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In this paper, we consider a Brownian motion risk model with stochastic return on investments. Using the strong Markov property and exploiting the limitation idea, we derive the Laplace-Stieltjes Transform(LST) of the total duration of negative surplus. In addition, two examples are also present.

Assume that the insurance business is described by the risk process

Here, is the initial capital; is the fixed rate of premium income; is a standard Brownian motion; and is a constant, representing the diffusion volatility.

Suppose that the insurer is allowed to invest in an asset or investment portfolio. Following Paulsen and Gjessing [

where r and are positive constants. In (1.2), r is a fixed interest rate; is another standard Brownian motion independent of, standing for the uncertainty associated with the return on investments at time.

Let the risk process denote the surplus of the insurer at time under this investments assumption. Thus, associated with (1.1) and (1.2) is then the solution of the following linear stochastic integral equation:

By Paulsen [

where

Note that is a homogeneous strong Markov process, see e.g. Paulsen and Gjessing [

The risk process (1.4) can be rewritten as

Because the quadratic variational processes of

and

are the same, where is a standard Brownian motion, by Ikeda and Watanabe [3, p. 185] they have the same distribution. Thus, in distribution, we have

There are many papers concerning occupation times for different risk models. For example, for the classical surplus process with positive safety loading, Egdio dos Reis [

The remainder of the paper is organized as follows. In Section 2, we give some preliminary results. In Section 3, by exploiting the limitation idea together with the results obtained in Section 2, we obtain the LST of the total duration of negative surplus. In the last section, we present two examples.

Given, where, define

and if the set is empty,

and if the set is empty,

and if the set is empty,

.

Lemma 2.1 The risk process (1.5) has the strong Markov property: for any finite stopping time T the regular conditional expectation of given is

, that is

where is the information about the process up to time, and the equality holds almost surely.

Lemma 2.2 For, the following ordinary differential equation

has two independent solutions

and

where

Proof. From Example 2.2 of Paulsen and Gjessing [

Lemma 2.3 For, and , , define

then

where and are given by (2.2) and (2.3).

Proof. The result can be found in Chapter 16 of Breiman [

Lemma 2.4 For any, then

where is a solution of the equation

Proof. By Dynkin’s formula,

where is the generator of diffusion (1.5). It follows that

Therefore

Since is finite, it takes values with probability and with the complimentary probability. Letting, we can assert, by dominated convergence, that

Expanding the expectation on the left, we have

This, together with, gives the result (2.4).

Lemma 2.5 For, the ruin probability for the risk model (1.5) is given by

The probability that the surplus process hit the level is given by

where

Proof. By Lemma 2.4, one can derive (2.5) and (2.6).

In this section, we will derive the main result of this paper. We assume that the risk process (1.5) does not attain the critical level. For convenience, we assume that the initial surplus is positive.

Let the total duration of negative surplus be

For, define two sequences of stopping times of the process (1.5):

(if the set is empty),

(if the set is empty)in general, for recursively define

(if the set is empty),

(if the set is empty).

Let. Given for some, from the strong Markov property of the surplus process, we obtain that the periods are mutually independent and have a common distribution. Let denote the number of.

Set. By the monotone convergence theorem, we have

First we give the expression for in the following Theorem 3.1.

Theorem 3.1 For and, the LST of is given by

where and are given by Lemmas 2.3 and 2.5.

Proof. From Lemma 2.1, we can get

and

From strong Markov property of the surplus process, we get

This, together with (3.3) and (3.4), gives (3.2).

Theorem 3.2 For and, the LST of total duration of negative surplus is given by

where, and are given by Lemmas 2.3 and 2.5.

Proof. It follows from (3.1) and (3.2) that

From Lemma 2.5, it follows that

By Hospital’s rule, we get

This, together with (3.6) and (3.7), gives (3.5).

In this section we consider two examples.

Example 4.1. Letting in (1.5), we get the risk model

From Cai et al. [

are

and

where M and U are called the confluent hypergeometric functions of the first and second kind respectively. More detail on confluent hypergeometric functions can be found in Abramowitz and Stegun [

By Lemmas 2.3 and 2.5, we get

where

According to Theorems 3.1 and 3.2, we get

where, and are given by (4.2), (4.5) and (4.6).

Remark 4.1 The results (4.7) and (4.8) coincide with the main results in Wang and He [

Example 4.2. Letting and in (1.5), we get the risk model

It is easy to obtain that the two independent solutions of the ordinary differential equation

are

and

By Lemmas 2.3 and 2.5, we get

and

According to Theorems 3.1 and 3.2, we have

and

In this paper, we have studied the diffusion model incorporating stochastic return on investments. We find the LST of the total duration of negative surplus of this process. However, if the risk model (1.1) is extended to a compound Poisson surplus process perturbed by a diffusion, it is difficult to make out. We leave this problem for further research.