More on Pareto distribution

There are several types of Pareto distributions. The Pareto distribution described in this previous post is called the Pareto Type II distribution (also called Lomax distribution). There is also a Pareto Type I distribution. The difference between the two Pareto types is that the support of Pareto Type I is the interval (\theta, \infty). In other words, the random variable takes on positive real numbers larger than some fixed positive parameter \theta. On the other hand, Pareto Type II distribution can take on any positive real number. However, the two types are mathematically related. Their density functions have the same shape (if they have the same parameters).

Pareto Type I Density Curve (Red) versus Pareto Type II Density Curve (Green)

In the above diagram, the red curve is the density function for the Pareto Type I distribution while the green curve is the density function for the Pareto Type II distribution. For both distributions, the shape parameter is \alpha=2 and the scale parameter is \theta=5.

Note that the green density curve is the result of shifting the red curve horizontally 5 units to the left. The following are the density functions.

    Red Curve
    \displaystyle f(x)=\frac{2 (5)^2}{x^3}=\frac{50}{x^3} \ \ \ \ \ x>5

    Green Curve
    \displaystyle g(x)=\frac{2 (5)^2}{(x+5)^3}=\frac{50}{(x+5)^3} \ \ \ \ \ x>0

From the diagram and the density functions, we see that the green density curve is the result of shifting the red curve to the left by 5. Otherwise, both curve have the same distributional shape.

Since Pareto Type II is a shifting of Pareto Type I, they share similar mathematical properties. For example, they are both heavy tailed distributions. Thus they are appropriate for modeling extreme losses (in insurance applications) or financial fiasco or financial ruin (in financial applications). Which one to use depends on where the starting point of the random variable is.

However, the mathematical calculations are different to a large degree since the Pareto Type II density function is the result of a horizontal shift.

We would like to introduce blog posts in two companion blogs that discuss the Pareto distribution in greater details. This blog post is from a companion blog called Topics in Actuarial Modeling. It describes Pareto Type I and Type II in greater details. This blog post from another companion blog called Actuarial Modeling Practice has a side-by-side comparison of Pareto Type I and Pareto Type II. It also discusses the two Pareto types from a calculations stand point. This blog post has practice problems on the two Pareto types.

Another important mathematical property of Pareto Type II is that it is the mixture of exponential distributions with gamma mixing weights (exponential-gamma mixture). This is also discussed in the highlighted blog posts.

\text{ }

\text{ }

\text{ }

\copyright 2017 – Dan Ma

Advertisements

The mean excess loss function

We take a closer look at the mean excess loss function. We start with the following example:

Example
Suppose that an entity is exposed to a random loss X. An insurance policy offers protection against this loss. Under this policy, payment is made to the insured entity subject to a deductible d>0, i.e. when a loss is less than d, no payment is made to the insured entity, and when the loss exceeds d, the insured entity is reimbursed for the amount of the loss in excess of the deductible d. Consider the following two questions:

\text{ }

  1. Of all the losses that are eligible to be reimbursed by the insurer, what is the average payment made by the insurer to the insured?
  2. What is the average payment made by the insurer to the insured entity?

\text{ }

The two questions look similar. The difference between the two questions is subtle but important. In the first question, the average is computed over all losses that are eligible for reimbursement (i.e., the loss exceeds the deductible). This is the average amount the insurer is expected to pay in the event that a payment in excess of the deductible is required to be made. So this average is a per payment average.

In the second question, the average is calculated over all losses (regardless of sizes). When the loss does not reach the deductible, the payment is considered zero and when the loss is in excess of the deductible, the payment is X-d. Thus the average is the average amount the insurer has to pay per loss. So the second question is about a per loss average.

—————————————————————————————————————-

The Mean Excess Loss Function
The average in the first question is called the mean excess loss function. Suppose X is the random loss and d>0. The mean excess loss variable is the conditional variable X-d \ \lvert X>d and the mean excess loss function e_X(d) is defined by:

\text{ }

\displaystyle (1) \ \ \ \ \ e_X(d)=E(X-d \lvert X>d)

\text{ }

In an insurance context, the mean excess loss function is the average payment in excess of a threshold given that the loss exceeds the threshold. In a mortality context, the mean excess loss function is called the mean residual life function and complete expectation of life and can be interpreted as the remaining time until death given that the life in question is alive at age d.

The mean excess loss function is computed by the following depending on whether the loss variable is continuous or discrete.

\text{ }

\displaystyle (2) \ \ \ \ \ e_X(d)=\frac{\int_d^\infty (x-d) \ f_X(x) \ dx}{S_X(d)}

\displaystyle (3) \ \ \ \ \ e_X(d)=\frac{\sum \limits_{x>d} (x-d) \ P(X=x)}{S_X(d)}

\text{ }

The mean excess loss function e_X(d) is defined only when the integral or the sum converges. The following is an equivalent calculation of e_X(d) that may be easier to use in some circumstances.

\text{ }

\displaystyle (4) \ \ \ \ \ e_X(d)=\frac{\int_d^\infty S_X(x) \ dx}{S_X(d)}

\displaystyle (5a) \ \ \ \ \ e_X(d)=\frac{\sum \limits_{x \ge d} S_X(x) }{S_X(d)}

\displaystyle (5b) \ \ \ \ \ e_X(d)=\frac{\biggl(\sum \limits_{x>d} S_X(x)\biggr)+(w+1-d) S_X(w) }{S_X(d)}

\text{ }

In both (5a) and (5b), we assume that the support of X is the set of nonnegative integers. In (5a), we assume that the deductible d is a positive integer. In (5b), the deductible d is free to be any positive number and w is the largest integer such that w \le d. The formulation (4) is obtained by using integration by parts (also see theorem 3.1 in [1]). The formulations of (5a) and (5b) are a result of applying theorem 3.2 in [1].

The mean excess loss function provides information about the tail weight of a distribution, see the previous post The Pareto distribution. Also see Example 3 below.
—————————————————————————————————————-
The Mean in Question 2
The average that we need to compute is the mean of the following random variable. Note that (a)_+ is the function that assigns the value of a whenever a>0 and otherwise assigns the value of zero.

\text{ }

\displaystyle (6) \ \ \ \ \ (X-d)_+=\left\{\begin{matrix}0&\ X<d\\{X-d}&\ X \ge d \end{matrix}\right.

\text{ }

The mean E((X-d)_+) is calculated over all losses. When the loss is less than the deductible d, the insurer has no obligation to make a payment to the insured and the payment is assumed to be zero in the calculation of E[(X-d)_+]. The following is how this expected value is calculated depending on whether the loss X is continuous or discrete.

\text{ }

\displaystyle (7) \ \ \ \ \ E((X-d)_+)=\int_d^\infty (x-d) \ f_X(x) \ dx

\displaystyle (8) \ \ \ \ \ E((X-d)_+)=\sum \limits_{x>d} (x-d) \ P(X=x)

\text{ }

Based on the definitions, the following is how the two averages are related.

\displaystyle E[(X-d)_+]=e_X(d) \ [1-F_X(d)] \ \ \ \text{or} \ \ \ E[(X-d)_+]=e_X(d) \ S_X(d)

—————————————————————————————————————-
The Limited Expected Value
For a given positive constant u, the limited loss variable is defined by

\text{ }

\displaystyle (9) \ \ \ \ \ X \wedge u=\left\{\begin{matrix}X&\ X<u\\{u}&\ X \ge u \end{matrix}\right.

\text{ }

The expected value E(X \wedge u) is called the limited expected value. In an insurance application, the u is a policy limit that sets a maximum on the benefit to be paid. The following is how the limited expected value is calculated depending on whether the loss X is continuous or discrete.

\text{ }

\displaystyle (9) \ \ \ \ \ E(X \wedge u)=\int_{-\infty}^u x \ f_X(x) \ dx+u \ S_X(u)

\displaystyle (10) \ \ \ \ \ E(X \wedge u)=\biggl(\sum \limits_{x < u} x \ P(X=x)\biggr)+u \ S_X(u)

\text{ }

Interestingly, we have the following relation.

\text{ }

\displaystyle (X-d)_+ + (X \wedge d)=X \ \ \ \ \ \ \text{and} \ \ \ \ \ \ E[(X-d)_+] + E(X \wedge d)=E(X)

\text{ }

The above statement indicates that purchasing a policy with a deductible d and another policy with a policy maximum d is equivalent to buying full coverage.

Another way to interpret X \wedge d is that it is the amount of loss that is eliminated by having a deductible in the insurance policy. If the insurance policy pays the loss in full, then the insurance payment is X and the expected amount the insurer is expected to pay is E(X). By having a deductible provision in the policy, the insurer is now only liable for the amount (X-d)_+ and the amount the insurer is expected to pay per loss is E[(X-d)_+]. Consequently E(X \wedge d) is the expected amount of the loss that is eliminated by the deductible provision in the policy. The following summarizes this observation.

\text{ }

\displaystyle (X \wedge d)=X-(X-d)_+ \ \ \ \ \ \ \text{and} \ \ \ \ \ \ E(X \wedge d)=E(X)-E[(X-d)_+]

—————————————————————————————————————-
Example 1
Let the loss random variable X be exponential with pdf f(x)=\alpha e^{-\alpha x}. We have E(X)=\frac{1}{\alpha}. Because of the no memory property of the exponential distribution, given that a loss exceeds the deductible, the mean payment is the same as the original mean. Thus e_X(d)=\frac{1}{\alpha}. Then the per loss average is:

\displaystyle E[(X-d)_+]=e_X(d) \ S(d) = \frac{e^{-\alpha d}}{\alpha}

\text{ }

Thus, with a deductible provision in the policy, the insurer is expected to pay \displaystyle \frac{e^{-\alpha d}}{\alpha} per loss instead of \displaystyle \frac{1}{\alpha}. Thus the expected amount of loss eliminated (from the insurer’s point of view) is \displaystyle E(X \wedge d)=\frac{1-e^{-\alpha d}}{\alpha}.

\text{ }

Example 2
Suppose that the loss variable has a Gamma distribution where the scale parameter is \alpha and the shape parameter is n=2. The pdf is \displaystyle g(x)=\alpha^2 \ x \ e^{-\alpha x}. The insurer’s expected payment without the deductible is E(X)=\frac{2}{\alpha}. The survival function S(x) is:

\displaystyle S(x)=e^{-\alpha x}(1+\alpha x)

\text{ }

For the losses that exceed the deductible, the insurer’s expected payment is:

\displaystyle \begin{aligned}e_X(d)&=\frac{\int_d^{\infty} S(x) \ dx}{S(d)}\\&=\frac{\int_d^{\infty} e^{-\alpha x}(1+\alpha x) \ dx}{e^{-\alpha d}(1+\alpha d)} \\&=\frac{\frac{e^{-\alpha d}}{\alpha}+d e^{-\alpha d}+\frac{e^{-\alpha d}}{\alpha}}{e^{-\alpha d}(1+\alpha d)} \\&=\frac{\frac{2}{\alpha}+d}{1+\alpha d} \end{aligned}

\text{ }

Then the insurer’s expected payment per loss is E[(X-d)_+]:

\displaystyle \begin{aligned}E[(X-d)_+]&=e_X(d) \ S(d) \\&=\frac{\frac{2}{\alpha}+d}{1+\alpha d} \ \ e^{-\alpha d}(1+\alpha d) \\&=e^{-\alpha d} \ \biggl(\frac{2}{\alpha}+d\biggr) \end{aligned}

\text{ }

With a deductible in the policy, the following is the expected amount of loss eliminated (from the insurer’s point of view).

\displaystyle \begin{aligned}E[X \wedge d]&=E(X)-E[(X-d)_+] \\&=\frac{2}{\alpha}-e^{-\alpha d} \ \biggl(\frac{2}{\alpha}+d\biggr) \\&=\frac{2}{\alpha}\biggl(1-e^{-\alpha d}\biggr)-d e^{-\alpha d} \end{aligned}

\text{ }

Example 3
Suppose the loss variable X has a Pareto distribution with the following pdf:

\text{ }

\displaystyle f_X(x)=\frac{\beta \ \alpha^\beta}{(x+\alpha)^{\beta+1}} \ \ \ \ \ x>0

\text{ }

If the insurance policy is to pay the full loss, then the insurer’s expected payment per loss is \displaystyle E(X)=\frac{\alpha}{\beta-1} provided that the shape parameter \beta is larger than one.

The mean excess loss function of the Pareto distribution has a linear form that is increasing (see the previous post The Pareto distribution). The following is the mean excess loss function:

\text{ }

\displaystyle e_X(d)=\frac{1}{\beta-1} \ d +\frac{\alpha}{\beta-1}=\frac{1}{\beta-1} \ d +E(X)

\text{ }

If the loss is modeled by such a distribution, this is an uninsurable risk! First of all, the higher the deductible, the larger the expected payment if such a large loss occurs. The expected payment for large losses is always the unmodified expected E(X) plus a component that is increasing in d.

The increasing mean excess loss function is an indication that the Pareto distribution is a heavy tailed distribution. In general, an increasing mean excess loss function is an indication of a heavy tailed distribution. On the other hand, a decreasing mean excess loss function indicates a light tailed distribution. The exponential distribution has a constant mean excess loss function and is considered a medium tailed distribution.

Reference

  1. Bowers N. L., Gerber H. U., Hickman J. C., Jones D. A., Nesbit C. J. Actuarial Mathematics, First Edition., The Society of Actuaries, Itasca, Illinois, 1986
  2. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004

Another way to generate the Pareto distribution

The Pareto distribution is a heavy tailed distribution, suitable as candidate for modeling large insurance losses above a threshold. It is a mixture of exponential distributions with Gamma mixing weights. Another way to generate the Pareto distribution is taking the inverse of another distribution (raising another distribution to the power of minus one). Previous discussion on the Pareto distribution can be found here: An example of a mixture and The Pareto distribution.

\text{ }

Let X be a continuous random variable with pdf f_X(x) and with cdf F_X(x) such that F_X(0)=0. Let Y=X^{-1}. Then the resulting distribution for Y is called an inverse. For example, if X has an exponential distribution, then Y is said to have an inverse exponential distribution. The following are the cdf and pdf of Y=X^{-1}.

\text{ }

\displaystyle F_Y(y)=1-F_X(y^{-1}) \ \ \ \ \ \ \ \ f_Y(y)=f_X(y^{-1}) \ y^{-2}

\text{ }

We now show that the Pareto distribution is an inverse. Suppose that X has the pdf f_X(x)=\beta \ x^{\beta-1}, 0<x<1. We show that Y=X^{-1} has a Pareto distribution with scale paramter \alpha=1 and shape parameter \beta>0. Once this base distribution is established, we can relax the scale parameter to have other positive values.

\text{ }

The cdf of X is F_X(x)=x^\beta where 0<x<1. Since the support of X is 0<x<1, the support of Y is y>1. Thus in deriving the cdf F_Y(y), we only need to focus on y>1 (or 0<x<1). The following is the cdf F_Y(y):

\text{ }

\displaystyle F_Y(y)=1-F_X(y^{-1})=1-y^{-\beta}, \ \ \ \ y>1

\text{ }

Upon differentiation, we obtain the pdf:

\displaystyle f_Y(y)=\beta \ y^{-(\beta+1)}=\frac{\beta}{y^{\beta+1}}, \ \ \ y>1

\text{ }

The above pdf is that of a Pareto distribution with scale paramter \alpha=1 and shape parameter \beta. However, the support of this pdf is y>1. In order to have y>0 as the support, we have the following alternative version:

\text{ }

\displaystyle f_Y(y)=\frac{\beta}{(y+1)^{\beta+1}}, \ \ \ y>0

\text{ }

We now transform the above pdf to become a true 2-parameter Pareto pdf by relaxing the scale parameter. The result is the following pdf.

\text{ }

\displaystyle f_Y(y)=\frac{\beta \ \alpha^{\beta}}{(y+\alpha)^{\beta+1}}, \ \ \ y>0

The Pareto distribution

This post takes a closer look at the Pareto distribution. A previous post demonstrates that the Pareto distribution is a mixture of exponential distributions with Gamma mixing weights. We now elaborate more on this point. Through looking at various properties of the Pareto distribution, we also demonstrate that the Pareto distribution is a heavy tailed distribution. In insurance applications, heavy-tailed distributions are essential tools for modeling extreme loss, especially for the more risky types of insurance such as medical malpractice insurance. In financial applications, the study of heavy-tailed distributions provides information about the potential for financial fiasco or financial ruin. The Pareto distribution is a great way to open up a discussion on heavy-tailed distribution.

\text{ }

Update (11/12/2017). This blog post introduces a catalog of many other parametric severity models in addition to Pareto distribution. The link to the catalog is found in that blog post. To go there directly, this is the link.

Update (10/29/2017). This blog post has updated information on Pareto distribution. It also has links to more detailed contents on Pareto distribution in two companion blogs. These links are also given here: more detailed post on Pareto, Pareto Type I and Type II and practice problems on Pareto.

\text{ }

The continuous random variable X with positive support is said to have the Pareto distribution if its probability density function is given by

\displaystyle f_X(x)=\frac{\beta \ \alpha^\beta}{(x+\alpha)^{\beta+1}} \ \ \ \ \ x>0

where \alpha>0 and \beta>0 are constant. The constant \alpha is the scale parameter and \beta is the shape parameter. The following lists several other distributional quantities of the Pareto distribution, which will be used in the discussion below.

\displaystyle S_X(x)=\frac{\alpha^\beta}{(x+\alpha)^\beta}=\biggl(\frac{\alpha}{x+\alpha}\biggr)^\beta \ \ \ \ \ \ \ \ \ \text{survival function}

\displaystyle F_X(x)=1-\biggl(\frac{\alpha}{x+\alpha}\biggr)^\beta \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{distribution function}

\displaystyle E(X)=\frac{\alpha}{\beta-1} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{mean},\beta>1

\displaystyle E(X^2)=\frac{2 \alpha^2}{(\beta-1)(\beta-2)} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{second momemt},\beta>2

\displaystyle Var(X)=\frac{\alpha^2 \beta}{(\beta-1)^2(\beta-2)} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{variance},\beta>2

\displaystyle E(X^k)=\frac{k! \alpha^k}{(\beta-1) \cdots (\beta-k)} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{higher moments},\beta>k, \text{ k positive integer}

——————————————————————————————————————
The Pareto Distribution as a Mixture
The Pareto pdf indicated above can be obtained by mixing exponential distributions using Gamma distributions as weights. Suppose that X follows an exponential distribution (conditional on a parameter value \theta). The following is the conditional pdf of X.

\displaystyle f_{X \lvert \Theta}(x \lvert \theta)=\theta e^{-\theta x} \ \ \ x>0

There is uncertainty in the parameter, which can be viewed as a random variable \Theta. Suppose that \Theta follows a Gamma distribution with scale parameter \alpha and shape parameter \beta. The following is the pdf of \Theta.

\displaystyle f_{\Theta}(\theta)=\frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^{\beta-1} \ e^{-\alpha \theta} \ \ \ \theta>0

The unconditional pdf of X is the weighted average of the conditional pdfs with the Gamma pdf as weight.

\displaystyle \begin{aligned}f_X(x)&=\int_0^{\infty} f_{X \lvert \Theta}(x \lvert \theta) \ f_\Theta(\theta) \ d \theta \\&=\int_0^{\infty} \biggl[\theta \ e^{-\theta x}\biggr] \ \biggl[\frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^{\beta-1} \ e^{-\alpha \theta}\biggr] \ d \theta \\&=\int_0^{\infty} \frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^\beta \ e^{-\theta(x+\alpha)} \ d \theta \\&=\frac{\alpha^\beta}{\Gamma(\beta)} \frac{\Gamma(\beta+1)}{(x+\alpha)^{\beta+1}} \int_0^{\infty} \frac{(x+\alpha)^{\beta+1}}{\Gamma(\beta+1)} \ \theta^{\beta+1-1} \ e^{-\theta(x+\alpha)} \ d \theta \\&=\frac{\beta \ \alpha^\beta}{(x+\alpha)^{\beta+1}} \end{aligned}

In the following discussion, X will denote the Pareto distribution as defined above. As will be shown below, the exponential distribution is considered a light tailed distribution. Yet mixing exponentials produces the heavy tailed Pareto distribution. Mixture distributions tend to heavy tailed (see [1]). The Pareto distribution is a handy example.

——————————————————————————————————————

The Tail Weight of the Pareto Distribution
When a distribution significantly puts more probability on larger values, the distribution is said to be a heavy tailed distribution (or said to have a larger tail weight). According to [1], there are four ways to look for indication that a distribution is heavy tailed.

  1. Existence of moments.
  2. Speed of decay of the survival function to zero.
  3. Hazard rate function.
  4. Mean excess loss function.

Existence of moments
Note that the existence of the Pareto higher moments E(X^k) is capped by the shape parameter \beta. In particular, the mean E(X)=\frac{\alpha}{\beta-1} does not exist for \beta \le 1. If the Pareto distribution is to model a random loss, and if the mean is infinite (when \beta=1), the risk is uninsurable! On the other hand, when \beta=2, the Pareto variance does not exist. This shows that for a heavy tailed distribution, the variance may not be a good measure of risk.

For a given random variable Z, the existence of all moments E(Z^k), for all positive integers k, indicates with a light (right) tail for the distribution of Z. The existence of positive moments exists only up to a certain value of a positive integer k is an indication that the distribution has a heavy right tail. In contrast, the exponential distribution and the Gamma distribution are considered to have light tails since all moments exist.

The speed of decay of the survival function
The survival function S_X(x)=P(X>x) captures the probability of the tail of a distribution. If a distribution whose survival function decays slowly to zero (equivalently the cdf goes slowly to one), it is another indication that the distribution is heavy tailed.

The following is a comparison of a Pareto survival function and an exponential survival function. The Pareto survival function has parameters (\alpha=2 and \beta=2). The two survival functions are set to have the same 75th percentile (x=2).

\displaystyle \begin{pmatrix} \text{x}&\text{Pareto }S_X(x)&\text{Exponential }S_Y(x)&\displaystyle \frac{S_X(x)}{S_Y(x)} \\\text{ }&\text{ }&\text{ }&\text{ } \\{2}&0.25&0.25&1 \\{10}&0.027777778&0.000976563&28  \\{20}&0.008264463&9.54 \times 10^{-7}&8666 \\{30}&0.00390625&9.31 \times 10^{-10}&4194304 \\{40}&0.002267574&9.09 \times 10^{-13}&2.49 \times 10^{9} \\{60}&0.001040583&8.67 \times 10^{-19}&1.20 \times 10^{15} \\{80}&0.000594884&8.27 \times 10^{-25}&7.19 \times 10^{20} \\{100}&0.000384468&7.89 \times 10^{-31}&4.87 \times 10^{26} \\{120}&0.000268745&7.52 \times 10^{-37}&3.57 \times 10^{32} \\{140}&0.000198373&7.17 \times 10^{-43}&2.76 \times 10^{38} \\{160}&0.000152416&6.84 \times 10^{-49}&2.23 \times 10^{44} \\{180}&0.000120758&6.53 \times 10^{-55}&1.85 \times 10^{50}  \end{pmatrix}

Note that at the large values, the Pareto right tails retain much more probability. This is also confirmed by the ratio of the two survival functions, with the ratio approaching infinity. If a random loss is a heavy tailed phenomenon that is described by the above Pareto survival function (\alpha=2 and \beta=2), then the above exponential survival function is woefully inadequate as a model for this phenomenon even though it may be a good model for describing the loss up to the 75th percentile. It is the large right tail that is problematic (and catastrophic)!

Since the Pareto survival function and the exponential survival function have closed forms, We can also look at their ratio.

\displaystyle \frac{\text{pareto survival}}{\text{exponential survival}}=\frac{\displaystyle \frac{\alpha^\beta}{(x+\alpha)^\beta}}{e^{-\lambda x}}=\frac{\alpha^\beta e^{\lambda x}}{(x+\alpha)^\beta} \longrightarrow \infty \ \text{ as } x \longrightarrow \infty

In the above ratio, the numerator has an exponential function with a positive quantity in the exponent, while the denominator has a polynomial in x. This ratio goes to infinity as x \rightarrow \infty.

In general, whenever the ratio of two survival functions diverges to infinity, it is an indication that the distribution in the numerator of the ratio has a heavier tail. When the ratio goes to infinity, the survival function in the numerator is said to decay slowly to zero as compared to the denominator. We have the same conclusion in comparing the Pareto distribution and the Gamma distribution, that the Pareto is heavier in the tails. In comparing the tail weight, it is equivalent to consider the ratio of density functions (due to the L’Hopital’s rule).

\displaystyle \lim_{x \rightarrow \infty} \frac{S_1(x)}{S_2(x)}=\lim_{x \rightarrow \infty} \frac{S_1^{'}(x)}{S_2^{'}(x)}=\lim_{x \rightarrow \infty} \frac{f_1(x)}{f_2(x)}

The Hazard Rate Function
The hazard rate function h_X(x) of a random variable X is defined as the ratio of the density function and the survival function.

\displaystyle h_X(x)=\frac{f_X(x)}{S_X(s)}

The hazard rate is called the force of mortality in a life contingency context and can be interpreted as the rate that a person aged x will die in the next instant. The hazard rate is called the failure rate in reliability theory and can be interpreted as the rate that a machine will fail at the next instant given that it has been functioning for x units of time. The following is the hazard rate function of the Pareto distribution.

\displaystyle \begin{aligned}h_X(x)&=\frac{f_X(s)}{S_X(x)} \\&=\frac{\beta}{x+\alpha}  \end{aligned}

The interesting point is that the Pareto hazard rate function is an decreasing function in x. Another indication of heavy tail weight is that the distribution has a decreasing hazard rate function. One key characteristic of hazard rate function is that it can generate the survival function.

\displaystyle S_X(x)=e^{\displaystyle -\int_0^x h_X(t) \ dt}

Thus if the hazard rate function is decreasing in x, then the survival function will decay more slowly to zero. To see this, let H_X(x)=\int_0^x h_X(t) \ dt, which is called the cumulative hazard rate function. As indicated above, the survival function can be generated by e^{-H_X(x)}. If h_X(x) is decreasing in x, H_X(x) is smaller than H_Y(x) where h_Y(x) is constant in x or increasing in x. Consequently e^{-H_X(x)} is decaying to zero much more slowly than e^{-H_Y(x)}.

In contrast, the exponential distribution has a constant hazard rate function, making it a medium tailed distribution. As explained above, any distribution having an increasing hazard rate function is a light tailed distribution.

The Mean Excess Loss Function
Suppose that a property owner is exposed to a random loss Y. The property owner buys an insurance policy with a deductible d such that the insurer will pay a claim in the amount of Y-d if a loss occurs with Y>d. The insuerer will pay nothing if the loss is below the deductible. Whenever a loss is above d, what is the average claim the insurer will have to pay? This is one way to look at mean excess loss function, which represents the expected excess loss over a threshold conditional on the event that the threshold has been exceeded.

Given a loss variable Y and given a deductible d>0, the mean excess loss function is e_Y(d)=E(Y-d \lvert X>d). For a continuous random variable, it is computed by

\displaystyle e_Y(d)=\frac{\int_d^{\infty} (y-d) \ f_Y(y) \ dy}{S_Y(d)}

Applying the technique of integration by parts produces the following formula:

\displaystyle e_Y(d)=\frac{\int_d^{\infty} S_Y(y) \ dy}{S_Y(d)}

It turns out that the mean excess loss function is one more way to examine the tail property of a distribution. The following is the mean excess loss function of the Pareto distribution:

\displaystyle e_X(d)=\frac{d+\alpha}{\beta-1}=\frac{1}{\beta-1} \ d + \frac{\alpha}{\beta-1}

Note that the Pareto mean excess loss function is a linear increasing function of the deductible d. This means that the larger the deductible, the larger the expected claim if such a large loss occurs! If a random loss is modeled by such a distribution, it is a catastrophic risk situation. In general, an increasing mean excess loss function is an indication of a heavy tailed distribution. On the other hand, a decreasing mean excess loss function indicates a light tailed distribution. The exponential distribution has a constant mean excess loss function and is considered a medium tailed distribution.

——————————————————————————————————————
The Pareto distribution has many economic applications. Since it is a heavy tailed distribution, it is a good candidate for modeling income above a theoretical value and the distribution of insurance claims above a threshold value.

——————————————————————————————————————

Reference

  1. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004

An example of a mixture

We use an example to motivate the definition of a mixture distribution.

Example 1

Suppose that the loss arising from an insured randomly selected from a large group of insureds follow an exponential distribution with probability density function (pdf) f_X(x)=\theta e^{-\theta x}, x>0, where \theta is a parameter that is a positive constant. The mean claim cost for this randomly selected insured is \frac{1}{\theta}. So the parameter \theta reflects the risk characteristics of the insured. Since the population of insureds is large, there is uncertainty in the parameter \theta. It is more appropriate to regard \theta as a random variable in order to capture the wide range of risk characteristics across the individuals in the population. As a result, the pdf indicated above is not an unconditional pdf, but, rather, a conditional pdf of X. The below pdf is conditional on a realized value of the random variable \Theta.

    \displaystyle f_{X \lvert \Theta}(x \lvert \theta)=\theta e^{-\theta x}, \ \ \ \ \ x>0

What about the marginal (unconditional) pdf of X? Let’s assume that the pdf of \Theta is given by \displaystyle f_\Theta(\theta)=\frac{1}{2} \ \theta^2 \ e^{-\theta}. Then the unconditional pdf of X is the weighted average of the conditional pdf.

    \displaystyle \begin{aligned}f_X(x)&=\int_0^{\infty} f_{X \lvert \Theta}(x \lvert \theta) \ f_\Theta(\theta) \ d \theta \\&=\int_0^{\infty} \biggl[\theta \ e^{-\theta x}\biggr] \ \biggl[\frac{1}{2} \ \theta^2 \ e^{-\theta}\biggr] \ d \theta \\&=\int_0^{\infty} \frac{1}{2} \ \theta^3 \ e^{-\theta(x+1)} \ d \theta \\&=\frac{1}{2} \frac{6}{(x+1)^4} \int_0^{\infty} \frac{(x+1)^4}{3!} \ \theta^{4-1} \ e^{-\theta(x+1)} \ d \theta \\&=\frac{3}{(x+1)^4} \end{aligned}

Several other distributional quantities are also weighted averages, which include the unconditional mean, and the second moment.

    \displaystyle \begin{aligned}E(X)&=\int_0^{\infty} E(X \lvert \Theta=\theta) \ f_\Theta(\theta) \ d \theta \\&=\int_0^{\infty} \biggl[\frac{1}{\theta} \biggr] \ \biggl[\frac{1}{2} \ \theta^2 \ e^{-\theta}\biggr] \ d \theta \\&=\int_0^{\infty} \frac{1}{2} \ \theta \ e^{-\theta} \ d \theta \\&=\frac{1}{2} \end{aligned}

    \displaystyle \begin{aligned}E(X^2)&=\int_0^{\infty} E(X^2 \lvert \Theta=\theta) \ f_\Theta(\theta) \ d \theta \\&=\int_0^{\infty} \biggl[\frac{2}{\theta^2} \biggr] \ \biggl[\frac{1}{2} \ \theta^2 \ e^{-\theta}\biggr] \ d \theta \\&=\int_0^{\infty} e^{-\theta} \ d \theta \\&=1 \end{aligned}

As a result, the unconditional variance is Var(X)=1-\frac{1}{4}=\frac{3}{4}. Note that the unconditional variance is not the weighted average of the conditional variance. The weighted average of the conditional variance only produces \frac{1}{2}.

\displaystyle \begin{aligned}E[Var(X \lvert \Theta)]&=\int_0^{\infty} Var(X \lvert \Theta=\theta) \ f_\Theta(\theta) \ d \theta \\&=\int_0^{\infty} \biggl[\frac{1}{\theta^2} \biggr] \ \biggl[\frac{1}{2} \ \theta^2 \ e^{-\theta}\biggr] \ d \theta \\&=\int_0^{\infty} \frac{1}{2} \ e^{-\theta} \ d \theta \\&=\frac{1}{2} \end{aligned}

It turns out that the unconditional variance has two components, the expected value of the conditional variances and the variance of the conditional means. In this example, the former is \frac{1}{2} and the latter is \frac{1}{4}. The additional variance in the amount of \frac{1}{4} is a reflection that there is uncertainty in the parameter \theta.

\displaystyle \begin{aligned}Var(X)&=E[Var(X \lvert \Theta)]+Var[E(X \lvert \Theta)] \\&=\frac{1}{2}+\frac{1}{4}\\&=\frac{3}{4}  \end{aligned}

——————————————————————————————————————-

The Definition of Mixture
The unconditional pdf f_X(x) derived in Example 1 is that of a Pareto distribution. Thus the Pareto distribution is a continuous mixture of exponential distributions with Gamma mixing weights.

Mathematically speaking, a mixture arises when a probability density function f(x \lvert \theta) depends on a parameter \theta that is uncertain and is itself a random variable with density g(\theta). Then taking the weighted average of f(x \lvert \theta) with g(\theta) as weight produces the mixture distribution.

A continuous random variable X is said to be a mixture if its probability density function f_X(x) is a weighted average of a family of probability density functions f(x \lvert \theta). The random variable \Theta is said to be the mixing random variable and its pdf g(\theta) is said to be the mixing weight. An equivalent definition of mixture is that the distribution function F_X(x) is a weighted average of a family of distribution functions indexed by a mixing variable. Thus X is a mixture if one of the following holds.

\displaystyle f_X(x)=\int_{-\infty}^{\infty} f(x \lvert \theta) \ g(\theta) \ d \theta

\displaystyle F_X(x)=\int_{-\infty}^{\infty} F(x \lvert \theta) \ g(\theta) \ d \theta

Similarly, a discrete random variable is a mixture if its probability function (or distribution function) is a weighted sum of a family of probability functions (or distribution functions). Thus X is a mixture if one of the following holds.

\displaystyle P(X=x)=\sum \limits_{y} P(X=x \lvert Y=y) \ P(Y=y)

\displaystyle P(X \le x)=\sum \limits_{y} P(X \le x \lvert Y=y) \ P(Y=y)

Additional Practice
See this blog post for practice problems on mixture distributions.

Reference

  1. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004