# The asymptotics of recovery probability in the dual renewal risk model with constant interest and debit force.

1. IntroductionThe classical risk model is specified as

U(t) = x + ct-S(t), (1)

where x [greater than or equal to] 0 is the initial surplus and c is the constant rate at which the premiums are received. The aggregate claims process {S(t)} is assumed to be a compound Poisson process, which denotes the total number of claims up to time t. Denote the time of arrival of the [i.sub.th] claim by [T.sub.i] and the size of the [i.sub.th] claim by [Y.sub.i]. More details about the surplus process can be found in Asmussen and Albrecher [1] and Rolski et al. [2]. As pointed out by Albrecher et al. [3], its dual process may also be relevant for companies whose inherent business involves a constant flow of expenses while revenues arrive occasionally due to some contingent events (e.g., discoveries and sales). For instance, pharmaceutical or petroleum companies are prime examples of companies for which it is reasonable to model their surplus process as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

Here, x is again the initial surplus, but the constant c is now the rate of expenses, assumed to be deterministic and fixed. The aggregate claims process [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is assumed to be a compound renewal process. The negative claim sequence {[X.sub.k], k = 1, 2, 3, ...} is assumed to be a sequence of independent and identically distributed (i.i.d.) nonnegative random variables (r.v.s.) with common distribution function F = 1 F; [bar.F]; we said [bar.F] is the tailed distribution. The interoccurrence times {[[theta].sub.k], k = 1, 2, 3, ...} form another sequence of i.i.d. positive random variables. {[[theta].sub.k], k = 1, 2, 3, ...} and {[X.sub.k], k = 1, 2, 3, ...} are mutually independent. The occurrence times of the successive claims, [T.sub.n] = [[summation].sup.n.sub.k=1], [[theta].sub.k], n = 1, 2, 3, ..., constitute a renewal counting process

[N.sub.t] = #{=1, 2, 3, ...: [T.sub.n] [less than or equal to] t}, t [greater than or equal to] 0. (3)

The past decade has witnessed an increasing attention on the research of dual risk model. For example, see Albrecher et al. [3] for optimal dividend problem, see Cheung and Drekic [4] for dividend approximation and dual risk model with perturbation, and see Yao et al. [5] for optimal dividend and equity issuance. Due to the positive safety loading, in dual risk model, when the operation time scale is infinite, the ruin probability of dual risk model is zero; thus, it is meaningless to discuss the ruin problem under dual risk model. However, there is a constant consumption in the dual risk model; thus, it has occasionally happened that the surplus of dual risk model is negative; in this case, the decision maker will care the probability that the surplus of the model became positive; we define this probability as the "recovery probability." This conception is of both theoretical value and practical relevance. In this paper, we focus on the asymptotic behavior of the recovery probability under the dual renewal risk model, which covers the compound Poisson dual risk model. The rest of this paper is organized as follows. In Section 2, we present an introduction to the dual renewal risk model with constant interest force and debit interest force and the problem to be investigated. Section 3 provides the main results and the corresponding proofs.

2. Model and Problem

In this section, we consider the case that the research institution would like to invest his surplus in bond market or borrow money from the bank; both the investment interest force or the debit interest force are the same, say [delta] > 0. If there are no claims in the interval (0, [DELTA]t), then the surplus up to time t is given by

U(t + [DELTA]t) = U (t) -c[DELTA]t + U (t) ([e.sup.[delta][DELTA]t] - 1) ; (4)

letting [DELTA]t [right arrow] 0, we can obtain

U'(t) = -c + [delta]U (t). (5)

It is easy to see, if U(t) > c/[delta], then U'(t) > 0; the surplus is increased and is not less than c/[delta]; if 0 < U(t) < c/[delta], then U'(t) < 0; the surplus is decreased and the insurance company may be ruin; if U(t) < 0, we know it is possible that the surplus U(t) can recover to c/[delta]. Above all, we can define the absolute positive profit as

U(t) > c/[delta]. (6)

In this paper, we will discuss the recovery probability which is the probability of the surplus recover to c/[delta] when U(t) [less than or equal to] 0.

Now we let [delta] > 0 be the constant force of interest so that after time t a capital x becomes [xe.sup.[delta]t]. Then, the total surplus which is denoted by [W.sub.[delta]](t) is given by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (7)

Now, we can define the finite-time recovery probability as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (8)

At occurrence time [T.sub.n] = [[summation].sup.n.sub.k=1][[theta].sub.k], we observe the value [W.sub.[delta]]([T.sub.n]) which represents the surplus immediately after paying the nth claim, n = 1, 2, .... By virtue of (7), we have

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (9)

Let

[V.sub.n] = [W.sub.[delta]] ([T.sub.n]) - c/[delta], n = 1,2, ...; (10)

it follows that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (11)

Since recovery can happen only at the time of a claim occurrence, we rewrite the finite-time recovery probability in (8) as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (12)

Since [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]; we obtain

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]; (13)

here, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. So we can study the recovery probability in (8) by the (13).

Similarly, for the infinite-time recovery probability defined in (13), we have

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (14)

Here and henceforth, all limit relationships are for x [right arrow] [infinity] unless stated otherwise.

For two positive functions f(x) and g(x), the relation f(x) ~ (x) amounts to the conjunction of the relations limsup f(x)/g(x) [less than or equal to] 1 and lim inf f(x)/g(x) [greater than or equal to] 1, which are denoted as f(x) [less than or equal to] g(x) and f(x) [??] g(x), respectively. Next, we will define the convolution. For two distributions F1 and [F.sub.2] on [0, [infinity]), we can say [F.sub.1] x [F.sub.2] is their convolution; that is,

[F.sub.1] x [F.sub.2] (x) = [[integral].sup.x.sub.0-] [F.sub.1] (x - y) [F.sub.2]dy, x [greater than or equal to] 0. (15)

Furthermore, we write [F.sup.1*] = F and [F.sup.n*] = [F.sup.(n-1*]) x F for every n = 2, 3, ....

For the general renewal risk model, an asymptotic expression For the finite time ruin probability is presented in [6]; that is,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (16)

In recent years, with the study of classical model and the development of the financial and insurance business, more and more people are interested in dual risk model; however, an analogue problem in insurance company is the dividend payment or high gain tax payment. Thus, the problem studied here sounds reasonable.

3. Main Results

In order to complete our results, we should have some definitions and lemmas.

Definition 1. A distribution F on [0, [infinity]) is said to belong to the class T if

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (17)

for every n [greater than or equal to] 2; that is, F [member of] T.

Definition 2. A distribution F on [0, [infinity]) is said to belong to the class S([gamma]) for some [gamma] [less than or equal to] 0 if

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (18)

for every real number y and the limit

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (19)

exists and is finite; that is, F [member of] S([gamma]). A larger class, L([gamma]), is defined by relation (18) alone.

A distribution function F concentrated on (-[infinity], [infinity]) is still said to be subexponential to the right if [F.sup.+](x) = F(x)[1.sub.(0[less than or equal to]x<[infinity])] is subexponential, and we usually denote by S the subexponential classes; see for example, [7]. Since it was introduced by [8-10], the subexponential class S has been extensively investigated by many researchers and applied to various fields. This class is often used to model claim-size distributions; see, for example, [11-13].

Definition 3. A distribution F on [0, [infinity]) is said to belong to [R.sub.-[infinity]] if

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (20)

for arbitrary y > 1.

Lemma 4. Let F, [F.sub.1], and [F.sub.2] be three distributions on [0, [infinity]) such that F [member of] S([gamma]) and that the lim [l.sub.i] = [lim.sub.x[right arrow][infinity]]([bar.[F.sub.i]](x)/[bar.F](x)) exists and is finite for i = 1,2. Then,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (21)

Proof. See [14], Proposition 2.

Lemma 5. Let [F.sub.1] and [F.sub.2] be two distributions on [0, [infinity]). If [F.sub.1] [member of] S([gamma]), [F.sub.2] [member of] L([gamma]), and [bar.[F.sub.2]](x) = [omicron]([bar.[F.sub.1]]), then [F.sub.1] * [F.sub.2] [member of] S([gamma]) and

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (22)

Proof. See [6], Corollary 1.

Lemma 6. Let F be a distribution on [0, [infinity]). If F [member of] S([gamma]), then it holds for each fixed n = 1,2, ... that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (23)

Proof. See [10], page 665.

Lemma 7. Let {[X.sub.k], k = 1,2,3, ...} be a sequence of independent and identically distributed (i.i.d.) nonnegative random variables with common distribution function F, F [member of] T. {[[sigma].sub.k], k = 1,2, ...} is a sequence of nonnegative random variables, {[[sigma].sub.k], k = 1,2, ...} and {[X.sub.k], k = 1,2, ...} are mutually independent, and if for some 0 < a [less than or equal to] b < [infinity] and all 1 [less than or equal to] k [less than or equal to] n, satisfies

P(a [less than or equal to] [[sigma].sub.k] [less than or equal to] b) = 1, (24)

then

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (25)

Proof. See [7], Proposition 5.1.

Lemma 8. Let {[N.sub.t], t [greater than or equal to] 0} be a renewal process. The interoccurrence time {[[theta].sub.k], k [greater than or equal to] 1} forms another sequence of i.i.d. positive random variable with common distribution function H; it is easy to see that [H.sub.n] which is the distribution of the nth claim occurrence time [T.sub.n] = [[summation].sup.n.sub.k =1] [[theta].sub.k] is the convolution of H. If m(t) = [EN.sub.t] is the renewal function, then we have

m(t) = [[infinity].summation over (n=1)] [H.sub.n](t). (26)

Proof. See [15], page 49.

3.1. Main Results and Proof

Theorem 9. In the dual renewal risk model with constant force of interest [delta] > 0, the number of claims {[N.sub.t], t [greater than or equal to] 0} is a renewal process, m(t) is the renewal function, and the claim sizes {[X.sub.k], k [greater than or equal to] 1} and {[N.sub.t], t [greater than or equal to] 1} are mutually independent. If F [member of] I [intersection] S([gamma]), then

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (27)

for arbitrary t > 0.

Proof. Starting with (13) and conditioning on [N.sub.t], we have

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (28)

Since [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], by Lemma 7, we have

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (29)

We know {[N.sub.t] [greater than or equal to] n} [??][T.sub.n] [less than or equal to] t; then,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (30)

since F [member of] S([gamma]), then

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (31)

So we have

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (32)

We present an asymptotic expression for the finite-time recovery probability in Theorem 9. In the following, we will show the last main result of the paper.

Theorem 10. In the dual renewal risk model with constant force of interest [delta] > 0, if F [member of] S [intersection] [R.sub.-[infinity]] for some [gamma] [greater than or equal to] 0, x [less than or equal to] 0, then

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (33)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (34)

where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is a sequence of i.i.d. positive random variables with common distribution function G.

Proof. The proof of [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the same as the one for Theorem 3.2 of [16]; we do not copy the steps here. Next, we will prove (34). Let [[??].sub.[infinity]] to be a copy of [S.sub.[infinity]] independent of {([X.sub.k], [Y.sub.k]), k = 1,2, ... j. Then, for every n = 1, 2, ...,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (35)

Therefore,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (36)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]; (37)

here [X.sup.*] = (Z | Z > c/[delta] - [x.sub.0]) is a new conditional random variable, and the distribution still belongs to the intersection S([gamma]) [intersection] [R.sub.-[infinity]]; [x.sub.0] < 0 is a small enough constant.

So from (36) and according to the (13), we can obtain

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (38)

Since

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (39)

and the Lemma 6, we can see

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (40)

We know F [member of] [R.sub.-[infinity]], so [lim.sub.x[right arrow][infinity]]([bar.F](xy)/[bar.F](x)) = 0, y > 1, that is, [bar.F](xy) = [omicron](F(x)), we can obtain [bar. F](-x/y) = [omicron]([bar.F](-x)). So

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]; (41)

we deduce that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (42)

By Lemma 4, we obtain that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (43)

so

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (44)

Then,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (45)

Clearly, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] converges to [S.sub.[infinity]] in distribution as n [right arrow] [infinity]. Therefore, by the dominated convergence theorem, the expectation on the right-hand side above converges to [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] as n [right arrow] [infinity]; that is,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (46)

It is easy to construct the corresponding asymptotic upper bound by (37). Similarly as above (by Lemmas 4 and 5),

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (47)

Clearly, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] converges to [S.sub.[infinity]] in distribution as n [right arrow] [infinity]. Therefore, similarly as above,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (48)

This completes the proof.

http://dx.doi.org/10.1155/2015/504987

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

[1] S. Asmussen and H. Albrecher, Ruin Probabilities, vol. 14, World Scientific, Singapore, 2010.

[2] T. Rolski, H. Schmidli, V. Schmidt, and J. Teugels, Stochastic Processes for Insurance and Finance, John Wiley & Sons, 2009.

[3] H. Albrecher, A. Badescu, and D. Landriault, "On the dual risk model with tax payments," Insurance: Mathematics and Economics, vol. 42, no. 3, pp. 1086-1094, 2008.

[4] E. C. K. Cheung and S. Drekic, "Dividend moments in the dual risk model: exact and approximate approaches," ASTIN Bulletin, vol. 38, no. 2, pp. 399-422, 2008.

[5] D. Yao, H. Yang, and R. Wang, "Optimal financing and dividend strategies in a dual model with proportional costs," Journal of Industrial and Management Optimization, vol. 6, no. 4, pp. 761-777, 2010.

[6] D. B. H. Cline, "Convolution tails, product tails and domains of attraction," Probability Theory and Related Fields, vol. 72, no. 4, pp. 529-557, 1986.

[7] Q. Tang and G. Tsitsiashvili, "Randomly weighted sums of subexponential random variables with application to ruin theory," Extremes, vol. 6, no. 3, pp. 171-188, 2003.

[8] V. P. Chistyakov, "A theorem on sums of independent positive random variables and its applications to branching random processes," Theory of Probability and Its Applications, vol. 9, pp. 640-648, 1964.

[9] J. Chover, P. Ney, and S. Wainger, "Functions of probability measures," Journal d'Analyse Mathematique, vol. 26, no. 1, pp. 255-302, 1973.

[10] J. Chover, P. Ney, and S. Wainger, "Degeneracy properties of subcritical branching processes," The Annals of Probability, vol. 1, no. 4, pp. 663-673, 1973.

[11] P. Embrechts and N. Veraverbeke, "Estimates for the probability of ruin with special emphasis on the possibility of large claims," Insurance Mathematics and Economics, vol. 1, no. 1, pp. 55-72, 1982.

[12] C. Kluppelberg, "Estimation of ruin probabilities by means of hazard rates," Insurance: Mathematics and Economics, vol. 8, no. 4, pp. 279-285, 1989.

[13] Q. Tang and G. Tsitsiashvili, "Finite- and infinite-time ruin probabilities in the presence of stochastic returns on investments," Advances in Applied Probability, vol. 36, no. 4, pp. 1278-1299, 2004.

[14] B. A. Rogozin and M. S. Sgibnev, "Banach algebras of measures on the real axis with the given asymptotics of distributions at infinity," Siberian Mathematical Journal, vol. 40, no. 3, pp. 565-576, 1999.

[15] A. Gut, Stopped Random Walks, Springer, Berlin, Germany, 1988.

[16] D. G. Konstantinides, K. W. Ng, and Q. Tang, "The probabilities of absolute ruin in the renewal risk model with constant force of interest," Journal of Applied Probability, vol. 47, no. 2, pp. 323-334, 2010.

Hao Wang and Lin Xu

Department of Statistics, Anhui Normal University, Wuhu, Anhui 241002, China

Correspondence should be addressed to Lin Xu; xulinahnu@gmail.com

Received 4 January 2015; Accepted 10 March 2015

Academic Editor: Alberto De Sole

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Title Annotation: | Research Article |
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Author: | Wang, Hao; Xu, Lin |

Publication: | International Scholarly Research Notices |

Date: | Jan 1, 2015 |

Words: | 3032 |

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