplace distribution and asymmetric Laplace distribution are special cases of the geometric stable distribution and of a Linnik distribution. The Mittag–Leffler distribution is also a special case of a geometric stable distribution.

The geometric stable distribution has applications in finance theory.^[1]^[2]^[3]^[4]

Characteristics

For most geometric stable distributions, the probability density function and cumulative distribution function have no closed form solution. But a geometric stable distribution can be defined by its characteristic function, which has the form:^[5]

\varphi(t;\alpha,\beta,\lambda,\mu) = [1+\lambda^{\alpha}|t|^{\alpha} \omega - i \mu t]^{-1}

where $\omega = \begin{cases} 1 - i\tan\tfrac{\pi\alpha}{2} \beta \, \operatorname{sign}(t) & \text{if }\alpha \ne 1 \\ 1 + i\tfrac{2}{\pi}\beta\log|t| \operatorname{sign}(t) & \text{if }\alpha = 1 \end{cases}$

$\alpha$ , which must be greater than 0 and less than or equal to 2, is the shape parameter or index of stability, which determines how heavy the tails are.^[5] Lower $\alpha$ corresponds to heavier tails.

$\beta$ , which must be greater than or equal to −1 and less than or equal to 1, is the skewness parameter.^[5] When $\beta$ is negative the distribution is skewed to the left and when $\beta$ is positive the distribution is skewed to the right. When $\beta$ is zero the distribution is symmetric, and the characteristic function reduces to:^[5]

\varphi(t;\alpha, 0, \lambda,\mu) = [1+\lambda^{\alpha}|t|^{\alpha} - i \mu t]^{-1}

The symmetric geometric stable distribution with $\mu=0$ is also referred to as a Linnik distribution.^[6]^[7] A completely skewed geometric stable distribution, that is with $\beta=1$ , $\alpha<1$ , with $0<\mu<1$ is also referred to as a Mittag–Leffler distribution.^[8] Although $\beta$ determines the skewness of the distribution, it should not be confused with the typical skewness coefficient or 3rd standardized moment, which in most circumstances is undefined for a geometric stable distribution.

$\lambda>0$ is the scale parameter and $\mu$ is the location parameter.^[5]

When $\alpha$ = 2, $\beta$ = 0 and $\mu$ = 0 (i.e., a symmetric geometric stable distribution or Linnik distribution with $\alpha$ =2), the distribution becomes the symmetric Laplace distribution with mean of 0,^[6] which has a probability density function of:

f(x|0,\lambda) = \frac{1}{2\lambda} \exp \left( -\frac{|x|}{\lambda} \right) \,\!

The Laplace distribution has a variance equal to $2\lambda^2$ . However, for $\alpha<2$ the variance of the geometric stable distribution is infinite.

Relationship to the stable distribution

The stable distribution has the property that if $X_1, X_2,\dots,X_n$ are independent, identically distributed random variables taken from a stable distribution, the sum $Y = a_n (X_1 + X_2 + \cdots + X_n) + b_n$ has the same distribution as the $X_i$ s for some $a_n$ and $b_n$ .

The geometric stable distribution has a similar property, but where the number of elements in the sum is a geometrically distributed random variable. If $X_1, X_2,\dots$ are independent and identically distributed random variables taken from a geometric stable distribution, the limit of the sum $Y = a_{N_p} (X_1 + X_2 + \cdots + X_{N_p}) + b_{N_p}$ approaches the distribution of the $X_i$ s for some coefficients $a_{N_p}$ and $b_{N_p}$ as p approaches 0, where $N_p$ is a random variable independent of the $X_i$ s taken from a geometric distribution with parameter p.^[2] In other words:

\Pr(N_p = n) = (1 - p)^{n-1}\,p\, .

The distribution is strictly geometric stable only if the sum $Y = a (X_1 + X_2 + \cdots + X_{N_p})$ equals the distribution of the $X_i$ s for some a.^[1]

There is also a relationship between the stable distribution characteristic function and the geometric stable distribution characteristic function. The stable distribution has a characteristic function of the form:

\Phi(t;\alpha,\beta,\lambda,\mu) = \exp\left[~it\mu\!-\!|\lambda t|^\alpha\,(1\!-\!i \beta \operatorname{sign}(t)\Omega)~\right] ,

where

\Omega = \begin{cases} \tan\tfrac{\pi\alpha}{2} & \text{if }\alpha \ne 1 ,\\ -\tfrac{2}{\pi}\log|t| & \text{if }\alpha = 1. \end{cases}

The geometric stable characteristic function can be expressed in terms of a stable characteristic function as:^[9]

\varphi(t;\alpha,\beta,\lambda,\mu) = [1 - \log(\Phi(t;\alpha,\beta,\lambda,\mu))]^{-1} .

References

1 2 Rachev, S. & Mittnik, S. (2000). Stable Paretian Models in Finance. Wiley. pp. 34–36. ISBN 978-0-471-95314-2.
1 2 Trindade, A.A.; Zhu, Y. & Andrews, B. (May 18, 2009). "Time Series Models With Asymmetric Laplace Innovations" (PDF). pp. 1–3. Retrieved 2011-02-27.
↑ Meerschaert, M. & Sceffler, H. "Limit Theorems for Continuous Time Random Walks" (PDF). p. 15. Retrieved 2011-02-27.
↑ Kozubowski, T. (1999). "Geometric Stable Laws: Estimation and Applications". Mathematical and Computer Modelling 29: 241. Retrieved 2015-12-29.
1 2 3 4 5 Kozubowski, T.; Podgorski, K. & Samorodnitsky, G. "Tails of Lévy Measure of Geometric Stable Random Variables" (PDF). pp. 1–3. Retrieved 2011-02-27.
1 2 Kotz, S.; Kozubowski, T. & Podgórski, K. (2001). The Laplace distribution and generalizations. Birkhäuser. pp. 199–200. ISBN 978-0-8176-4166-5.
↑ Kozubowski, T. (2006). "A Note on Certain Stability and Limiting Properties of ν-inﬁnitely divisible distribution" (PDF). Int. J. Contemp. Math. Sci. 1 (4): 159. Retrieved 2011-02-27.
↑ Burnecki, K.; Janczura, J.; Magdziarz, M. & Weron, A. (2008). "Can One See a Competition Between Subdiffusion and Lévy Flights? A Care of Geometric Stable Noise" (PDF). Acta Physica Polonica B 39 (8): 1048. Retrieved 2011-02-27.
↑ "Geometric Stable Laws Through Series Representations" (PDF). Serdica Mathematical Journal 25: 243. 1999. Retrieved 2011-02-28.

Probability distributions

Discrete univariate with finite support

Benford Bernoulli Beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot

Discrete univariate with infinite support

beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta

Continuous univariate supported on a bounded interval, e.g. [0,1]

Arcsine ARGUS Balding–Nichols Bates Beta Beta rectangular Irwin–Hall Kumaraswamy logit-normal Noncentral beta raised cosine Reciprocal Triangular U-quadratic uniform Wigner semicircle

[[List of probability distributions#Supported_on_semi-infinite_intervals.2C_usually_.5B0.2C.E2.88.9E.29|Continuous univariate supported on a semi-infinite interval, usually [0,∞)]]

Continuous univariate supported on the whole real line (−∞, ∞)

Cauchy exponential power Fisher's z generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson S_U Landau Laplace Asymmetric Laplace Linnik logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt

Continuous univariate with support whose type varies

generalized extreme value generalized Pareto Tukey lambda q-Gaussian q-exponential q-Weibull shifted log-logistic

Mixed continuous-discrete univariate distributions

rectified Gaussian

Multivariate (joint)

Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet Generalized Dirichlet multivariate normal Multivariate stable multivariate Student normal-scaled inverse gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart

Directional

Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham

Degenerate and singular

Degenerate discrete degenerate Dirac delta function Singular Cantor

Families

Circular compound Poisson elliptical exponential natural exponential location-scale maximum entropy mixture Pearson Tweedie wrapped

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