Discrete-time Risk Model Research Articles

Asymptotics for ruin probabilities in a bidimensional discrete-time risk model with dependent and consistently varying tailed net losses

Consider an insurer who operates two lines of businesses and hence receives two types of insurance net losses. In the bidimensional discrete-time risk model with a constant interest rate, the net loss vectors from the two business lines form a sequence of independent and identically distributed real-valued random vectors. We propose a bidimensional extended regularly varying structure in terms of survival copulas to model the dependence between the two components of each net loss vector, which includes not only asymptotic independence but also asymptotic dependence. Under the framework of consistent variation, we obtain several (uniformly) asymptotic formulas for various ruin probabilities. In particular, the investigation of random-time ruin probabilities unifies and extends the results on both finite-time and infinite-time ruin probabilities. Some numerical studies are also performed to check the accuracy of our obtained asymptotic results.

Distribution of shifted discrete random walk generated by distinct random variables and applications in ruin theory

In this paper, the distribution function \[ \varphi (u)=\mathbb{P}\Bigg(\underset{n\geqslant 1}{\sup }{\sum \limits_{i=1}^{n}}({X_{i}}-\kappa )\lt u\Bigg),\] and the generating function of $\varphi (u+1)$ are set up. We assume that $u\in \mathbb{N}\cup \{0\}$, $\kappa \in \mathbb{N}$, the random walk $\{{\textstyle\sum _{i=1}^{n}}{X_{i}},\hspace{0.1667em}n\in \mathbb{N}\}$ involves $N\in \mathbb{N}$ periodically occurring distributions, and the integer-valued and nonnegative random variables ${X_{1}},{X_{2}},\dots $ are independent. This research generalizes two recent works where $\{\kappa =1,N\in \mathbb{N}\}$ and $\{\kappa \in \mathbb{N},N=1\}$ were considered respectively. The provided sequence of sums $\{{\textstyle\sum _{i=1}^{n}}({X_{i}}-\kappa ),\hspace{0.1667em}n\in \mathbb{N}\}$ generates the so-called multi-seasonal discrete-time risk model with arbitrary natural premium and its known distribution enables to compute the ultimate time ruin probability $1-\varphi (u)$ or survival probability $\varphi (u)$. The obtained theoretical statements are verified in several computational examples where the values of the survival probability $\varphi (u)$ and its generating function are provided when $\{\kappa =2,\hspace{0.1667em}N=2\}$, $\{\kappa =3,\hspace{0.1667em}N=2\}$, $\{\kappa =5,\hspace{0.1667em}N=10\}$ and ${X_{i}}$ adopts the Poisson and some other distributions. The conjecture on the nonsingularity of certain matrices is posed.

Ruin probability for renewal risk models with neutral net profit condition

In ruin theory, the net profit condition intuitively means that the sizes of the incurred random claims are on average less than the premiums gained between the successive interoccurrence times. The breach of the net profit condition causes guaranteed ruin in few but simple cases when both the claims’ interoccurrence time and random claims are degenerate. In this work, we give a simplified argumentation for the unavoidable ruin when the incurred claims are on average equal to the premiums gained between the successive interoccurrence times. We study the discrete-time risk model with N ∈ N periodically occurring independent distributions, the classical risk model, also known as the Cramér–Lundberg risk process, and the more general Sparre Andersen model.

Multiseasonal discrete-time risk model revisited

Multiseasonal discrete-time risk model revisited

Ruin Analysis on a New Risk Model with Stochastic Premiums and Dependence Based on Time Series for Count Random Variables

In this paper, we propose a new discrete-time risk model of an insurance portfolio with stochastic premiums, in which the temporal dependence among the premium numbers of consecutive periods is fitted by the first-order integer-valued autoregressive (INAR(1)) process and the temporal dependence among the claim numbers of consecutive periods is described by the integer-valued moving average (INMA(1)) process. To measure the risk of the model quantitatively, we study the explicit expression for a function whose solution is defined as the Lundberg adjustment coefficient and give the Lundberg approximation formula for the infinite-time ruin probability. In the case of heavy-tailed claim sizes, we establish the asymptotic formula for the finite-time ruin probability via the large deviations of the aggregate claims. Two numerical examples are provided in order to illustrate our theoretical findings.

On the Devylder–Goovaerts Conjecture in Ruin Theory

The Devylder–Goovaerts conjecture is probably the oldest conjecture in actuarial mathematics and has received a lot of attention in recent years. It claims that ruin with equalized claim amounts is always less likely than in the classical model. Investigating the validity of this conjecture is important both from a theoretical aspect and a practical point of view, as it suggests that one always underestimates the risk of insolvency by replacing claim amounts with the average claim amount a posteriori. We first state a simplified version of the conjecture in the discrete-time risk model when one equalizes aggregate claim amounts and prove that it holds. We then use properties of the Pareto distribution in risk theory and other ideas to target candidate counterexamples and provide several counterexamples to the original Devylder–Goovaerts conjecture.

Ruin Probability for Risk Model with Random Premiums

Based on Invariance Principle for Brownian Motion, we obtained a closed-form expression of the ruin probability for the Discrete-Time Risk Model with Random Premiums that was recently introduced by Korzeniowski [1]. We show that in this model, given two strategies that have the same probability of ultimate ruin, the strategy with larger initial capital and smaller loading factor is less risky than the strategy with smaller initial capital and larger loading factor in that it lowers the probability of ruin on the finite time horizon.

Discrete-time risk models with surplus-dependent premium corrections

This paper studies discrete-time risk models with insurance premiums adjusted according to claims experience. The premium correction mechanism follows the well-known principle in the non-life insurance industry, the so-called bonus-malus system. The bonus-malus framework that we study here extends the current literature by allowing the premium correction rules to vary according to the current surplus level of the insurance company. The main goal of this paper is to evaluate the risk of ruin for the insurer who implements the proposed bonus-malus system. Two premiums correction principles are examined: by aggregate claims or by claim frequency. Further, the Parisian type of ruin is also considered, where the premium adjustment rules are different in positive- and negative-surplus environment.

On $$\mathbf {2\times 2}$$ Determinants Originating from Survival Probabilities in Homogeneous Discrete Time Risk Model

We analyse \(2\times 2\) Hankel-like determinants \(D_n\) that arise in search of initial values for the ultimate time survival probability \(\varphi (u)={\mathbb {P}}\left( \cap _{n=1}^{\infty }\left\{ W(n)>0\right\} \right) \) in homogeneous discrete time risk model \(W(n)=u+\kappa n-\sum _{i=1}^{n}Z_i\), where \(Z_i\) are non-negative integer valued i.i.d. random claims, the initial surplus \(u\in {{\mathbb {N}}}_0\) and the income rate \(\kappa =2\). We prove the asymptotic version of a recent conjecture on the non–vanishing and monotonicity of \(D_n\) and derive explicit formulas for the initial values \(\varphi (0)\), \(\varphi (1)\) of a recurrence that yields survival probabilities. In cases when \(Z_i\) are Bernoulli or Geometrically distributed, the conjecture on \(D_n\) is shown to hold for all \(n\in \mathbb {N}_0\). Additionally, a generating function \(\Xi (s)\) for ultimate survival probabilities is derived.

Ruin probability for finite negative binomial mixture claims via recurrence sequences

A new procedure to find the ultimate ruin probability in a discrete-time risk model is presented for claims with a mixture of m negative binomial distributions. The method involves the theory of linear recurrence sequences. It requires to find the zeroes of an m degree polynomial and the solution of a system of m linear equations. Numerical results and plots are provided as examples.

Uniform asymptotics for ruin probabilities of multidimensional risk models with stochastic returns and regular variation claims

Consider a multidimensional discrete-time risk model in terms of some dependence structure. For the case of multivariate regularly varying claims, we study the asymptotic behavior of the ruin probabilities determined by some ruin sets. The estimates hold uniformly for all time horizons. Then we focus on a ruin set that allows partial capital transfers. As an application, the optimal allocation of the initial reserve is obtained. Some numerical simulations are presented to illustrate the results.

Second-Order Tail Behavior for Stochastic Discounted Value of Aggregate Net Losses in a Discrete-Time Risk Model

Second-Order Tail Behavior for Stochastic Discounted Value of Aggregate Net Losses in a Discrete-Time Risk Model

Bi-seasonal discrete time risk model with income rate two

This article proceeds calculation of ultimate time survival probability for bi-seasonal discrete time risk model when premium rate equals two. The same model with income rate equal to one was investigated in 2014 by Damarackas and Šiaulys. In general, discrete time and related risk models deal with possibility for a certain version of random walk to hit a certain threshold at least once in time. In this research, the mentioned threshold is the line and random walk consists from two interchangeably occurring independent but not necessarily identically distributed random variables. Most of proved theoretical statements are illustrated via numerical calculations. Also, there are raised a couple of conjectures that a certain recurrent determinants are non-vanishing.

Discrete Time Risk Model Financed by Random Premiums

We propose a novel actuarial risk model which, unlike the classical Crámer-Lundberg model, incorporates a stream of random premiums that offset random claims. A key feature of the model is a discrete time accounting of premiums and claims flow, whereby lending itself to random walk type analysis. We derive various estimates of ruin probability thereby providing an effective method of risk assessment over a future time horizon.

Note on the bi-risk discrete time risk model with income rate two

This article provides survival probability calculation formulas for bi-risk discrete time risk model with income rate two. More precisely, the possibility for the stochastic process $u+2t-{\textstyle\sum _{i=1}^{t}}{X_{i}}-{\textstyle\sum _{j=1}^{\lfloor t/2\rfloor }}{Y_{j}}$, $u\in \mathbb{N}\cup \{0\}$, to stay positive for all $t\in \{1,\hspace{0.1667em}2,\hspace{0.1667em}\dots ,\hspace{0.1667em}T\}$, when $T\in \mathbb{N}$ or $T\to \infty $, is considered, where the subtracted random part consists of the sum of random variables, which occur in time in the following order: ${X_{1}},\hspace{0.1667em}{X_{2}}+{Y_{1}},\hspace{0.1667em}{X_{3}},\hspace{0.1667em}{X_{4}}+{Y_{2}},\hspace{0.1667em}\dots $ Here ${X_{i}},\hspace{0.1667em}i\in \mathbb{N}$, and ${Y_{j}},\hspace{0.1667em}j\in \mathbb{N}$, are independent copies of two independent, but not necessarily identically distributed, nonnegative and integer-valued random variables X and Y. Following the known survival probability formulas of the similar bi-seasonal model with income rate two, $u+2t-{\textstyle\sum _{i=1}^{t}}{X_{i}}{\mathbb{1}_{\{i\hspace{2.5pt}\text{is odd}\}}}-{\textstyle\sum _{j=1}^{t}}{Y_{i}}{\mathbb{1}_{\{j\hspace{2.5pt}\text{is even}\}}}$, it is demonstrated how the bi-seasonal model is used to express survival probability calculation formulas in the bi-risk case. Several numerical examples are given where the derived theoretical statements are applied.

Exact expression of ultimate time survival probability in homogeneous discrete-time risk model

<abstract><p>In this work, we set up the generating function of the ultimate time survival probability $ \varphi(u+1) $, where</p> <p><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \varphi(u) = \mathbb{P}\left(\sup\limits_{n\geqslant 1}\sum\limits_{i = 1}^{n}\left(X_i- \kappa\right)<u\right), $\end{document} </tex-math></disp-formula></p> <p>$ u\in\mathbb{N}_0, \, \kappa\in\mathbb{N} $ and the random walk $ \left\{\sum_{i = 1}^{n}X_i, \, n\in\mathbb{N}\right\} $ consists of independent and identically distributed random variables $ X_i $, which are non-negative and integer-valued. We also give expressions of $ \varphi(u) $ via the roots of certain polynomials. The probability $ \varphi(u) $ means that the stochastic process</p> <p><disp-formula> <label/> <tex-math id="FE2"> \begin{document}$ u+ \kappa n-\sum\limits_{i = 1}^{n}X_i $\end{document} </tex-math></disp-formula></p> <p>is positive for all $ n\in\mathbb{N} $, where a certain growth is illustrated by the deterministic part $ u+ \kappa n $ and decrease is given by the subtracted random part $ \sum_{i = 1}^{n}X_i $. Based on the proven theoretical statements, we give several examples of $ \varphi(u) $ and its generating function expressions, when random variables $ X_i $ admit Bernoulli, geometric and some other distributions.</p></abstract>

Gerber-Shiu Function in a Discrete-time Risk Model with Dividend Strategy

In this paper, a discrete-time risk model with dividend strategy and a general premium rate is considered. Under such a strategy, once the insurer’s surplus hits a constant dividend barrier , dividends are paid off to shareholders at instantly. Using the roots of a generalization of Lundberg’s fundamental equation and the general theory on difference equations, two difference equations for the Gerber-Shiu discounted penalty function are derived and solved. The analytic results obtained are utilized to derive the probability of ultimate ruin when the claim sizes is a mixture of two geometric distributions. Numerical examples are also given to illustrate the applicability of the results obtained.

On the analysis of a discrete-time risk model with INAR(1) processes

This paper considers an extension of the classical discrete-time risk model for which an INAR(1) process is utilized to model a temporal dependence between the number of claims. We apply a recursive method for deriving the Laplace transform of the aggregate claims with or without discounting in this framework. This methodology is implemented for the class of INAR(1) processes with an arbitrary innovations' distribution. Three risk models via specific INAR(1) processes are studied when the distribution of the individual claim sizes belongs to the class of mixed Erlang distributions. These different models allow us to discuss the frequent manifestations of equidispersion, overdispersion and zero inflation, and to evaluate the distribution of the (discounted) aggregate claims. Numerical examples are performed in order to illustrate the results obtained in this paper.

On a Discrete-Time Risk Model with Random Income and a Constant Dividend Barrier

In this paper, a discrete-time risk model with random income and a constant dividend barrier is considered. Under such a dividend policy, once the insurer’s reserve hits the level b b > 0 , the excess of the reserve over b is paid off as dividends. We derive a homogeneous difference equation for the expected present value of dividend payments. Corresponding solution procedures for the difference equation are invested. Finally, we give a numerical example to illustrate the applicability of the results obtained.

Discrete-Time Risk Models with Claim Correlated Premiums in a Markovian Environment

In this paper we consider a discrete-time risk model, which allows the premium to be adjusted according to claims experience. This model is inspired by the well-known bonus-malus system in the non-life insurance industry. Two strategies of adjusting periodic premiums are considered: aggregate claims or claim frequency. Recursive formulae are derived to compute the finite-time ruin probabilities, and Lundberg-type upper bounds are also derived to evaluate the ultimate-time ruin probabilities. In addition, we extend the risk model by considering an external Markovian environment in which the claims distributions are governed by an external Markov process so that the periodic premium adjustments vary when the external environment state changes. We then study the joint distribution of premium level and environment state at ruin given ruin occurs. Two numerical examples are provided at the end of this paper to illustrate the impact of the initial external environment state, the initial premium level and the initial surplus on the ruin probability.