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Milds # Wrapped asymmetric Laplace distribution

Parameters Probability density function Wrapped asymmetric Laplace PDF with m = 0.Note that the κ =  2 and 1/2 curves are mirror images about θ=π $m$ location $(0\leq m<2\pi )$ $\lambda >0$ scale (real) $\kappa >0$ asymmetry (real) $0\leq \theta <2\pi$ (see article) $m$ (circular) $1-{\frac {\lambda ^{2}}{\sqrt {\left({\frac {1}{\kappa ^{2}}}+\lambda ^{2}\right)\left(\kappa ^{2}+\lambda ^{2}\right)}}}$ (circular) ${\frac {\lambda ^{2}e^{imn}}{\left(n-i\lambda /\kappa \right)\left(n+i\lambda \kappa \right)}}$ In probability theory and directional statistics, a wrapped asymmetric Laplace distribution is a wrapped probability distribution that results from the "wrapping" of the asymmetric Laplace distribution around the unit circle. For the symmetric case (asymmetry parameter κ = 1), the distribution becomes a wrapped Laplace distribution. The distribution of the ratio of two circular variates (Z) from two different wrapped exponential distributions will have a wrapped asymmetric Laplace distribution. These distributions find application in stochastic modelling of financial data.

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## Definition

The probability density function of the wrapped asymmetric Laplace distribution is:

{\begin{aligned}f_{WAL}(\theta ;m,\lambda ,\kappa )&=\sum _{k=-\infty }^{\infty }f_{AL}(\theta +2\pi k,m,\lambda ,\kappa )\\[10pt]&={\begin{cases}{\dfrac {e^{-(\theta -m)\lambda \kappa }}{1-e^{-2\pi \kappa \lambda }}}-{\dfrac {e^{(\theta -m)\lambda /\kappa }}{1-e^{2\pi \lambda /\kappa }}}&{\text{if }}\theta \geq m\\[12pt]{\dfrac {e^{-(\theta -m)\lambda \kappa }}{e^{2\pi \lambda \kappa }-1}}-{\dfrac {e^{(\theta -m)\lambda /\kappa }}{e^{-2\pi \lambda /\kappa }-1}}&{\text{if }}\theta where $f_{AL}$ is the asymmetric Laplace distribution. The angular parameter is restricted to $0\leq \theta <2\pi$ . The scale parameter is $\lambda >0$ which is the scale parameter of the unwrapped distribution and $\kappa >0$ is the asymmetry parameter of the unwrapped distribution.

## Characteristic function

The characteristic function of the wrapped asymmetric Laplace is just the characteristic function of the asymmetric Laplace function evaluated at integer arguments:

$\varphi _{n}(m,\lambda ,\kappa )={\frac {\lambda ^{2}e^{imn}}{\left(n-i\lambda /\kappa \right)\left(n+i\lambda \kappa \right)}}$ which yields an alternate expression for the wrapped asymmetric Laplace PDF in terms of the circular variable z=ei(θ-m) valid for all real θ and m:

{\begin{aligned}f_{WAL}(z;m,\lambda ,\kappa )&={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }\varphi _{n}(0,\lambda ,\kappa )z^{-n}\\[10pt]&={\frac {\lambda }{\pi (\kappa +1/\kappa )}}{\begin{cases}{\textrm {Im}}\left(\Phi (z,1,-i\lambda \kappa )-\Phi \left(z,1,i\lambda /\kappa \right)\right)-{\frac {1}{2\pi }}&{\text{if }}z\neq 1\\[12pt]\coth(\pi \lambda \kappa )+\coth(\pi \lambda /\kappa )&{\text{if }}z=1\end{cases}}\end{aligned}} where $\Phi ()$ is the Lerch transcendent function and coth() is the hyperbolic cotangent function.

## Circular moments

In terms of the circular variable $z=e^{i\theta }$ the circular moments of the wrapped asymmetric Laplace distribution are the characteristic function of the asymmetric Laplace distribution evaluated at integer arguments:

$\langle z^{n}\rangle =\varphi _{n}(m,\lambda ,\kappa )$ The first moment is then the average value of z, also known as the mean resultant, or mean resultant vector:

$\langle z\rangle ={\frac {\lambda ^{2}e^{im}}{\left(1-i\lambda /\kappa \right)\left(1+i\lambda \kappa \right)}}$ The mean angle is $(-\pi \leq \langle \theta \rangle \leq \pi )$ $\langle \theta \rangle =\arg(\,\langle z\rangle \,)=\arg(e^{im})$ and the length of the mean resultant is

$R=|\langle z\rangle |={\frac {\lambda ^{2}}{\sqrt {\left({\frac {1}{\kappa ^{2}}}+\lambda ^{2}\right)\left(\kappa ^{2}+\lambda ^{2}\right)}}}.$ The circular variance is then 1 − R

## Generation of random variates

If X is a random variate drawn from an asymmetric Laplace distribution (ALD), then $Z=e^{iX}$ will be a circular variate drawn from the wrapped ALD, and, $\theta =\arg(Z)+\pi$ will be an angular variate drawn from the wrapped ALD with $0<\theta \leq 2\pi$ .

Since the ALD is the distribution of the difference of two variates drawn from the exponential distribution, it follows that if Z1 is drawn from a wrapped exponential distribution with mean m1 and rate λ/κ and Z2 is drawn from a wrapped exponential distribution with mean m2 and rate λκ, then Z1/Z2 will be a circular variate drawn from the wrapped ALD with parameters ( m1 - m2 , λ, κ) and $\theta =\arg(Z_{1}/Z_{2})+\pi$ will be an angular variate drawn from that wrapped ALD with $-\pi <\theta \leq \pi$ .