Impact of dependence modeling of non-life insurance risks on capital requirement: D-Vine Copula approach

Mejdoub, Hanène; Arab, Mounira Ben

doi:10.1016/j.ribaf.2017.07.152

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Bermúdez et al [22] using the Monte Carlo method to simulate the data of Spanish non-life insurance market from 2000 to 2009 found that the correlation assumption between different insurance business lines will have a significant impact on risk capital. Mejdoub and Ben Arab [23] used copula function modeling to estimate venture capital based on the data of an insurance company in Tunisia and verified its availability with the Monte Carlo method. Hanewald et al [24] based on the research of macroeconomic fluctuation and demography 2 Complexity developed a dynamic asset liability model to evaluate the impact of macroeconomic fluctuation on the solvency of life insurance companies.…”

Section: Solvency Estimatementioning

confidence: 99%

Solvency Evaluation Model of Insurance Company Based on Stochastic Differential Equation

Wang

Zhu

2021

Complexity

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Solvency assessment is the core content of insurance supervision. In this paper, from the perspective of capital flow, the insurance company’s capital flow is regarded as a dynamic system, the stochastic differential equations model is established to describe its flow characteristics, and the existence of positive equilibrium point of the system is proved, as well as the conditions of stability at equilibrium point, that is, the requirements of the insurance company’s solvency. Furthermore, by using the numerical simulation method, we get the strategy of insurance companies to deal with the solvency shortage when facing the change of external environment, and the strategy of insurance company to deal with solvency shortage is obtained.

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Section: Solvency Estimatementioning

confidence: 99%

Solvency Evaluation Model of Insurance Company Based on Stochastic Differential Equation

Wang

Zhu

2021

Complexity

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“…In recent decades, plenty of risk analysis methods have appeared in the construction industry. Risk matrix (RM) (Mahamid, 2011), Monte Carlo simulation (MCS) (Mejdoub and Ben Arab, 2018), fault-tree analysis (FTA) and FMEA (Liu et al , 2015; Panchal et al , 2018) have all been used to identify and decrease risk events in the construction industry. An RM uses a qualitative analysis of components to rank risk events, classifying quality risks into a limited number of categories (Ahmadi et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects

Guo

2019

IJQRM

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Purpose The purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints. Design/methodology/approach This paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs. Findings The methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately. Research limitations/implications The research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management. Practical implications The model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives. Originality/value The authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.

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“…The problem is correspondingly transformed into a series of bivariate copula parameter estimation problems (one or two parameters need to be optimized each time). A d -dimensional D-vine is represented by ( d – 1) trees as follows; The tree i contains ( d – 1 + i ) nodes and ( d – i ) edges, and two nodes are connected by an edge; Each edge corresponds to a bivariate conditional or unconditional copula density; The edges of the tree i become nodes of the next tree i + 1; Complete decomposition is defined by d ( d – 1)/2 edges and marginal densities of each variable. …”

Section: Preliminariesmentioning

confidence: 99%

Soft-Sensor Model for Chemical Processes Based on D-Vine Copula with Rolling Pin Transformation

Cai

Zhou

Zhang

et al. 2019

Ind. Eng. Chem. Res.

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Soft-sensing methods have been widely used in recent years to predict key variables that are difficult to measure or involve costly and time-consuming in chemical processes. Owing to the increasing complexity of industrial processes, industrial data often exhibit strong nonlinearities and self-correlation, and the data distribution fails to satisfy the Gaussian assumption. To address these problems, a vine copula-based soft-sensor model combined with the rolling pin method is proposed. This approach uses a D-vine copula to model a joint probability distribution of auxiliary variables and a key variable. In accordance with the joint distribution, the predicted value of the key variable can be determined by weighting the training samples. During modeling, the Bayesian information criterion is adopted to select the best-fitted copula pairs. Given the nonmonotonicity between variables of actual industrial data, the rolling pin monotonic transformation is simultaneously introduced to improve the suitability of transformed data for vine copula modeling. The efficiency of the proposed method was verified using a numerical example and ethylene cracking.

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Impact of dependence modeling of non-life insurance risks on capital requirement: D-Vine Copula approach

Cited by 12 publications

References 26 publications

Solvency Evaluation Model of Insurance Company Based on Stochastic Differential Equation

Solvency Evaluation Model of Insurance Company Based on Stochastic Differential Equation

A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects

Soft-Sensor Model for Chemical Processes Based on D-Vine Copula with Rolling Pin Transformation

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