Citation: Dimitrios G. Konstantinides. Ruin probabilities for a double renewal risk model with frequent premium arrivals[J]. Quantitative Finance and Economics, 2018, 2(3): 717-732. doi: 10.3934/QFE.2018.3.717
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We consider the asymptotics of ruin probabilities in the renewal risk model with constant force of interest. In this model the claim sizes,
N1(t)=#{n:τn≤t},t≥0. |
Therefore, the compound renewal process
The premium sizes,
N2(s)=#{n:σn≤s}=N(Λs),s≥0. |
where
Let
˜Uδ(t)=xeδt+∫t0eδ(t−y)dC(y)−∫t0eδ(t−x)dS(x),t≥0. |
Let consider the discounted surplus through the formula
Uδ(t):=˜Uδ(t)e−δt=x+∫t0e−δydC(y)−∫t0e−δxdS(x)=x+N(Λt)∑k=1Yke−δσk−N1(t)∑n=1Zne−δτn, |
for any
Now we introduce a sequence of random variables
Considering as given, that the premium arrivals are much more frequent in comparison with the occurrences of claims, we take as basic time cycle the inter-occurrence times. In practical set up, the premium can be received every week but the claims are expected to occur every year. Let introduce successively the following random variables
X1=Z1−N(Λτ1)∑k=1Y∗keδ(τ1−σk)=Z1−N(Λθ1)∑k=1Y∗keδ(θ1−σk),X2=Z2−N(Λτ2)∑k=N(Λτ1)+1Y∗keδ(τ2−σk),………Xn=Zn−N(Λτn)∑k=N(Λτn−1)+1Y∗keδ(τn−σk), | (1.1) |
through which we obtain
Uδ(t)d=x−N1(t)∑n=1Xne−δτn, | (1.2) |
for any
In the actuarial literature, the probability of ultimate ruin is defined to be the probability that the surplus falls below zero. This probability has been extensively investigated.
Let us define the ultimate ruin probability as
ψδ(x)=P(infs≥0Uδ(s)<0|Uδ(0)=x)=P(M∞>x),x≥0, | (1.3) |
which represents the distribution tail of the supremum
M∞:=supm≥1m∑n=1Xne−δτn=supm≥1Sm, | (1.4) |
with
Here and henceforth, all limit relationships are for
A real-valued random variable
{\bf E}\left[ e^{\varepsilon X}\right] = \int_{-\infty}^{\infty}e^{\varepsilon x}\, F(dx) = \infty\, . |
A distribution
\lim \dfrac{\bar F(x-y)}{\bar F(x)} = 1\, . |
A distribution
\lim \dfrac{\overline{F^{n*}}(x)}{\bar F(x)} = n\, , |
where
A distribution
\limsup \dfrac{\bar F(x\, y)}{\bar F(x)} < \infty\, . |
The intersection
A distribution
\lim\limits_{y \uparrow 1} \limsup \dfrac{\bar F(x\, y)}{\bar F(x)} = 1\, , |
or equivalently the following holds
\lim\limits_{y \downarrow 1} \limsup \dfrac{\bar F(x\, y)}{\bar F(x)} = 1\, . |
A distribution
y^{-\alpha} \leq \liminf \dfrac{\bar F(x\, y)}{\bar F(x)} \leq \limsup \dfrac{\bar F(x\, y)}{\bar F(x)} \leq y^{-\beta}\, . |
A distribution
\lim \dfrac{\bar F(x\, y)}{\bar F(x)} = y^{-\alpha}\, . |
It is well known that
\mathcal{R}_{-\alpha} \subset ERV(-\beta, \, -\alpha) \subset \mathcal{C} \subset \mathcal{B} \subset \mathcal{S} \subset \mathcal{L} \subset \mathcal{K}\, . |
For a distribution
\beta_F = - \lim \dfrac{\ln M^F(x)}{\ln x}\, , \qquad \alpha_F = - \lim \dfrac{\ln M_F(x)}{\ln x}\, , |
where for any
M^F(x) = \limsup\limits_{u \to \infty} \dfrac {\bar F(x\, u)}{\bar F(u)}\, , \qquad M_F(x) = \limsup\limits_{u \to \infty} \dfrac {\bar F(x\, u)}{\bar F(u)}\, , |
If
For a subexponential distribution
\limsup \dfrac{\bar F(v\, x)}{\bar F(x)} < 1\, . |
If
Let consider the sequence of real-valued random variables
\lim {\bf P}[|X_i|\wedge X_j > x \;\big|\;X_i \vee X_j > 0] = 0\, . |
Further following the work (Geluk and Tang, 2009) we say that the
\lim\limits_{x_i \wedge x_j \to \infty} {\bf P}[|X_i| > x_i \;\big|\;X_j > x_j ] = 0\, . |
We say that the
{\bf P}\left[ \bigcap\limits_{k = 1}^n \{ X_k \leq x_k\} \right] \leq g_L(n)\, \prod\limits_{k = 1}^n {\bf P}[X_k \leq x_k]\, , \\[2mm] {\bf P}\left[ \bigcap\limits_{k = 1}^n \{ X_k > x_k\} \right] \leq g_U(n)\, \prod\limits_{k = 1}^n {\bf P}[X_k > x_k]\, , |
hold. This dependent structure was introduced in (Wang et al., 2003).
We say that the
{\bf P}\left[ \bigcap\limits_{k = 1}^n \{ X_k \leq x_k\} \right] \leq M\, \prod\limits_{k = 1}^n {\bf P}[X_k \leq x_k]\, , \\[2mm] {\bf P}\left[ \bigcap\limits_{k = 1}^n \{ X_k > x_k\} \right] \leq M\, \prod\limits_{k = 1}^n {\bf P}[X_k > x_k]\, , |
hold. This notion was introduced in (Liu, 2009).
When in these two relations the value of the constant is
It is well known the inclusions
NQD \subset END \subset WOD \subset TAI \subset pQAI\, . |
Now, we study the asymptotic behaviour of the distribution tail of the discounted sums in (1.1). By the total probability formula we obtain
\begin{eqnarray*} &&{\bf P}\left(\sum\limits_{k = 1}^{N(\Lambda\, \theta_{1})} Y_{k}e^{\delta (\theta_{1} - \sigma_{k})}>x \right)\\[2mm] & = & \int_0^\infty\, \int_0^\infty {\bf P}\left(\sum\limits_{k = 1}^{N(q t)} Y_{k}e^{\delta (t - \sigma_{k})}>x \:\Big|\:\, \theta_{1} = t, \; \Lambda = q \right)\, Q(dq)\, A(dt)\\[2mm] & = &\int_0^\infty\, \int_0^\infty\, \sum\limits_{n = 1}^\infty {\bf P}\left(\sum\limits_{k = 1}^{n} Y_{k}e^{\delta (t - \sigma_{k})}>x \:\Big|\: \, \theta_{1} = t, \;\Lambda = q, \; N(qt) = n\right)\, {\bf P}(N(qt) = n)\, \\[2mm] &&\times \, Q(dq)\, A(dt)\, . \end{eqnarray*} |
Next, we employ Theorem 2.3.1 from (Ross, 1983) to express the conditional distribution of the random vector
\begin{eqnarray*} &&{\bf P}\left(\sum\limits_{k = 1}^{N(\Lambda \, \theta_{1})} Y_{k}e^{\delta (\theta_{1} - \sigma_{k})}>x \right)\\[2mm] & = &\int_0^\infty\, \int_0^\infty\, \sum\limits_{n = 1}^\infty {\bf P}\left(\sum\limits_{k = 1}^{n} Y_{k}e^{\delta t U_{k}}>x \:\Big|\: \, \theta_{1} = t, \;\Lambda = q, \; N(qt) = n\right)\, {\bf P}(N(qt) = n)\\[2mm] &&\times\, Q(dq)\, A(dt) = \int_0^\infty\, {\bf P}\left(\sum\limits_{k = 1}^{N(\Lambda t)} Y_{k}e^{\delta t U_{k}}>x \:\Big|\: \, \theta_{1} = t\right)\, A(dt)\, . \end{eqnarray*} |
Let us denote
Lemma 2.1. If the random variables
\begin{equation*} {\bf P}\left(\sum\limits_{k = 1}^{N(\Lambda_t)} Y_{k}e^{\delta t U_{k}}>x \right) \sim {\bf P}\left(N(\Lambda_t)\, \mu_t>x \right)\, , \end{equation*} |
for any
Proof. We check the conditions of Theorem 4.1 (b) from (Chen et al., 2010). As far the uniform random variables
From the fact that the products are non-negative and non-degenerate, we obtain the positive mean value
Next, from the fact that
\dfrac{N^{\leftarrow}(y\, x)}{N^{\leftarrow}(x)} \to y\, , |
from where we get
\begin{array}{l} \lim\limits_{y \uparrow 1} \limsup \dfrac{{\bf P}[N(\Lambda_t) > y\, x]}{{\bf P}[N(\Lambda_t) > x]} & = & \lim\limits_{y \uparrow 1} \limsup \dfrac{{\bf P}[\Lambda > N^{\leftarrow}(y\, x)/t]}{{\bf P}[\Lambda > N^{\leftarrow}(x)/t]} \\[2mm] & = & \lim\limits_{y \uparrow 1} \limsup \dfrac{{\bf P}[\Lambda > y\, N^{\leftarrow}(x)/t]}{{\bf P}[\Lambda > N^{\leftarrow}(x)/t]} = 1\, , \end{array} |
where the last equality comes from
\begin{equation*} {\bf P}\left(Y e^{\delta t U }>x \right) = o\left({\bf P}\left[ N(\Lambda_t) > x\right] \right)\, . \end{equation*} |
Now we just apply Theorem 4.1 (b) from (Chen et al., 2010) to take the required result.
We observe that
Lemma 2.2. In addition to the other conditions of Lemma 2.1, if
\begin{equation*} {\bf P}\left(\sum\limits_{k = 1}^{N(\Lambda_t)} Y_{k}e^{\delta t U_{k}}>x \right) \sim {\bf P}\left(\Lambda_t> \dfrac x{\mu_t} \right)\, , \end{equation*} |
for any
Proof. Following the expression found in Lemma 2.1, for any
\begin{eqnarray*} &&{\bf P}\left(N(\Lambda_t)> \dfrac x{\mu_t} \right) = \int_0^\infty\, {\bf P}\left(N(q)> \dfrac x{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq)\\[2mm] && = \left(\int_0^{ x/({\mu_t}+\varepsilon)} + \int_{x/({\mu_t}+\varepsilon)}^{x/({\mu_t}-\varepsilon)} + \int_{x/({\mu_t}-\varepsilon)}^{\infty} \right)\, {\bf P}\left(N(q)> \dfrac x{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq) \\[2mm] && = I_1 + I_2 + I_3\, . \end{eqnarray*} |
Let us observe that the main term is the last one. Indeed, taking into account the SLLN we obtain the convergence
\begin{eqnarray*} &&I_3 = \int_{x/({\mu_t}-\varepsilon)}^{\infty} \, {\bf P}\left(N(q)> \frac x{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq) \\[2mm] &&\geq \int_{x/({\mu_t}-\varepsilon)}^{\infty} \, {\bf P}\left(\dfrac{ N(q)}{q}> \dfrac {{\mu_t} -\varepsilon}{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq)\\[2mm] && \to {\bf P}\left(\Lambda_t > \dfrac x{{\mu_t} -\varepsilon}\right)\, , \end{eqnarray*} |
as
\begin{eqnarray*} I_3 = \int_{x/({\mu_t}-\varepsilon)}^{\infty} \, {\bf P}\left(N(q)> \dfrac x{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq) \leq {\bf P}\left(\Lambda_t > \dfrac x{{\mu_t} -\varepsilon}\right)\, , \end{eqnarray*} |
so after leaving the
\begin{equation*} I_3 \sim {\bf P}\left(\Lambda_t> \dfrac x{\mu_t} \right)\, . \end{equation*} |
Next, we calculate the asymptotics of
\begin{eqnarray*} &&I_2 = \int_{x/({\mu_t}+\varepsilon)}^{x/({\mu_t}-\varepsilon)} \, {\bf P}\left(N(q)> \dfrac x{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq) \\[2mm] &&\leq {\bf P}\left(\dfrac x{{\mu_t} +\varepsilon}\leq \Lambda_t \leq \dfrac x{{\mu_t} -\varepsilon}\right) = {\bf P}\left(\Lambda_t \leq \dfrac x{{\mu_t} -\varepsilon}\right) - {\bf P}\left(\Lambda_t \leq \dfrac x{{\mu_t} +\varepsilon}\right)\\[2mm] && \sim [({\mu_t} + \varepsilon)^\alpha - ({\mu_t} - \varepsilon)^\alpha] {\bf P}\left(\Lambda_t > x \right) = o\left[ {\bf P}\left(\Lambda_t> \dfrac x{\mu_t} \right) \right]\, , \end{eqnarray*} |
as
Next, we consider
\begin{eqnarray*} I_1& = & \int_0^{ x/({\mu_t}+\varepsilon)} {\bf P}\left(N(q)> \dfrac x{\mu_t} \:\Big|\: \Lambda_t = q \right)\, {\bf P}(\Lambda_t \in dq) \\[2mm] &\leq& {\bf P}\left[ N\left(\dfrac x{{\mu_t} +\varepsilon}\right)> \dfrac x{\mu_t} \right] = {\bf P}\left[ \sum\limits_{i = 1}^{\left\lfloor x/{\mu_t} \right\rfloor} \zeta'_i \leq \dfrac x{{\mu_t} +\varepsilon} \right]\, . \end{eqnarray*} |
Now for an arbitrarily chosen variable
\begin{eqnarray*} I_1 &\leq& \exp\left\{ h\, \dfrac x{{\mu_t} +\varepsilon} \right\}\, {\bf E}\left[ \exp\left\{ -h\, \sum\limits_{i = 1}^{\left\lfloor x/{\mu_t} \right\rfloor} \zeta'_i \right\} \right] \\[2mm] &\sim&\exp\left\{ h\, \dfrac x{{\mu_t} +\varepsilon} \right\}\, \left({\bf E}\left[e^{ -h\, \zeta'_1 } \right]\right)^{ x/{\mu_t} } \sim\exp\left\{ \left(h\, \dfrac {\mu_t}{{\mu_t} +\varepsilon} + \ln {\bf E}\left[ e^{ -h\, \zeta'_1 } \right]\right) \, \dfrac{ x}{\mu_t} \right\} \, . \end{eqnarray*} |
Now we choose some positive value for
\begin{equation*} v(h) : = h\, \dfrac {\mu_t}{{\mu_t} +\varepsilon} + \ln {\bf E}\left(e^{ -h\, \zeta'_1 } \right)\, , \end{equation*} |
becomes negative. This is possible because for
\begin{equation*} v'(h) : = \dfrac {\mu_t}{{\mu_t} +\varepsilon} - \dfrac{ {\bf E}\left(\zeta'_1\, e^{ -h\, \zeta'_1 }\right)}{{\bf E}\left(e^{ -h\, \zeta'_1 } \right)}\, , \end{equation*} |
due to the fact that
\begin{equation*} I_1 \sim \exp\left\{ v(h)\, \dfrac x{{\mu_t}} \right\} = o\left[{\bf P}\left(\Lambda_t > \dfrac x{{\mu_t}}\right) \right]\, , \end{equation*} |
which makes the first term also negligible.
Next, consider the case with regular varying tails of distributions of the random variables
\begin{equation} \label{eq.4.1} \overline{F} (x) = {\bf P}[X >x] = {\bf P}\left(Z - \sum\limits_{k = 1}^{N(\Lambda_\theta)} Y_{k}^*\, e^{\delta (\theta- \sigma_{k})}>x \right)\, , \end{equation} | (3.1) |
From Theorem 3.1 in (Tang and Tsitsiashvili, 2003) we can find easily:
Lemma 3.1. If
\bar F(x) = o(x^{-\beta}), \qquad \forall\; \beta < \beta_{F}\, , \\[2mm] x^{\alpha} \bar F(x) \rightarrow \infty, \qquad \forall\; \alpha > \alpha_{F}\, , \\[2mm] 0 \leq \beta_{ F} \leq \alpha_{F} < \infty, |
hold.
Now we assume that the joint distribution of
\begin{equation*} \frac{1}{\overline{B}(x)}\mathbb{P}\left(\frac{(\Lambda, Z)}{x}\in \cdot \right) \overset{v}{\rightarrow }\nu \left(\cdot \right) \quad \text{ on } \left[ 0, \infty \right] ^{d}\backslash \left\{ {\bf 0}\right\} . \end{equation*} |
In this case, we write
We introduce now the event
\begin{equation*} A_{x, \, t} : = \left\{ (Z, \, \Lambda)\::\: Z - \Lambda_t\, {\mu_t} > x \right\} = \left\{ (Z, \, \Lambda)\::\: Z - \Lambda \, t\, {\bf E}\left[ Y_{1}\, e^{\delta t U_{1}}\right] > x \right\}\, , \end{equation*} |
for any
Lemma 3.2. The following asymptotic relation is true
{\bar F}(x) \sim \bar B(x)\, E\left[\nu(A_{1, \theta}) \right]\, . |
Proof. Through Lemma 3.1 we find that
\begin{eqnarray*} \overline{F}(x) & = & \int_0^\infty P\left[ \sum\limits_{m = 1}^{N(\Lambda_\theta)} Y_m^* \, e^{\delta (\theta - \sigma_m) } < z - x\right] B(dz)\\[2mm] & = & \int_0^\infty \left(1- \int_0^\infty P\left[ \sum\limits_{m = 1}^{N(\Lambda_t)} Y_m^* \, e^{\delta \, t\, U_m } \geq z - x \:\Big|\:\theta = t \right]\, A(dt) \right) B(dz)\\[2mm] &\sim& \int_0^\infty \left(1- \int_0^\infty P\left[ \Lambda_t\, \mu_t \geq z - x \:\Big|\:\theta = t \right]\, A(dt) \right) B(dz)\\[2mm] & = & \int_0^\infty P\left[ \Lambda_t\, \mu_t < z - x \:\Big|\:\theta = t \right]\, A(dt) \, B(dz)\\[2mm] & = & \int_0^\infty P\left[ Z -\Lambda_t\, \mu_t > x \:\Big|\:\theta = t \right]\, A(dt) \, . \end{eqnarray*} |
Now we employ the multivariate regular variation of the
\begin{eqnarray*} \overline{F}(x) & = & \int_0^\infty P\left[ \sum\limits_{m = 1}^{\Lambda_\theta} Y_m^* \, e^{\delta (\theta - \sigma_m) } < z - x\right] B(dz)\\[2mm] &\sim& \int_0^\infty \overline{B}(x)\, \nu\left[ A_{1, \, t} \right]\, A(dt) = \overline{B}(x)\, {\bf E}\left(\nu\left[ A_{1, \, \theta} \right] \right)\, . \end{eqnarray*} |
Proposition 3.3. Let the real-valued random variables
\psi_{\delta}(x) \sim \sum\limits_{n = 1}^\infty {\bf P}[e^{-\delta \tau_n}\, X_n >x]\, , |
holds if either of the following conditions are true:
(i) If
\sum\limits_{n = 1}^\infty ({\bf E}[e^{-\alpha\, \delta\, \tau_n}] + {\bf E}[e^{-\beta\, \delta\, \tau_n}]) < \infty\, . |
(ii) If
\sum\limits_{n = 1}^\infty ({\bf E}[e^{-\alpha\, \delta\, \tau_n}] + {\bf E}[e^{-\beta\, \delta\, \tau_n}])^{1/\alpha} < \infty\, . |
Proof. We follow the argument developed in Theorem 2 from (Yi et al., 2011). However, we omit the condition
We begin with the lower asymptotic bound. For any
\begin{array}{l} \psi_{\delta}(x) & = &{\bf P}[\sup\limits_{n\geq 1} \sum\limits_{k = 1}^n e^{-\delta \tau_k}\, X_k >x] \geq \dfrac{{\bf P}[\sup\limits_{1\leq n \leq m} \sum\limits_{k = 1}^n e^{-\delta \tau_k}\, X_k >x]}{\sum\limits_{k = 1}^m{\bf P}[ e^{-\delta \tau_k}\, X_k >x]}\, \sum\limits_{k = 1}^m{\bf P}[ e^{-\delta \tau_k}\, X_k >x]\\[2mm] &\gtrsim& \liminf\limits_{x \to \infty} \sum\limits_{k = 1}^m{\bf P}[ e^{-\delta \tau_k}\, X_k >x]\\[2mm] & = & \liminf\limits_{x \to \infty} \left(\sum\limits_{k = 1}^\infty {\bf P}[ e^{-\delta \tau_k}\, X_k >x]- \sum\limits_{k = m+1}^\infty {\bf P}[ e^{-\delta \tau_k}\, X_k >x]\right)\\[2mm] & = & \sum\limits_{k = 1}^\infty {\bf P}[ e^{-\delta \tau_k}\, X_k >x]\, \left(1- \limsup\limits_{x \to \infty} \dfrac{\sum\limits_{k = m+1}^\infty {\bf P}[ e^{-\delta \tau_k}\, X_k >x]}{\sum\limits_{k = 1}^\infty {\bf P}[ e^{-\delta \tau_k}\, X_k >x]}\right)\, , \end{array} |
where in the second line we used Theorem 2.1 from (Ignataviciute et al., 2018) in combination with Theorem 1 from (Yi et al., 2011). Further by Theorem 3.3 from (Cline and Samorodnitksy, 1994) we have
\psi_{\delta}(x) \gtrsim \sum\limits_{k = 1}^\infty {\bf P}[ e^{-\delta \tau_k}\, X_k >x]\, \left(1- C\, \sum\limits_{k = m+1}^\infty ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}])\right)\, . |
Next, letting
For the upper asymptotic bound we see that for any
\psi_{\delta}(x) \leq {\bf P}\left[\sup\limits_{1 \leq n \leq m}\sum\limits_{k = 1}^n e^{-\delta \tau_k}\, X_k >(1-v)\, x\right] + {\bf P}\left[\sum\limits_{k = m+1}^\infty e^{-\delta \tau_k}\, X_k^+ >v\, x \right] = P_1 + P_2\, . |
For the first term we find
\begin{array}{l} P_1 &\lesssim& \limsup \dfrac{{\bf P}\left[\sup\limits_{1 \leq n \leq m}\sum\limits_{k = 1}^n e^{-\delta \tau_k}\, X_k >(1-v)\, x\right]}{\sum\limits_{k = 1}^m{\bf P}\left[ e^{-\delta \tau_k}\, X_k >(1-v)\, x\right]} \, \dfrac{\sum\limits_{k = 1}^m {\bf P}\left[e^{-\delta \tau_k}\, X_k >(1-v)\, x\right]}{\sum\limits_{k = 1}^m {\bf P}\left[e^{-\delta \tau_k}\, X_k >x\right]} \\[2mm] &&\times\, \sum\limits_{k = 1}^m{\bf P}\left[ e^{-\delta \tau_k}\, X_k >x\right] \leq M^{F}(1-v)\, \sum\limits_{k = 1}^\infty {\bf P}\left[ e^{-\delta \tau_k}\, X_k >x\right]\, .\end{array} |
For the second term we can obtain
P_2 \lesssim C\, M^{F_1}(v)\, \sum\limits_{k = m+1}^\infty ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}])\, \sum\limits_{k = 1}^\infty {\bf P}\left[ e^{-\delta \tau_k}\, X_k >x\right] \, . |
Indeed, from the elementary inequality
\begin{array}{l} &&P_2 \leq \sum\limits_{k = m+1}^\infty{\bf P}\left[ e^{-\delta \tau_k}\, X_k^+ >v\, x \right] + {\bf P}\left[\sum\limits_{k = m+1}^\infty e^{-\delta \tau_k}\, X_k^+ \, {\bf 1}_{\{ e^{-\delta \tau_k}\, X_k^+ \leq v\, x \}}>v\, x \right]\\[2mm] &&\leq C_1\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) \\[2mm] &&+\dfrac 1{(v\, x)^{\alpha}}\left({\bf E}\left[\sum\limits_{k = m+1}^\infty e^{-\delta \tau_k}\, X_k^+ \, {\bf 1}_{\{ e^{-\delta \tau_k}\, X_k^+ \leq v\, x \}} \right]\right)^{\alpha}\\[2mm] &&\leq C_1\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) \\[2mm] &&+\dfrac 1{(v\, x)^{\alpha}}\sum\limits_{k = m+1}^\infty {\bf E}\left[\left(e^{-\delta \tau_k}\, X_k^+ \, {\bf 1}_{\{ e^{-\delta \tau_k}\, X_k^ \leq v\, x \}}\right)^{\alpha} \right]\\[2mm] &&\leq C_1\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) +C_2\, \sum\limits_{k = m+1}^\infty {\bf P}\left[ e^{-\delta \tau_k}\, X_k > v\, x \right]\\[2mm] &&\leq (C_1 + C_2)\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) \, . \end{array} |
Hence, using again the weak equivalence
P_2 \lesssim (C_1+C_2)\, M^{F}(v)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}])\, \sum\limits_{k = 1}^\infty {\bf P}\left[ e^{-\delta \tau_k}\, X_k >x\right]\, . |
After substitution we have
\begin{array}{l} &&\psi_{\delta}(x) \lesssim \left(M^{F}(1-v) + (C_1+C_2)\, M^{F}(v)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}])\right)\\[2mm] &&\times\, \sum\limits_{k = 1}^\infty{\bf P}\left[ e^{-\delta \tau_k}\, X_k >x\right] \, . \end{array} |
Now letting
Under the condition
\begin{array}{l} &&P_2 \leq C_1\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) \\[2mm] &&+\dfrac 1{(v\, x)^{\alpha}}\left({\bf E}\left[\sum\limits_{k = m+1}^\infty e^{-\delta \tau_k}\, X_k^+ \, {\bf 1}_{\{ e^{-\delta \tau_k}\, X_k^+ \leq v\, x \}} \right]\right)^{\alpha}\\[2mm] &&\leq C_1\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) \\[2mm] &&+\dfrac 1{(v\, x)^{\alpha}}\sum\limits_{k = m+1}^\infty {\bf E}\left[\left(e^{-\delta \tau_k}\, X_k^+ \, {\bf 1}_{\{ e^{-\delta \tau_k}\, X_k^ \leq v\, x \}}\right)^{\alpha} \right]\\[2mm] &&\leq C_1\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) +C_2\, \sum\limits_{k = m+1}^\infty {\bf P}\left[ e^{-\delta \tau_k}\, X_k > v\, x \right]\\[2mm] &&\leq (C_1 + C_2)\, \bar F(vx)\, \sum\limits_{k = m+1}^\infty\, ({\bf E}[e^{-\alpha_F\, \delta \tau_k}] \vee {\bf E}[e^{-\beta_F\, \delta \tau_k}]) \, . \end{array} |
Remark 3.4. We observe that given that
{\bf E}\left[e^{-\alpha\, \delta\, \tau_n} \right] = \left({\bf E}\left[e^{-\alpha\, \delta\, \theta_1} \right]\right)^n\, , \qquad {\bf E}\left[e^{-\beta\, \delta\, \tau_n} \right] = \left({\bf E}\left[e^{-\beta\, \delta\, \theta_1} \right]\right)^n\, , |
and taking into account that
Hence, using the property of class
Corollary 3.5. If the sequence
\sum\limits_{k = 1}^{N(\Lambda\, \theta_n)} e^{-\delta\, \sigma_k}\, Y_k^* \leq C\, , | (3.2) |
holds almost surely, and the positive random variables
\psi_{\delta}(x) \sim \sum\limits_{n = 1}^\infty {\bf P}[e^{-\delta \tau_n}\, Z_n >x]\, . | (3.3) |
Proof. Since we have the sequence
e^{-\delta\, \tau_n}\, X_n = e^{-\delta\, \tau_n}\, Z_n - \sum\limits_{k = 1}^{N(\Lambda\, \theta_n)} e^{-\delta\, \sigma_k}\, Y_k^*\, , |
Therefore, from the condition
Now from the double inequality
Now, we are ready to provide the final asymptotic expression for the ruin probability
Theorem 3.6. Let the random variables
\psi_{\delta}(x) \sim \dfrac {{\bf E}\left[ e^{-\alpha \delta \theta}\right]}{1- {\bf E}\left[ e^{-\alpha \delta \theta}\right]}\, {\bf E}\left[ \nu \left(A_{1, \, \theta} \right)\right] \, \overline{B}(x)\, . | (3.4) |
Proof. From the formulas (1.2) and (1.3) we conclude that
\begin{equation} \label{eq.4.13} x - \sum\limits_{n = 1}^\infty X_n\, e^{-\delta \, \tau_n} \leq \widetilde{U}_\delta(t) \leq x - \sum\limits_{n = 1}^\infty X_n\, e^{-\delta \, \tau_n}\, {\bf 1}_{\{\tau_n \leq t\}}\, , \end{equation} | (3.5) |
and further taking into account the regular variation of the distribution
\begin{equation} \label{eq.4.14} \psi_{\delta}(x) \leq P\left[ \sum\limits_{n = 1}^{\infty} X_n \, e^{-\delta \tau_{n} }> x\right] = \overline{F}(x) \, \sum\limits_{n = 1}^{\infty} {\bf E}\left[ X_n \, e^{-\alpha \delta \tau_{n} }\right] = \overline{F}(x)\, \dfrac{ {\bf E}\left[ e^{-\alpha \delta \theta}\right]}{1-{\bf E}\left[ e^{-\alpha \delta \theta}\right]}\, . \end{equation} | (3.6) |
Next, for the lower bound we find
\begin{eqnarray*} \psi_{\delta}(x) &\geq& P\left[\sup\limits_{t\geq 0} \sum\limits_{n = 1}^{\infty} X_n \, e^{-\delta \tau_{n} } \, {\bf 1}_{\{\tau_n \leq t\}} > x\right] = P\left[ \sum\limits_{n = 1}^{\infty} X_n \, e^{-\delta \tau_{n} } > x\right] \\[2mm] &\sim& \overline{F}(x)\, \sum\limits_{n = 1}^{\infty} {\bf E}\left[ e^{-\alpha \delta \tau_{n}}\right] = \overline{F}(x)\, \dfrac{ {\bf E}\left[ e^{-\alpha \delta \theta}\right]}{1-{\bf E}\left[ e^{-\alpha \delta \theta}\right]}\, . \end{eqnarray*} |
So with combination of the previous bounds we have
\begin{equation} \label{eq.4.15} \psi_{\delta}(x) \sim \overline{F}(x)\, \dfrac{ {\bf E}\left[ e^{-\alpha \delta \theta}\right]}{1-{\bf E}\left[ e^{-\alpha \delta \theta}\right]}\, . \end{equation} | (3.7) |
Finally after substitution from Lemma 3.2 we conclude the result.
Remark 3.7. For
I would like to thank Prof. S. Kou for the suggestion of this topic and to two anonymous referees for their several constructive comments.
The author declares no conflict of interest.
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