On the (R,s, Q) inventory model when demand is modelled as a compound Bernoulli process

In this paper we present an approximation method to compute the reorder point s in an (R, s, Q) inventory model with a service level restriction. Demand is modelled as a compound Bernoulli process, i.e., with a fixed probability there is positive demand during a time unit; otherwise demand is zero. The demand size and the replenishment leadtime are random variables. It is shown that this kind of modelling is especially suitable for intermittent demand. In this paper we will adapt a method presented by Dunsmuir and Snyder such that the undershoot is not neglected. The reason for this is that for compound demand processes the undershoot has a considerable impact on the performance levels, especially when the probability that demand is zero during the leadtime is high, which is the case when demand is lumpy. Furthermore, the adapted method is used to derive an expression for the expected average physical stock. The quality of both the reorder point and the expected average physical stock, calculated with the method presented in this paper, rum out to be excellent, as has been verified by simulation.     

A stochastic system with MMPP input and an access function

In classical studies of loss systems with restricted availability, the utilization was suggested of a probabilistic loss function, defining the conditional probability of an incoming call being rejected, as a function of the number of occupations in the destination group of servers. This paper gives an exact analysis of stochastic processes of practical relevance, associated with a system with MMPP (Markov Modulated Poisson Process) input, finite queueing capacity and a general loss function, assuming exponential service times. In addition to the process defining the state of the system at any instant, the analysis of the overflow point process (associated with the rejected arriving customers), the accepted point process (associated with the accepted arriving customers), and of the departure process will be presented. Together with the exact analysis of this system, based on the matrix analytical methodology of Neuts, (1981), we will derive expressions for calculating some key-parameters of pertinent associated processes, which may also be used for their approximate modelling. Also, examples of applications and of blocking probability calculations in specific models of this class will be presented.     

Nonparametric Bayesian topic modelling with the hierarchical Pitman–Yor processes

The Dirichlet process and its extension, the Pitman–Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics. In particular, we propose a general framework for designing these Bayesian models, which are called topic models in the computer science community. We then propose a specific nonparametric Bayesian topic model for modelling text from social media. We focus on tweets (posts on Twitter) in this article due to their ease of access. We find that our nonparametric model performs better than existing parametric models in both goodness of fit and real world applications.     

Some Properties of CIR Processes

Abstract

This article derives some properties of variants of squared Bessel processes known as CIR processes in the finance literature. We derive the transition probability density function of a square-root process and compute the resolvent density of CIR processes. As a consequence, we derive the density of CIR processes sampled at an independent exponential time. Moreover, we derive explicit expressions of the Laplace transforms (LTs) of first hitting times by martingale methods.     

复合二项模型下盈余到达一给定水平的时间分析

利用鞅论的方法得到了复合二项模型中盈余过程首次和末次到达一给定水平的时间的分布特征,并导出了几个概率等式,另外,也讨论了其它一些相关量的概率特征.     

On the Esscher transforms and other equivalent martingale measures for Barndorff-Nielsen and Shephard stochastic volatility models with jumps

We compute and then discuss the Esscher martingale transform for exponential processes, the Esscher martingale transform for linear processes, the minimal martingale measure, the class of structure preserving martingale measures, and the minimum entropy martingale measure for stochastic volatility models of the Ornstein–Uhlenbeck type as introduced by Barndorff-Nielsen and Shephard. We show that in the model with leverage, with jumps both in the volatility and in the returns, all those measures are different, whereas in the model without leverage, with jumps in the volatility only and a continuous return process, several measures coincide, some simplifications can be made and the results are more explicit. We illustrate our results with parametric examples used in the literature.     

Bessel Bridges Decomposition with Varying Dimension: Applications to Finance

We consider a class of stochastic processes containing the classical and well-studied class of squared Bessel processes. Our model, however, allows the dimension to be a function of the time. We first give some classical results in a larger context where a time-varying drift term can be added. Then, in the non-drifted case, we extend many results already proven in the case of classical Bessel processes to our context. Our deepest result is a decomposition of the Bridge process associated with this generalized squared Bessel process, much similar to the much celebrated result of J. Pitman and M. Yor. From a more practical point of view, we give a methodology to compute the Laplace transform of additive functionals of our process and the associated bridge. In particular, this provides direct access to the joint distribution of the values at \(t\) of the process and its integral. We finally give some financial applications of our results.     

Analysis of risk models using a level crossing technique

This paper analyzes ruin-like risk models in Insurance, which are variants of the Cramer–Lundberg (C–L) model with a barrier or a threshold. We consider three model variants, which have different portfolio strategies when the risk reserve reaches the barrier or exceeds the threshold. In these models we construct a time-extended risk process defined on cycles of a specific renewal process. The time until ruin is equal to one cycle of the specific renewal process. We also consider a fourth model, which is a variant of a model proposed by Dickson and Waters (2004). The analysis of each model employs a level crossing method (LC) to derive the steady-state probability distribution of the time-extended risk process. From the derived distribution we compute the expected time until ruin, the probability distribution of the deficit at ruin, and related quantities of interest.     

The impact of data collection on fill rate performance in the (R,s, Q) inventory model

In this paper we consider the determination of the reorder point s in an (R, s, Q) inventory model subject to a fill rate service level constraint. We assume that the underlying demand process is a compound renewal process. We then derive an approximation method to compute the reorder level such that a target service level is achieved. Restrictions on the input parameters are given, within which this method is applicable. Moreover, we will investigate the effects on the fill rate performance in case the underlying demand process is indeed a compound renewal process, while the demand process is modelled as a discrete-time demand process. That is, the time axis is divided in time units (for example, days) and demands per time unit are independent and identically distributed random variables. It will be shown that smooth and erratic behaviour of the inter-arrival times have different impacts on the performance of the fill rate when demand is modelled as a discrete-time process and in case the underlying demand process is a compound renewal process.     

Buffer Overflow Probabilities for a Markov-Modulated Fluid Model

Markov modulated fluid models are widely used in modelling communications and computer systems. In the AMS (Annick, Mitra, Sohndi) model, heterogeneous, bursty sources modeled by multidimensional Markov processes are superimposed or multiplexed together to drive a fluid buffer. The performance of the system is measured by the steady state probability that the buffer exceeds a high level. The exact solution to this problem derived by AMS requires too much computation to be used on-line. Here we derive an upper bound for the above probability which is fast to compute and accurate enough for practical use.     

On multivariate modifications of Cramer–Lundberg risk model with constant intensities

ABSTRACT

The paper considers very general multivariate modifications of Cramer–Lundberg risk model. The claims can be of different types and can arrive in groups. The groups arrival processes have constant intensities. The counting groups processes are dependent multivariate compound Poisson processes of Type I. We allow empty groups and show that in that case we can find stochastically equivalent Cramer–Lundberg model with non-empty groups. The investigated model generalizes the risk model with common shocks, the Poisson risk process of order k, the Poisson negative binomial, the Polya-Aeppli, the Polya-Aeppli of order k among others. All of them with one or more types of policies. The numerical characteristics, Cramer–Lundberg approximations, and probabilities of ruin are derived. During the paper, we show that the theory of these risk models intrinsically relates to the special types of integro differential equations. The probability solutions to such differential equations provide new insights, typically overseen from the standard point of view.     

Analysis of risk models using a level crossing technique

This paper analyzes ruin-like risk models in Insurance, which are variants of the Cramer–Lundberg (C–L) model with a barrier or a threshold. We consider three model variants, which have different portfolio strategies when the risk reserve reaches the barrier or exceeds the threshold. In these models we construct a time-extended risk process defined on cycles of a specific renewal process. The time until ruin is equal to one cycle of the specific renewal process. We also consider a fourth model, which is a variant of a model proposed by Dickson and Waters (2004). The analysis of each model employs a level crossing method (LC) to derive the steady-state probability distribution of the time-extended risk process. From the derived distribution we compute the expected time until ruin, the probability distribution of the deficit at ruin, and related quantities of interest.     

On stochastic processes in a multilevel control bulk queueing system

This article analyzes some stochastic processes that arise in a bulk single server queue with continuously operating server, state dependent compound Poisson input flow and general state dependent service process. The authors treat the queueing process as a semi–regenerative process and obtain the invariant probability measure and the transient distribution for the embedded Markov chain. The stationary probability measure for the queueing process with continuous time parameter is found by using semi-regenerative techniques. The authors also study the input and output processes and establish ergodic theorems for some functionals of these processes. The results are obtained in terms of the invariant probability measure for the embedded process and the stationary measure for the queueing process with continuous time parameter     

Approximate Bayesian Computation and Model Assessment for Repulsive Spatial Point Processes

In many applications involving spatial point patterns, we find evidence of inhibition or repulsion. The most commonly used class of models for such settings are the Gibbs point processes. A recent alternative, at least to the statistical community, is the determinantal point process. Here, we examine model fitting and inference for both of these classes of processes in a Bayesian framework. While usual MCMC model fitting can be available, the algorithms are complex and are not always well behaved. We propose using approximate Bayesian computation (ABC) for such fitting. This approach becomes attractive because, though likelihoods are very challenging to work with for these processes, generation of realizations given parameter values is relatively straightforward. As a result, the ABC fitting approach is well-suited for these models. In addition, such simulation makes them well-suited for posterior predictive inference as well as for model assessment. We provide details for all of the above along with some simulation investigation and an illustrative analysis of a point pattern of tree data exhibiting repulsion. R code and datasets are included in the supplementary material.     

Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues

A univariate Hawkes process is a simple point process that is self-exciting and has a clustering effect. The intensity of this point process is given by the sum of a baseline intensity and another term that depends on the entire past history of the point process. Hawkes processes have wide applications in finance, neuroscience, social networks, criminology, seismology, and many other fields. In this paper, we prove a functional central limit theorem for stationary Hawkes processes in the asymptotic regime where the baseline intensity is large. The limit is a non-Markovian Gaussian process with dependent increments. We use the resulting approximation to study an infinite-server queue with high-volume Hawkes traffic. We show that the queue length process can be approximated by a Gaussian process, for which we compute explicitly the covariance function and the steady-state distribution. We also extend our results to multivariate stationary Hawkes processes and establish limit theorems for infinite-server queues with multivariate Hawkes traffic.     

On the first time of ruin in the bivariate compound Poisson model

This paper considers a bivariate compound Poisson model for a book of two dependent classes of insurance business. We focus on the ruin probability that at least one class of business will get ruined. As expected, general explicit expressions for this bivariate ruin probability is very difficult to obtain. In view of this, we introduce the so-called bivariate compound binomial model which can be used to approximate the finite-time survival probability of the assumed model. We then study some simple bounds for the infinite-time ruin probability via the association properties of the bivariate compound Poisson model. We also investigate the impact of dependence on the infinite-time ruin probability by means of multivariate stochastic orders.     

含投资因素的崩溃模型

本文讨论了已推广的保险公司的崩溃模型.本文得到了离散时间的崩溃模型复利情形下的崩溃概率公式,也得出了连续时间的崩溃模型崩溃概率的明确解和Vokterra积分方程.这些结果推广了经典崩溃模型中的相应结果.     

On compound Poisson processes arising in change-point type statistical models as limiting likelihood ratios

Different change-point type models encountered in statistical inference for stochastic processes give rise to different limiting likelihood ratio processes. In a previous paper of one of the authors it was established that one of these likelihood ratios, which is an exponential functional of a two-sided Poisson process driven by some parameter, can be approximated (for sufficiently small values of the parameter) by another one, which is an exponential functional of a two-sided Brownian motion. In this paper we consider yet another likelihood ratio, which is the exponent of a two-sided compound Poisson process driven by some parameter. We establish, that similarly to the Poisson type one, the compound Poisson type likelihood ratio can be approximated by the Brownian type one for sufficiently small values of the parameter. We equally discuss the asymptotics for large values of the parameter and illustrate the results by numerical simulations.     

基于跳扩散过程的Omega模型

文章通过在Omega模型中加入布朗运动扰动项,提出了一种跳扩散Omega破产模型.在索赔额为指数分布的情形下,给出了破产率函数是常数时的破产概率函数表达式.文章进一步研究了破产概率和盈余过程的“负占有时”之间的关系,并给出了破产概率函数的第二种推导过程.最后通过两个数值试验,将我们的模型与Albreeher和Lautscham (2013)的Omega模型的破产概率进行了比较分析.