STOCHASTIC APPROACH TO DIVIDEND EQUALIZATION FUND MODELLING AND SOLVENCY

2005 ◽  
Vol 15 (12) ◽  
pp. 1795-1810 ◽  
Author(s):  
M. A. PETERSEN ◽  
H. RAUBENHEIMER ◽  
M. VAN DER WALT
Keyword(s):  
Stochastic Models ◽  
Continuous Time ◽  
Stochastic Approach ◽  
Insurance Companies ◽  
Dividend Payment ◽  
Related Notion ◽  
The World ◽  
Net Assets ◽  
Equalization Fund ◽  
Solvency Ratio

In this contribution, the nonlinear dynamics of the surplus (net assets or reserve) process for a dividend-distributing company is studied in conjunction with the dynamics of its dividend equalization fund. The latter type of fund is maintained by leading insurance companies throughout the world and pays a special dividend for income that the investors lost because the dividend payment process was adversely affected for some or other reason. In our paper, a stochastic model for the related notion of a dividend equalization solvency ratio is derived. The ambient value of this ratio is an indication of the capacity of the insurer to pay dividends to shareholders especially when profit is low. The aforementioned analysis is, in turn, based on the construction of continuous time stochastic models for the dynamics of the surplus and total liabilities processes of an insurer. The discussions are reliant on principles arising within the asset-liability modelling paradigm.

scholarly journals THE INFLUENCE OF THE CROWN CRISIS ON THE DEVELOPMENT OF INSURANCE BUSINESS IN THE WORLD

2021 ◽  
Author(s):  
Hanna Mamonova
Keyword(s):  
Negative Impact ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Demand For Insurance ◽  
Online Sales ◽  
The World ◽  
Future Challenges ◽  
Declining Demand ◽  
Insurance Business ◽  
The Impact

The article analyzes the impact of the COVID-19 pandemic on the world insurance market and some European countries. Separated economic indicators of the impact of the COVID-19 pandemic on the insurance business of the world are singled out. It was determined that the impact of the COVID-19 pandemic inspired declining incomes of insurers and households, rising unemployment, declining demand for insurance services, a significant decline in productivity of insurance companies, uncertainty about the future development of the insurance industry and the effects of the pandemic. The experience of the world insurers' struggle against the consequences of the COVID-19 pandemic has been studied and generalized. The latest tools that have allowed insurers around the world to mitigate or mitigate the negative impact of the crown crisis, in particular, are: the development of new insurance products; increasing the level of requirements for insurance services in terms of its relevance, price flexibility, mobility and transparency; transition of insurers to online sales of insurance services and online payments for insurance cases; direct funding of specific means of combating COVID-19; use of the latest technologies and innovative methods in the insurance business; introduction of a new mode of staff work in the activities of insurance companies. The transition of insurers to online sales of insurance services and online payments has revealed many unresolved issues regarding the insurer's cybersecurity. Insurers are forced to improve existing technologies and methods of control, to intensify training and information activities. The Crown Crisis has significantly increased the importance of modern underwriting. Therefore, insurers around the world are using the capabilities of artificial intelligence, alternative data sources and better forecasting models. Greater understanding of pandemic processes, gaining experience is needed not only to accelerate the way out of the modern pandemic, but also to form a stable insurance system to the inevitable future challenges. The study of positive experience in the functioning and development of insurance markets around the world in crises and shocks is useful for application in national practice.

scholarly journals SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence

2020 ◽  
Author(s):  
Alberto Godio ◽  
Francesca Pace ◽  
Andrea Vergnano
Keyword(s):  
Particle Swarm Optimization ◽  
South Korea ◽  
Stochastic Approach ◽  
Medium Term ◽  
Swarm Optimization ◽  
Community Mobility ◽  
Italian Regions ◽  
The World ◽  
Model Equations ◽  
Official Data

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardia, Piemonte, and Veneto regions. We focus on the application of a stochastic approach in fitting the model numerous parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyze the official data and the predicted evolution of the epidemic in the Italian regions, and we compare the results with data and predictions of Spain and South Korea. We link the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discuss the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

STOCHASTIC MODELING OF PHYTOPLANKTON–ZOOPLANKTON INTERACTIONS WITH TOXIN PRODUCING PHYTOPLANKTON

2018 ◽  
Vol 26 (01) ◽  
pp. 87-106 ◽  
Author(s):  
T. MIHIRI M. DE SILVA ◽  
SOPHIA R.-J. JANG
Keyword(s):  
Markov Chain ◽  
Stochastic Modeling ◽  
Stochastic Models ◽  
Continuous Time ◽  
Toxin Production ◽  
Mutual Interference ◽  
Deterministic System ◽  
Continuous Time Markov Chain ◽  
Deterministic Systems ◽  
Population Interactions

We construct models of continuous-time Markov chain (CTMC) and Itô stochastic differential equations of population interactions based on a deterministic system of two phytoplankton and one zooplankton populations. The mechanisms of mutual interference among the predator zooplankton and the avoidance of toxin-producing phytoplankton (TPP) by zooplankton are incorporated. Sudden population extinctions occur in the stochastic models that cannot be captured in the deterministic systems. In addition, the effect of periodic toxin production by TPP is lessened when the birth and death of the populations are modeled randomly.

Exploring Insurance and Natural Disaster Tweets Using Text Analytics

2017 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Tylor Huizinga ◽  
Anteneh Ayanso ◽  
Miranda Smoor ◽  
Ted Wronski
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Association Rule ◽  
Association Rule Mining ◽  
Insurance Companies ◽  
Insurance Company ◽  
Text Analytics ◽  
Rule Mining ◽  
Twitter Data ◽  
The World

This study explores twitter data about insurance and natural disasters to gain business insights using text analytics. The program R was used to obtain tweets that included the word ‘insurance' in combination with other natural disaster words (e.g., snow, ice, flood, etc.). Tweets related to six top Canadian insurance companies as well as the top five insurance companies from the rest of the world, including the new entrant Google Insurance, was collected for this study. A total of 11,495 natural disaster tweets and 19,318 insurance company tweets were analyzed using association rule mining. The authors' analysis identified several strong rules that have implications for insurance products and services. These findings show the potential text mining applications offer for insurance companies in designing their products and services.

scholarly journals SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence

2020 ◽  
Vol 17 (10) ◽  
pp. 3535 ◽  
Author(s):  
Alberto Godio ◽  
Francesca Pace ◽  
Andrea Vergnano
Keyword(s):  
Particle Swarm Optimization ◽  
Stochastic Approach ◽  
Model Parameters ◽  
Medium Term ◽  
Swarm Optimization ◽  
Community Mobility ◽  
Italian Regions ◽  
The World ◽  
Model Equations ◽  
Official Data

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the application of a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyzed the official data and the predicted evolution of the epidemic in the Italian regions, and we compared the results with the data and predictions of Spain and South Korea. We linked the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discussed the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

Markov chains and generalized continued fractions

1992 ◽  
Vol 29 (04) ◽  
pp. 838-849 ◽  
Author(s):  
Thomas Hanschke
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Wide Class ◽  
Stochastic Models ◽  
Continuous Time ◽  
Continued Fractions ◽  
Invariant Measures ◽  
Continuous Time Markov Chains ◽  
Generalized Continued Fractions

This paper deals with a class of discrete-time Markov chains for which the invariant measures can be expressed in terms of generalized continued fractions. The representation covers a wide class of stochastic models and is well suited for numerical applications. The results obtained can easily be extended to continuous-time Markov chains.

Continuous-time stochastic models of a multigrade population

1978 ◽  
Vol 15 (1) ◽  
pp. 26-37 ◽  
Author(s):  
Sally I. McClean
Keyword(s):  
Markov Model ◽  
Exponential Distribution ◽  
Stochastic Models ◽  
Continuous Time ◽  
Time Dependent ◽  
Poisson Arrival ◽  
Mixed Exponential Distribution ◽  
Previous Assumption

The continuous-time Markov model of a multigrade organization is extended in several ways. Firstly the internal transitions and the leaving process are generalized to a semi-Markov formulation which allows for the inclusion of well-authenticated leaving distributions such as the mixed exponential distribution. The previous assumption of Poisson recruitment is then generalized to allow for a time-dependent Poisson arrival distribution in which the instantaneous probability of an arrival is a mixture of exponential terms. Finally we extend the capital-related manpower model to describe a multigrade organization.

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