Wrapped asymmetric Laplace distribution

Wrapped asymmetric Laplace distribution

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
Parameters	$m$ location $(0\leq m<2\pi )$ $\lambda >0$ scale (real) $\kappa >0$ asymmetry (real)
Support	$0\leq \theta <2\pi$
PDF	(see article)
Mean	$m$ (circular)
Variance	$1-{\frac {\lambda ^{2}}{\sqrt {\left({\frac {1}{\kappa ^{2}}} \lambda ^{2}\right)\left(\kappa ^{2} \lambda ^{2}\right)}}}$ (circular)
CF	${\frac {\lambda ^{2}e^{imn}}{\left(n-i\lambda /\kappa \right)\left(n i\lambda \kappa \right)}}$

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.

Definition

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

${\begin{aligned}f_{WAL}(\theta ;m,\lambda ,\kappa )&=\sum _{k=-\infty }^{\infty }f_{AL}(\theta 2\pi k,m,\lambda ,\kappa )\\[10pt]&={\dfrac {\kappa \lambda }{\kappa ^{2} 1}}{\begin{cases}{\dfrac {e^{-(\theta -m)\lambda \kappa }}{1-e^{-2\pi \lambda \kappa }}}-{\dfrac {e^{(\theta -m)\lambda /\kappa }}{1-e^{2\pi \lambda /\kappa }}}&{\text{if }}\theta \geq m\\[12pt]{\dfrac {e^{-(\theta -m)\lambda \kappa }}{e^{2\pi \lambda \kappa }-1}}-{\dfrac {e^{(\theta -m)\lambda /\kappa }}{e^{-2\pi \lambda /\kappa }-1}}&{\text{if }}\theta <m\end{cases}}\end{aligned}}$

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.

The cumulative distribution function $F_{WAL}$ is therefore:

$F_{WAL}(\theta ;m,\lambda ,\kappa )={\dfrac {\kappa \lambda }{\kappa ^{2} 1}}{\begin{cases}{\dfrac {e^{m\lambda \kappa }(1-e^{-\theta \lambda \kappa })}{\lambda \kappa (e^{2\pi \lambda \kappa }-1)}} {\dfrac {\kappa e^{-m\lambda /\kappa }(1-e^{\theta \lambda /\kappa })}{\lambda (e^{-2\pi \lambda /\kappa }-1)}}&{\text{if }}\theta \leq m\\{\dfrac {1-e^{-(\theta -m)\lambda \kappa }}{\lambda \kappa (1-e^{-2\pi \lambda \kappa })}} {\dfrac {\kappa (1-e^{(\theta -m)\lambda /\kappa })}{\lambda (1-e^{2\pi \lambda /\kappa })}} {\dfrac {e^{m\lambda \kappa }-1}{\lambda \kappa (e^{2\pi \lambda \kappa }-1)}} {\dfrac {\kappa (e^{-m\lambda /\kappa }-1)}{\lambda (e^{-2\pi \lambda /\kappa }-1)}}&{\text{if }}\theta >m\end{cases}}$

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=e^i(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 $-\pi <\theta \leq \pi$.

See also

• Wrapped distribution
• Directional statistics