Articles
Asymptotic formulas for the left truncated moments of sums with consistently varying distributed increments*
Asymptotic formulas for the left truncated moments of sums with consistently varying distributed increments*
Nonlinear Analysis: Modelling and Control, vol. 26, núm. 6, pp. 1200-1212, 2021
Vilniaus Universitetas

Recepción: 01 Febrero 2021
Aprobación: 19 Julio 2021
Financiamiento
Fuente: Authors are supported by the Research Council of Lithuania, grant
Nº de contrato: S-MIP-20-16
Beneficiario: Asymptotic formulas for the left truncated moments of sums with consistently varying distributed increments*
Abstract:
In this paper, we consider the sum
of possibly dependentand nonidentically distributed real-valued random variables
with consistently varyingdistributions. By assuming that collection
follows the dependence structure, similarto the asymptotic independence, we obtain the asymptotic relations for
and
, whereαis an arbitrary nonnegative real number. The obtained results haveapplications in various fields of applied probability, including risk theory and random walks.
Keywords: sum of random variables, asymptotic independence, tail moment, truncated moment, heavy tail, consistently varying distribution.
Introduction
Let
and let
be a collection of possibly dependent real-valued random variables (r.v.s) with heavy-tailed distributions. Denote
(1)Throughout the paper, we assume that random summands have consistently varying distributions. This is a subclass of heavy-tailed distributions. We recall some definitions. We say that a distribution function (d.f.) is supported on
if its tail
satisfies
A d.f. F supported on R is said to be heavy-tailed, written as F , if for every it holds that

A d.f. F supported on
is said to be heavy-tailed, written as
, if for every
it holds that

A d.f. F on R is said to be dominatedly vaying as
, if for any fixed
(0,1)it holds that

A d.f. F on
is said to be consistently varying, written as
, if

A d.f. F on
is said to be regularly varying with index
, written as
if for any
, it holds that

It is well known (see, for instance, [5]) that

The following two indices are important to the determination whether d.f. F belongs to the aforementioned heavy-tailed distribution classes. The first index is the so-called upper Matuszewska index (see, e.g., [2, Sect. 2], [9, 23]), defined as

Another index, so-called L-index, is defined as

This index was used by [16, 19, 33], among others.
The definitions of the aforementioned heavy-tailed distribution classes imply that

The classes and have been extensively used in real analysis and various areas of probability (see, e.g., [2, 12, 25, 27]). The class of
consistently varying distributions was introduced as a generalization of the class
in [8], and was named there as a class of distributions with “intermediate regular variation”. The concept of consistent variation has been used in various papers in the context of applied probability, such as queueing systems, graph theory and ruin theory (see, e.g., [1, 3–7, 9, 13, 17, 22, 32]).
We explain some notations which will be used throughout the paper. For two positive functions f , g, we write:

In this paper, we suppose that the random variables
are pairwise quasi- asymptotically independent. This dependence structure was introduced in [7] and consid- ered in [14, 20, 21, 30, 31] and other papers. In the definition below and elsewhere, we use the standard notations:
Definition 1. Real-valued random variables
with distributions supported on
are called pairwise quasi-asymptotically independent (pQAI) if for all pairs of indices
it holds that

The following statement is Theorem 3.1 in [7]. The statement provides the asymptotic results for tail probability of sums of
r.v.s having distributions from class 
Theorem 1.Let
be a collection of real-valued
r.v.s such that 
Then

The following assertion with slightly narrower dependence structure and r.v.s from a wider class
is derived in Theorem 2.1 of [18].
Theorem 2. Let
be a collection of real-valued r.v.s such that

for all pairs of indices
In addition, suppose that 
for some 
Then

In this paper, we obtain asymptotic relationships for
(2)and
(3)for arbitrary power α [0, ) and for r.v.s
following wider,
, depen- dence structure. Asymptotic behavior of the left truncated moments of random sums was considered in various fields of applied probability, including risk theory and random walks [10,11,24]. In addition, quantity in (3) is closely related with the Haezendonck–Goovaerts risk measure (see, for instance, [15, 18, 28] and [29]). To get the precise asymptotic equivalence relationship, we consider r.v.s with d.f.s from class
The main results on the asymptotics of (2) and (3) are presented in Theorems 3 and 4 below.
The rest of the paper is organized as follows. In Section 2, we provide formulations of the main results. In Section 3, we present the proofs of the asymptotic formulas for the left truncated moments of
. The last Section 4 deals with the examples illustrating the obtained results.
Main results
The first assertion generalizes results of Theorem 1 which can be derived from theorem below by supposing
. In addition, for class
, theorem below gives an analogous result to Theorem 2 for r.v.s
following a wider dependence structure and for a real-valued nonnegative moment order α.
Theorem 3. Let
be a collection of real-valued
r.v.s such that
and
Then
(4)The second theorem shows that the asymptotic behaviour of the left truncated mo- ments of sums depends on consistently varying distributed increments but does not depend on asymptotically lighter increments.
There 4. let
be a collectin of real-valued r,v.s such that ,for each 
, it holds that
. Suppose that
and
and some
be a subset of indices k such that
. If the subcollection
consists of
r.v.s, then,
for each 
(5)and, for
it holds that
(6)We notice that the basic index in the formulation of Theorem 4, which is equal to one, can be replaced by any index
. In addition, it should be noted that depen- dence of r.v.s
, as well as mutual dependence between the sets
and
, can be arbitrary.
Proofs of main results
We present two auxiliary lemmas before providing proofs of the main results.
Lemma 1. Let
be a real-valued r.v. such that
for some
Then, for any
, we have
(7)and
(8)Proof. Both equalities of the lemma follow directly from the following well-known formula
(9)provided that
and η is a nonnegative r.v. (see, for instance, [26, p. 208, Cor. 2]).
Namely, by supposing
, from (9) we obtain

and equality (7) follows.
Similarly, by supposing
, from (9) equality (8) holds because

Lemma 2. Let
and η be two arbitrarily dependent r.v.s. If
and 
then
(10)Proof. Proof of the lemma is presented in [34] (see part (i) of Lemma 3.3).
Proof of Theorem 3. In the case
the assertion of Theorem 3 follows from Theo- rem 1 immediately. Hence, further, we can suppose that α is positive. By Lemma 1, for all
, we have

due to right inequality in min-max inequality
(11)provided that
and
.
By Theorem 1 we get
(12)Similarly, using the left inequality in (11), we obtain
(13)The derived estimates (12) and (13) complete the proof of Theorem 3.
Proof of Theorem 4. If
, then relation (5) follows immediately from The- orem 3. Hence, let us suppose that
and denote

Summands in
are
r.v.s with consistently varying d.f.s. Hence, Theorem 1 implies that
(14)This asymptotic relation and inequality (11) imply that d.f.
belongs to the class
due to the following estimate

provided that y (0, 1).
In addition, each r.v.
k with index
satisfies condition
according to requirements of the theorem. The fact that Fξ1 and asymptotic equality (14) imply that
(15)because

where 
Consequently, Lemma 2 and asymptotic relations (14), (15) imply that
(16)Hence, the first relation (5) of Theorem 4 holds in the case
then using the first equality of Lemma 1 and estimates of (11), similarly as in the proof of
Theorem 3, we derive that

Relation (5) of Theorem 4 for
follows now from (16).
Importar imagenThe second asymptotic relation (6) can be obtained in a similar way by using the second equality of Lemma 1, relation (16) and estimate (11). Theorem 4 is proved.
Examples
In this section, we provide two examples illustrating our main results.
Example 1. Let r.v.s
satisfy the assumptions of Theorem 3. Suppose that for each k, r.v.
is a copy of r.v.
, where
are independent,
is uniformly distributed on interval [0, 1], and
is geometrically distributed with parameter q ∈ (0, 1), i.e.,
. We derive the asymptotic formulas for

in the case of 0 ≤ α < log2(1/q), where
as usual.
Due to considerations on pages 122–123 of [5],
. In addition, for
, we have

where symbol
denotes the integer part of a real number a, symbol
denotes the fractional part of a, and function f is defined by the following equality

For the function f , we have


Consequently, for
,

where B denotes the Beta function

These relations and theorems 3, 4 imply that

for
(0, 1) and
and

for all
(0, 1) and 
The derived asymptotic formulas imply the following particular cases:


if q ∈ (0, 1/4).
Example 2. Let r.v.s
, be pQAI. Suppose that
is distributed according to the following tail function

For other indices
let us suppose that

Like in Example 1, we write asymptotic formulas for the left truncated moments

in the case of suitable α.
It is obvious that
, and, further,
du to results of [9] (see page 87)
Therefore, Theorem 4 implies that

and

Consequently,

and, for α ∈ (0, 1),

Acknowledgments
We would like to thank the four anonymous referees for the detailed and helpful comments on the previous versions of the manuscript.
References
1. I.M. Andrulyte˙, M. Manstavicˇius, J. Šiaulys, Randomly stopped maximum and maximum of sums with consistently varying distributions, Mod. Stoch., Theory Appl., .:65–78, 2017, https://doi.org/10.15559/17-VMSTA74.
2. N.H. Bingham, C.M. Goldie, J.L. Teugels, Regular Variation, Encycl. Math. Appl., Vol 27, Cambridge Univ. Press, Cambridge, 1987, https://doi.org/10.1017/ CBO9780511721434.
3. M. Bloznelis, Local probabilities of randomly stopped sums of power-law lattice random variables, Lith. Math. J., 59:437–468, 2019, https://doi.org/10.1007/s10986- 019-09462-9.
4. V.V. Buldygin, K.H. Indlekofer, O.I. Klesov, J.G. Steinebach, Pseudo-Regularly Varying Functions and Generalized Renewal Processes, Springer-Verlag, Switzerland, 2018, https: //doi.org/10.1007/978-3-319-99537-3
5. J. Cai, Q. Tang, On max-sum equivalence and convolution closure of heavy-tailed distributions and their applications, J. Appl. Probab., 41:117–130, 2004, https://doi.org/10. 1239/jap/1077134672.
6. Y. Cang, Y. Yang, X. Shi, A note on the uniform asymptotic behavior of the finite-time ruin probability in a nonstandard renewal risk model, Lith. Math. J., 60:161–172, 2020, https: //doi.org/10.1007/s10986-020-09473-x
7. Y. Chen, K.C. Yuen, Sums of pairwise quasi-asymptotically independent random variables with consistent variation, Stoch. Models, 25:76–89, 2009, https://doi.org/10.1080/ 15326340802641006.
8. D.B.H. Cline, Intermediate regular and Π variation, Proc. Lond. Math. Soc., 68:594–611, 1994, https://doi.org/10.1112/plms/s3-68.3.594.
9. D.B.H. Cline, G. Samorodnitsky, Subexponentiality of the product of independent random variables, Stochastic Processes Appl., 49:75–98, 1994, https://doi.org/10.1016/ 0304-4149(94)90113-9.
10. Z. Cui, Y. Wang, K. Wang, Asymptotics for moments of the overshoot and undershoot of a random walk, Adv. Appl. Probab., 41:469–494, 2009, https://doi.org/10.1239/ aap/1246886620.
11. Z. Cui, Y. Wang, K. Wang, The uniform local asymptotics for a Lévy process and its overshoot and undershoot, Commun. Stat., Theory Methods, 45:1156–1181, 2016, https://doi. org/10.1080/03610926.2013.861492.
12. P. Embrechts, C. Klüppelberg, T. Mikosch, Modelling Extremal Events for Insurance and Finance, Springer-Verlag, New York, 1997, https://doi.org/10.1198/jasa. 2002.s455.
13. S. Foss, D. Korshunov, S. Zachary, An Introduction to Heavy-Tailed and Subexponential Dis- tributions, 2nd ed., Springer-Verlag, New York, 2013, https://doi.org/10.1007/ 978-1-4419-9473-8.
14. J. Geluk, Q. Tang, Asymptotic tail probabilities of sums of dependent subexponential random variables, J. Theor. Probab., 22:871–882, 2009, https://doi.org/10.1007/s10959- 008-0159-5.
15. J. Haezendonck, M.J. Goovaerts, A new premium calculation principle based on Orlicz norms, Insur. Math. Econ., .:41–53, 1982, https://doi.org/10.1016/0167-6687(82) 90020-8.
16. E. Jaune˙, O. Ragulina, J. Šiaulys, Expectation of the truncated randomly weighted sums with dominatedly varying summands, Lith. Math. J., 58:421–440, 2018, https://doi.org/ 10.1007/s10986-018-9408-1.
17. E. Kizinevicˇ, J. Sprindys, J. Šiaulys, Randomly stopped sums with consistently varying distri- butions, Mod. Stoch., Theory Appl., .:165–179, 2016, https://doi.org/10.15559/ 16-VMSTA60.
18. R. Leipus, S. Paukštys, J. Šiaulys, Tails of higher-order moments of sums with heavy-tailed increments and application to the Haezendonck–Goovaerts risk measure, Stat. Probab. Lett., 170:108998, 2021, https://doi.org/10.1016/j.spl.2020.108998.
19. R. Leipus, J. Šiaulys, I. Vareikaite˙, Tails of higher order moments with dominatedly varying summands, Lith. Math. J., 59:389–407, 2019, https://doi.org/10.1007/s10986- 019-09444-x.
20. J. Li, On pairwise quasi-asymptotically independent random variables and their applications, Stat. Probab. Lett., 83:2081–2087, 2013, https://doi.org/10.1016/j.spl.2013. 05.023.
21. X. Liu, Q. Gao, Y. Wang, A note on a dependent risk model with constant interest rate, Stat. Probab. Lett., 82:707–712, 2011, https://doi.org/10.1016/j.spl.2011. 12.016.
22. I. Matsak, Asymptotic behavior of maxima of independent random variables, Lith. Math. J., 59:185–197, 2019, https://doi.org/10.1007/s10986-019-09437-w.
23. W. Matuszewska, On generalization of regularly increasing functions, Stud. Math., 24:271– 279, 1964, https://doi.org/10.4064/sm-24-3-271-279.
24. H.S. Park, R.A. Maller, Moment and MGF convergence of overshoots and undershoots for Lévy insurance risk processes, Adv. Appl. Probab., 40:716–733, 2008, https://doi. org/10.1239/aap/1222868183.
25. S.I. Resnick, Extreme Values, Regular Variation, and Point Processes, Springer-Verlag, New York, 1987, https://doi.org/10.1007/978-0-387-75953-1.
26. A.N. Shiryaev, Probability, Springer-Verlag, New York, 1995.
27. J. Sprindys, J. Šiaulys, Regularly distributed randomly stopped sum, minimum and maximum, Nonlinear Anal. Model. Control, 25:509–522, 2020, https://doi.org/10.15388/ namc.2020.25.16661.
28. Q. Tang, F. Yang, On the Haezendonck–Goovaerts risk measure for extreme risks, Insur. Math. Econ., 50:217–227, 2012, https://doi.org/10.1016/j.insmatheco.2011.11. 007.
29. Q. Tang, F. Yang, Extreme value analysis of the Haezendonck–Goovaerts risk measure with a general Young function, Insur. Math. Econ., 59:311–320, 2014, https://doi.org/10. 1016/j.insmatheco.2014.10.004.
30. K. Wang, Randomly weighted sums of dependent subexponential random variables, Lith. Math. J., 51:573–586, 2011, https://doi.org/10.1007/s10986-011-9149-x.
31. S. Wang, C. Chen, X. Wang, Some novel results on pairwise quasi-asymptotical independence with applications to risk theory, Commun. Stat., Theory Methods, 46:9075–9085, 2017, https://doi.org/10.1080/03610926.2016.1202287.
32. S. Wang, D. Guo, W. Wang, Closure property of consistently varying random variables based on precise large deviation principles, Commun. Stat., Theory Methods, 48:2218–2228, 2019, https://doi.org/10.1080/03610926.2018.1459717.
33. Y. Yang, Y. Wang, R. Leipus, J. Šiaulys, Asymptotics for tail probability of total claim amount with negatively dependent claim sizes and its applications, Lith. Math. J., 49:337–352, 2009, https://doi.org/10.1007/s10986-009-9053-9.
34. Y. Yang, K.C. Yuen, J.F. Liu, Asymptotics for ruin probabilities in Lévy-driven risk models with heavy-tailed claims, J. Ind. Manag. Optim., 14:231–247, 2018, https://doi.org/ 10.3934/jimo.2017044.
Notes