1. Introduction
A game of gain and loss occurs in various situations. All individuals have savings, earn income, and face expenses. Obviously, expenses, being greater than income and savings, cause inconveniences or bankruptcy. Models, trying to express such situations and measure its likelihood, are often random walk (sum of certain random variables) based. In general, random walk has various occasions: pure mathematics, insurance, engineering, computer science, physics, and many others—across all natural and related sciences. Our work is both pure and insurance mathematics shifted and we calculate probability that a certain increasing random amount will never hit some selected increasing threshold. The mentioned event is directly related to some equilibrium conditions which, in separate cases, might be deemed as axes of symmetry.
One of the most general risk models in collective risk theory is the Sparre Andersen’s risk model presented in [
1]. This model assumes that the insurers surplus process
W has the following expression:
where:
denotes the initial insurer’s surplus;
denotes the premium rate per unit of time;
The cost of claims are independent copies of a non-negative random variable (r.v.) Z;
The inter-occurrence times of claims are another sequence of independent copies of a non-negative r.v. , which is not degenerate at zero;
The sequences and are mutually independent;
is the renewal process generated by r.v. , where .
Since 1957, when the Sparre Andersen’s risk model (
1) was introduced, there occurred a significant amoutn of research papers across the world on certain versions of the model (
1). For example, [
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12] and many others. An observable break in the subject was achieved when [
7,
13,
14] were published in 1988.
In this paper, we consider the special case of the general Sparre Andersen’s model. In (
1), we set
,
, and
, where
are independent copies of an integer valued non-negative r.v.
X. Under such restrictions, for the insurers surplus process
W, we get the following expression:
Since a r.v.
X is discrete, it is enough to consider
and
for the defined model (
2). Therefore, the model we work with is given by the following formula:
and we call it the generalized premium discrete time risk model (GPDTRM). In addition, it is natural to define that
. The finite and ultimate time survival probabilities for the model presented by (
3) are correspondingly defined as:
where
.
In addition, for each , let us denote the local probabilities of r.v. X by , value of the accumulated distribution function of X by , and value of the tail distribution of X by .
The following simple statement provide us the algorithm to calculate values of the finite time survival probability.
Theorem 1. For GPDTRM presented by Formula (3), the finite time ruin probability satisfies the following equations:whereand. Proof. For
, the formula follows straightforward by the finite time survival probability definition (
4):
and for
it follows by the law of total probability and elementary rearrangements:
□
Our reason to present Theorem 1 is to see the broader view calculating the ultimate time survival probability in
Section 5 below. From definition (
4) it is easy to see that
and
for all
and
.
Let us turn to the ultimate time survival probability. By similar arguments as in proof of Theorem 1, the ultimate time survival probability of the model (
3) for all
satisfies the following relation:
Indeed, by the same arguments as in the proof of Theorem 1, we get:
We can see from the derived recurrence relation (
5) that to get the value of
we must know all the previous values
even in the case of
. In fact, we further do spins around the finding of those initial values. But first, we need to describe a net profit condition.
It is said that the net profit condition for the GPDTRM (
3) holds if:
The intuitive explanation of this condition is simple. Let us rewrite the main model Equation (
3) by the form:
From this, it follows that
and only condition
allows us to expect that
with some non-zero probability for all
. In
Section 2 and
Section 3, we assume the net profit condition be satisfied, and in
Section 4 we will prove precisely that
almost always if (
6) is not fulfillled. Breach of the net profit condition consists from two options too:
or
. Therefore, the whole structure of this paper can be seen as based on the expectation of r.v.
X: on, shift to the left, or to the right comparing to
.
It is worth mentioning that the exact recursive formulas for the finite time ruin probability
for an even more generalized model than (
3), was obtained in [
15]. Authors there derive the finite time ruin probability calculation formulas for the model:
supposing
and allowing r.v.s
to be a non-negative integer valued and independent, but not necessary identically distributed. Then, by using certain shifts, the model is generalized for certain rational values of initial surplus, premium, and claim sizes
. However, similar tricks do not work for the ultimate time ruin probability. We complete the introduction section with the following assertion on a couple of properties of
which will be often used in the later sections.
Lemma 1. For the GPDTRM (3) under the net profit conditionthe following relations hold: Proof. The proof of the first property (
7) starts with an observation that:
and the strong law of large numbers implies that:
almost surely. We can now mimic the proof of Theorem 2.3 in [
16] and derive that:
where
is an arbitrary small positive number. This implies (
7).
The second relation of the lemma follows by an observation that the lower and upper bounds of the sum in (8) are the same. Indeed,
In addition, for a temporary fixed non-negative
M,
where the last term tends to
as
and
tends to unit as
due to the derived relation (
7). Lemma is proved. □
The rest of the paper is organized as follows. In
Section 2, the algorithms are presented for the ultimate time survival probability calculation of the GPDTRM with premium rate
. Proofs of the main results for the case
are given in
Section 6. Additionally, in
Section 2, we present one special observation on the limit behavior of a certain recurrent sequence for the model (
3) with
.
Section 3 is dealt with the results on the ultimate time survival probability for model (
3) with
. The proofs of these results are presented in
Section 7.
Section 4 is devoted for the unsatisfied net profit condition and in
Section 5 a numerical calculations are given for some theoretical statements illustration.
2. Particular Cases of GPDTRM
In this section, we investigate in detail the survival probability
for model (
3) when
. In addition, we derive one interesting recurrent tendency to the expectation
when
.
At first suppose
. According to the main model Equation (
3) we have that:
where
and
are independent copies of an integer valued nonnegative r.v.
X. Due to (
5), the recursive formula of survival probability
is the following:
Below we present theorems that can be used to calculate the ultimate time survival probability for the model (
9). The first theorem describes the case
.
Theorem 2. Let us consider the model (9). Ifand, thenwhere sequencesandare defined by the following recurrent equalities:In addition, for each,and Remark 1. From the definition of the survival probability (4), it is evident thatandas. Therefore, for practical calculations we assume thatif n is sufficiently large in Theorem 2. Remark 2. According to Lemma 1, when the net profit condition holds,as. Therefore, we can get the value ofusing equality (14) of Theorem 2. Indeed, inequalities in (23) ensure thatfor all, and consequently the equality (14) implies thatfor sufficiently large n. It is hard to argue which algorithm is better for finding, however one may think that for some slowly increasingthe assumptionis more accurate than. See the Section 5 for more detailed examples on that. The net profit condition
for the model (
9) may remain satisfied if
and
. If that happens, the survival probability
for
can be calculated using the following assertion.
Theorem 3. Ifand, then: The first required initial values
and
if
, or just
if
and
, needed for the recursive relation (
10), may be calculated a bit differently than in Theorems 2 and 3. This follows from the following assertion.
Theorem 4. Let us consider the model (9). (i)
If and , then:where(ii)
If and , then where:with property for all . Remark 3. The implication ofand, or just, by Theorem 4 is evident in terms thatwhen the net profit condition holds and n is sufficiently large. The remaining values ofwhenare of course implied by (10). However, the efficiency of Theorem 4 is low when compared to Theorems 2 and 3 due to the n size to get a sufficient precision initial valuesand(oronly). See Section 5 for some explicit examples on that. Remark 4. We can not prove that the matrix in the first part of Theorem 4, formed by coefficientsand, is non-singular for all. Attempts to prove and calculations with some chosen distributions lead to the following conjecture.
Let
denote determinant of matrix in part (i) of Theorem 4, i.e.
Conjecture 1. For the defined determinants, it holds thatandfor all.
Comparing Theorems 2 and 3 to Theorem 4, one may observe that defined recurrent sequences have some interesting limit properties. For example
. A simple illustration of that can be obtained also for the discrete time risk model when we set
in (
3). It is well known (see, for example, [
17,
18,
19,
20,
21]) that for the discrete time risk model with
the survival probability for
is
. On the other hand, from (
5) with
, we can express
via
. Such thoughts lead to the following statement.
Theorem 5. For the discrete time risk model (15) with the satisfied net profit condition, it holds that:whereandfor We remind that proofs of statements of this section are given in
Section 6 below.
3. General GPDTRMs
In this Section, we proceed to develop statements for the ultimate time survival probability calculation for model (
3) when
. Due to the Remarks 2 and 3 in the previous section on algorithm efficiency, we will not develop any statements on finding initial values for (
5) straight forward by (
5) itself as in Theorem 4. In addition, we will not gather any initial values without setting differences
, and so on. The following three theorems provide us an algorithm to calculate desired values of survival probability.
Theorem 6. Let us consider the general GPDTRM with. Ifandthenfor allsatisfy the following system of equations:where coefficientsandforare:and forIn addition, By the same argumentation as in Remark 1, the right hand side of (16) tends to the zero vector as
. Therefore, for numerical calculations we assume that the right hand side of (16) is zero vector for some sufficiently large
n and obtain
by solving the system. For some chosen distributions and
we never find the matrix in (16):
to be singular for any
. However, to give a strict mathematical proof of that is challenging. The most simple version of (
17) is when
. In this particular case,
where
and, for
,
Then, for the determinant of matrix (
18):
This leads to the following conjecture, which related versions may also be found in [
22].
Conjecture 2. Ifandthen the matrix (17) is non-singular for all. In particular, if, thenand. Let us turn to cases when , but the net profit condition is still satisfied. One can observe that there are distinct versions of such a situation. In addition, one may observe that if .
Suppose that
and let
. In other words, we suppose that
and
when
. Then, by Lemma 5 in
Section 7, it holds that:
Analogically, from (
5) it follows that:
These two equalities leads to the following assertion on the survival probabilities under requirements and .
Theorem 7. Let us consider the general GPDTRM with. Ifandwhen, andthenfor allsatisfy the following equalities:where coefficientsandforare:and forIn addition, Remark 5. For the quadratic matrix in (21) formed bycoefficients forwe can not prove its non-singularity. However, we never find it being singular and conjecture that it is non-singular for any underlying distribution of r.v. X and. Our last statement of this section is a generalized version of Theorem 3.
Theorem 8. Ifandandthen: As mentioned, proofs of statements of this section are given in
Section 7 below.
5. Numerical Examples
First we give numerical examples for statements in
Section 2. We compare
calculations by Formulas (
11) and (
14) of Theorem 2 then turn to Theorem 4. Let us introduce the following notations:
where coefficients
and
are defined in Theorem 2. Since
and
, Theorem 2 implies:
Therefore, by setting
we can calculate the absolute difference of lower and upper bounds for
estimate depending on
n.
We note that in the below tables, where and are present, we limit u up to 50 or up to some lower number if rounded finite time survival probability equals 1. We also limit a variety of T when some rounded values present no difference.
Example 1. We say that a r.v. X is geometrically distributed
with parameter and denote by if Let us consider the model (9) generated by r.v. . It is clear that the model satisfies the net profit condition
, and we can fill
Table 1 of approximate values for
by rounding the numbers up to 15 decimal places.
From
Table 1 it is easy to see that
(except when we compare zeros) for all
n being present in that table. Solutions of the system in Theorem 4 are:
where the first component tends to
. However, the convergence is much slower with respect to that in the previous table. This example illustrates that Theorem 2 is more efficient to estimate
. In
Table 2 we present more values of survival probability of finite and ultimate time for model (
9) with
according to Theorems 1 and 2. All values in
Table 2 are rounded up to 3 decimal places.
Example 2. We say that a r.v. X is the shifted version of geometric distribution
with parameter (denote by if Let and let us consider the model (9) with the satisfied net profit condition . The advantage of finding
by Theorem 3 against the equality of part (ii) in Theorem 4 is not questionable. Theorem 4 gives the results being presented in
Table 3.
In
Table 4, values of survival probability (finite and ultimate time) are presented for the model in Example 2. To calculate these values we used Theorem 1 and Theorem 3. All values are rounded up to 3 decimal places.
We now turn to illustrations of the statements from
Section 3. For this we present three additional examples.
Example 3. Letagain as in Example 1 and let us consider the model (3) with. Due to Theorems 1 and 6 we fill
Table 5 with the values of
by rounding values of survival probability up to 3 decimal places.
Example 4. We say that a r.v. X follows the Pascal distribution with parametersand natural number N (denote by) if: Parameter N in Pascal distribution describes a required number of successes performing an independent experiments with success probability p each. Let us consider the model (3) withand. Then we have that
and, according to Theorems 1 and 7 we can fulfilll the
Table 6 with the values of
(rounded up to 3 decimal places as usual).
Example 5. Let us consider the GPDTRM withand.
In the case, we have
. Therefore, according to Theorems 1 and 8 we fulfill
Table 7 with the values of
.
An impact of r.v. X to survival probabilities and is well seen when comparing the last two tables. Roughly, the closer to is, the lower values of survival probabilities we get.
8. Discussion
The development of collective risk models is closely related to a random walk (r.w.), which is understood as a sum , where are i.i.d. and . The analogous description of ruin (or survival) probability is that a certain version of r.w. hits (or does not) some threshold for at least one (or none) . As mentioned, our study of a certain version of r.w. in this work are both pure and insurance mathematics shifted. On the other hand, the range of r.w. applications is wide. Not repeating possible r.w. applications, mentioned in Introduction, we could add an example of some ecosystem where a certain amount individuals live, reproduce, and die with some level of randomness.
The split between finite and ultimate
t is crucial. For example, for the model being investigated it holds that:
where
. The last change of r.v. can be well utilized in view of that what is known for discrete time risk model survival or ruin probability calculation, see
Section 5 in [
15]. However, for finite time only.
Our obtained results apparently are similar to previously known non-homogeneous risk models, see [
16,
22,
27,
28,
29,
30], where certain convolutions of random variables occur and initial values for recurrent formulas are needed. Namely, convolutions of distinct r.v.s generating some discrete time non-homogeneous risk model is the reason for not allowing to easily express ultimate time survival probability. Furthermore, most likely it will spin around ultimate time survival probability of non-homogeneous risk models incorporating the generalized premium rate studied in this work. In addition, properties of determinants, defined by a certain "long memory" recurrent sequences, at some level, forms a new research branch.