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Weather index insurance has become one of the important methods for agricultural weather risk transfer due to its transparent information and convenient claims process. In this study, Chenxi county in Hunan wasselectedas an example, based on the daily minimum temperature data and Camellia oleifera(C.oleifera) production data from 2009 to 2020, the extreme minimum temperature (Tc) and the negative effective accumulated temperature below 0℃(TT) were selected during the flowering and young fruit formation period of C.oleifera (from November to April of the next year)to determine the freezing injury index of C.oleifera and construct the index-disaster model. The Anderson-Darling test was used to obtain the optimal probability distribution model for the risk of C.oleifera freezing injury, and to determine the pure premium rate and compensation standard corresponding to different yield reduction rates. The actual compensation data from China Pacific Property Insurance Co., Ltd. Hunan Branch (CPIC) in February 2022 was used for validation. The results showed that during the flowering and young fruit formation period of C. oleifera, TT had a higher correlation with the yield reduction rate than Tc, and was used to characterize the meteorological index for freezing injury to C.oleifera in Chenxi county. The risk distribution of C.oleifera freezing injury conformed to the 3-parameter logarithmic logistic distribution, with the position parameter of 2.537, scaleparameter of 0.323, and threshold parameter of -1.805. When the compensation trigger condition TT was 2.1-20.8℃·d, the net premium rate was determined to be 0.85%-7.85%, and the compensation standard corresponding to the production reduction rate at all levels was 3750-48750yuan·ha-1. After calculation, the average compensation amount for the major C.oleifera producing towns in Chenxi county in February 2022 (5000yuan·ha-1) was basically consistent with the actual compensation amount of CPIC (4954yuan·ha-1). Based on the influence of freezing injury during the flowering and young fruit formation periodon the yield of C.oleifera in Hunan, a weather index insurance scheme for C.oleifera freezing injuries has been designed, which can serve as a reference for Hunan to establish and improve its insurance system for C.oleifera. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of property insurance on corporate innovation. Using data from Chinese listed firms between 2010 and 2021, we find that property insurance can reduce the uncertainty arising from negative cash flows and then promote corporate innovation. This finding holds true when controlling endogeneity and performing robustness checks. The mechanism tests suggest that the positive impact of property insurance use on corporate innovation is primarily attributed to the enhancement of companies' risk-taking and the alleviation of financing constraints. Additionally, the effects of property insurance use are stronger for non-state-owned companies, high- and new-technology enterprises, and firms led by more innovative managers. Overall, this study highlights the role of property insurance in enhancing corporate incentives for innovation. [ABSTRACT FROM AUTHOR]

The article discusses the importance of cyber insurance for water utilities as part of a robust cybersecurity strategy. It highlights the complexity of selecting the right insurance coverage and provides a framework for determining the events that should be covered. The text emphasizes the need for organizations to understand the potential impacts of cyberattacks and to work with insurance experts to secure adequate coverage. The article aims to guide water utilities in analyzing their insurance needs and discussing the results with insurance brokers to ensure comprehensive coverage. [Extracted from the article]

Climate change poses complex risks without precedent that challenge established planning and risk management tools, including property insurance. The nature and timing of transitions in markets and institutions in response to growing climate risks will shape prospects for future socioeconomic well-being. As property insurance markets in the United States face higher levels of turmoil, policymakers are weighing various interventions to stabilize not only insurance but also housing and mortgage markets. We identify policy lessons based on the investigation of three bounding scenarios designed to address the question of how near-term (1 to 3 y) policy and regulatory choices surrounding insurance markets in Florida could influence medium-term (3 to 15 y) risk levels, disaster recovery, housing markets, and local economic outcomes. Since our policy choices today have a profound impact on future outcomes, careful consideration of the longer-term impacts of today’s policy choices is required to secure more effective and equitable adaptation decisions and pathways. [ABSTRACT FROM AUTHOR]

In a subgroup analysis for an actuarial problem, the goal is for the investigator to classify the policyholders into unique groups, where the claims experience within each group are made as homogenous as possible. In this paper, we illustrate how the alternating direction method of multipliers (ADMM) approach for subgroup analysis can be modified so that it can be more easily incorporated into an insurance claims analysis. We present an approach to penalize adjacent coefficients only and show how the algorithm can be implemented for fast estimation of the parameters. We present three different cases of the model, depending on the level of dependence among the different coverage groups within the data. In addition, we provide an interpretation of the credibility problem using both random effects and fixed effects, where the fixed effects approach corresponds to the ADMM approach to subgroup analysis, while the random effects approach represents the classic Bayesian approach. In an empirical study, we demonstrate how these approaches can be applied to real data using the Wisconsin Local Government Property Insurance Fund data. Our results show that the presented approach to subgroup analysis could provide a classification of the policyholders that improves the prediction accuracy of the claim frequencies in case other classifying variables are unavailable in the data. [ABSTRACT FROM AUTHOR]

Frequent extreme weather events cause significant loss of life and property, highlighting the link between "extreme weather risk -- catastrophic loss -- high claim ratio -- insurer solvency." However, little research explores the mechanisms among these factors. This paper analyses the relationship between extreme weather and property insurance claim ratios, using data from 30 Chinese provinces (2012-2021). It constructs indices for high temperature, low temperature, and heavy precipitation, showing a strong correlation between extreme weather and claim ratios. Over 3- and 5-year periods, these indices significantly increase claim ratios, leading to complete or partial mediation effects on the solvency of property insurance companies. [ABSTRACT FROM AUTHOR]

India's insurance market is predominantly focused on motor and health insurance, accounting for over 70% of the total market. While these sectors remain vital, this overreliance creates vulnerabilities and limits market diversification. To ensure sustainable growth and financial resilience, insurers must explore underdeveloped segments like property, agricultural, catastrophe, and cyber insurance. Rapid urbanization, technological advancements, and emerging risks from climate change further underscore the need to broaden the insurance offerings. Drawing on global examples from markets like Japan, South Korea, and Europe, this essay advocates for strategic diversification, leveraging advanced technologies and public-private partnerships to unlock the vast untapped potential within India's insurance sector. Ultimately, by fostering a more inclusive and diversified insurance landscape, India can increase insurance penetration, enhance customer experience, and secure long-term stability in the industry. [ABSTRACT FROM AUTHOR]

Motivation: The need to implement the process of economic transformation in the recent past is what significantly differentiates the countries of Central and Eastern Europe from other members of the European Union. Integration into the financial system of the European Union has been an important factor influencing the process of transformation of the life insurance sector and the establishment of the level of competition, as well as the quality and scope of insurance services offered. In addition, the improvement in the macroeconomic situation and the increase in household income have had a significant impact on the growth in demand for insurance products from the life insurance segment. Despite positive changes, the insurance market of Central and Eastern European countries remains a relatively underdeveloped market, and its structure, unlike that of Western European countries, is dominated by property insurance. This points towards a need for further research into the determinants of life insurance market growth in this region of Europe. Aim: The aim of this paper is twofold. The first part presents an assessment of the level of development of the life insurance market in ten countries of Central and Eastern Europe over the period 2011-2021 using the method of multivariate comparative analysis in dynamic terms. The second part of the paper is devoted to an attempt to identify the determinants of the development of this market using econometric models for panel data (GMM estimator). Special attention is paid to the impact of economic growth on the life insurance market. Results: The life insurance market varies widely among the group of countries considered. The trends of change during the period under consideration were also different. The most severe downturn in the life insurance market was recorded in Poland and the Czech Republic. Significant development was observed in Lithuania and Latvia, countries with an underdeveloped market in the past. The econometric models used confirmed the impact of economic development on the life insurance market. [ABSTRACT FROM AUTHOR]

Private insurers are declining to issue or renew homeowner policies in California, Colorado, Florida, and Louisiana following massive payouts due to hurricane and wildfire damage in recent years. As climate change worsens, more private insurers will withdraw from property insurance markets. Governments, particularly at the state level, will likely expand their insurance programs to fill the gap. Just as the federal government now underwrites most flood insurance policies, public insurers will come to dominate the fire and wind insurance markets. Property insurance can generate price signals reflecting the risks of living in climate-vulnerable areas. However, public insurance programs often prioritize insurance availability and affordability while muting or eliminating accurate price signals. Because premiums collected by public insurance programs usually do not cover the payouts that follow a catastrophic event, expanding these programs could prove financially disastrous. Unfortunately, by offering underpriced coverage, such programs have encouraged development in climate-vulnerable areas, such as floodplains and the wildland-urban interface. Managed retreat policies aim to move people and communities out of climate-vulnerable areas. Typically involving public buyouts of private property, managed retreat can mitigate the damage associated with climate change, counter inefficient building practices, and facilitate disaster recovery. Yet managed retreat is logistically and politically challenging, and buyout programs thus far have had limited impact. Linking public insurance with buyouts can promote access to insurance coverage, break the disaster-rebuild cycle, and jump-start the relocation of people and communities from climate-vulnerable areas. This Article proposes that public insurance coverage in climate-vulnerable areas be made contingent on insureds agreeing to buyouts if property damage exceeds a predetermined threshold amount. If a covered event causes property damage above the threshold, the government will choose between paying on the insurance policy or purchasing the property. The government's choice would depend on funding availability, the value of the property as open space, the presence of other buyout properties nearby, the history of repetitive loss, and other relevant factors. The proposed mechanism, which can be incorporated into federal or state-backed insurance programs, would make the most of disaster-caused damage by compelling retreat after such damage has occurred. It would also sidestep some of the difficulties that have hampered voluntary buyout programs. Part I of this Article discusses the growing reluctance of private insurers to offer policies in climate-vulnerable areas, the accompanying rise of state-backed insurance, and ongoing managed retreat efforts. Part II considers the objectives of public insurance programs. Part III shows how governments might try to advance these objectives as climate change worsens. In the context of a dramatically expanded government role as an insurer of last resort, the proposal can facilitate managed retreat without compromising these objectives. [ABSTRACT FROM AUTHOR]

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In property insurance claims triage, insurers often use static information to assess the severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of weather conditions throughout the course of a loss event is predictive of the insured losses, and hence appropriate use of weather dynamics improves the operation of insurers' claim management. To test this hypothesis, we propose a deep learning method to incorporate dynamic weather information in the predictive modeling of the insured losses for reported claims. The proposed method features a hierarchical network architecture to address the challenges in claims triage due to the nature of weather dynamics. In the empirical analysis, we examine a portfolio of hail damage property insurance claims obtained from a major U.S. insurance carrier. When supplemented by dynamic weather information, the deep learning method exhibits substantial improvement in the hold-out predictive performance. We further design a cost-conscious decision strategy for triaging claims using the probabilistic forecasts of the insurance claim amounts. We show that leveraging weather dynamics in claims triage leads to a reduction of up to 9% and 6% in operational costs compared to when the triaging decision is based on forecasts without any weather information and with only static weather information, respectively. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This paper examines the causal impact of natural disasters on property insurance take-up of firms. Using the data of industrial firms in China, we find that a one-standard-deviation increase in typhoon damage leads to a 2.6% increase in the purchase of property insurance the following year. This increase gradually declines and returns to the previous level three years later. Our results demonstrate that this impact is driven not by changes in risk preferences or supply-side variation but by the updated risk beliefs learned from the typhoon experience. Unlike household insurance decisions, the learning effect of firms demonstrates an indirect but positive effect of typhoons on the firm's insurance decisions from its related firms in the upstream or downstream sectors, but not from competitors. [ABSTRACT FROM AUTHOR]

The relevance of the study is conditioned by the role and place of profit tax in the regional budgets revenues. Unlike other taxes, the profit tax provides for many methods of tax optimization, the use of which will reduce the tax burden on a taxpayer and at the same time reduce regional budgets tax revenues. The state recognizes and accepts tax optimization if it is based on the current tax legislation provisions. Tax optimization means a taxpayer’s activity aimed at reducing tax payments and leading to an increase in the financial result, including the use of all tax benefits, tax exemptions, and other legal advantages provided by the current legislation. The application of some optimization methods depends on a taxpayer themselves, and some other ones depend on the regional authorities, for example, investment tax deduction. The purpose of the study is to analyze the practice of profit tax optimization methods application and their impact on budget revenues and organizations income. Common research methods have been applied such as analysis and synthesis, generalization, grouping, tabular method, etc. The article is based on the official data of the Federal State Statistics Service of the Russian Federation, the Ministry of Finance of the Russian Federation, and the Federal Tax Service of the Russian Federation, which ensures the reliability of the data used in the study.

Severe weather events have become more frequent and more extreme over the last two decades, and the level of weather-related destruction has increased exponentially. A variety of factors have converged to increase the cost of insurance premiums, the repair or replacement of damaged property, as well as the cost to mitigate future weather events. This panel examines what the future may hold in terms of weather catastrophes, the property and casualty insurance market, and its potential impact on municipal credit. [ABSTRACT FROM AUTHOR]

Predicting the property insurance claim has become very important due to the increasing demand for property and the fluctuating number of monthly claims. However, current prediction methods suffer from limited feature important information and imbalanced claim data. This research introduces a machine learning approach by considering demographic factors of insured objects and marketing strategies to predict claim trends. The study also tackles data imbalance using the SMOTE technique. Results reveal that random forest outperforms logistic regression, achieving a recall value of 96%. The research findings enable insurance companies to better analyze insured risk profiles and manage the number of claims. [ABSTRACT FROM AUTHOR]

This study extends current knowledge on competition and corporate defense by investigating how firms transfer risk in response to their firm-specific multimarket and structural competitive conditions. Drawing on the theory of multimarket competition, we propose an inverted U–shaped relationship between multimarket competition and risk transfer. We also propose that, for the sake of gaining a competitive edge, the extent to which a firm manages risk will tend to be opposite to that of its competitors. Last, we propose that egocentric industry concentration will moderate the relationship between multimarket competition and risk transfer besides its direct effect. Analysis of reinsurance usage in the US property and casualty insurance industry strongly supports our model and shows that firm-specific competitive conditions are salient to a firm's risk transfer level. [ABSTRACT FROM AUTHOR]

The insurers’ subrogation holds a significant place in insurance law. In the Croatian legal system, upon the payment of the insurance proceeds, the insurer ex lege steps into the legal position of the insured in relation to the tortfeasor. This is a particularly important concept of property insurance, based on which the insurer, who has paid the indemnity, can seek compensation from the tortfeasor up to the amount of the indemnity paid. Procedural and substantive legal challenges arising from this mechanism are often the source of complex legal considerations, especially in terms of the commencement and duration of the limitation period for subrogated claims. The application of the insurer’s subrogation concept in practice raises legal questions, particularly when a dispute over the insured’s claim for insurance indemnity is being argued by the insurer, and a final court decision in such a dispute can only be expected years after the insured’s submission of the insurance indemnity claim. On the other hand, the law prescribes that the limitation period of the insurer’s subrogated claim against the tortfeasor begins at the same time as the limitation period of the insured’s claim against the tortfeasor, and those two limitation periods expire simultaneously. As a result, there is great likelihood that the claim against the tortfeasor as the liable party will become statute-barred before the insurer has even been subrogated into that claim. This article analyses the concept of the insurer’s subrogation, particularly from the perspective of maritime insurance, with special reference to the issue of the limitation period of the insurer’s subrogated claim. The authors point out the inconsistency of court decisions and raise arguments for and against maintaining the existing statute of limitations relating to the subrogated insurers’ claims. The discussion includes the relevant case law, a critical analysis of legislative provisions, as well as comparative legal solutions. The authors advocate for the reform of relevant legislative provisions, emphasising the need for new legal mechanisms within the existing legislative framework. They propose specific amendments de lege ferenda aimed at establishing greater legal certainty and better protection of the insurer’s right to subrogation while preserving the balance of legal interests of the involved parties. [ABSTRACT FROM AUTHOR]

This paper demonstrates workflows to incorporate text data into actuarial classification and regression tasks. The main focus is on methods employing transformer-based models. A dataset of car accident descriptions with an average length of 400 words, available in English and German, and a dataset with short property insurance claims descriptions, are used to demonstrate these techniques. The case studies tackle challenges related to a multilingual setting and long input sequences. They also show ways to interpret model output and to assess and improve model performance, by fine-tuning the models to the domain of application or to a specific prediction task. Finally, the paper provides practical approaches to handle classification tasks in situations with no or only few labelled data. The results achieved by using the language-understanding skills of off-the-shelf natural language processing (NLP) models with only minimal pre-processing and fine-tuning clearly demonstrate the power of transfer learning for practical applications. [ABSTRACT FROM AUTHOR]

Using a panel dataset of commercial fisheries in the Gulf of Mexico, we attempt to separately identify the moral hazard and self‐selection effects of property insurance coverage among commercial fishers. We use captains' propensity to take fishing trips under adverse weather conditions as a proxy for their private information; these data are available to us, but not to insurers. We find that vessels with higher long‐term exposure to risk are significantly less likely to be insured, suggesting potential advantageous selection. However, this relationship dissipates once we control for information likely known to the insurer. Finally, using a Heckit estimator, we find evidence of moral hazard: insured captains take more risks at sea. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new credibility model based on heterogeneous integer-valued self-exciting threshold autoregressive time series, in which the SETINAR(2,1) process is used to fit the claim numbers of policyholders for consecutive periods, and the unobservable heterogeneity is assumed to follow Gamma distribution. We obtain the Bayesian pricing formula for the proposed model and present some numerical examples to illustrate how the claim history affects the future premiums. We also apply the proposed model to a real panel dataset from the Wisconsin Local Government Property Insurance Fund. By comparing with some existing models, we find that our model can exploit the past information more efficiently and has better predictive performance. [ABSTRACT FROM AUTHOR]

In conditions of high competition and variability of economic conditions, timely insurance of business risks can help the enterprise to preserve its financial resources and ensure stability and security in the market. The article aims to systematize business risks by groups of losses and generalize and characterize the types of insurance policies that can be useful to the enterprise due to their manifestations. It was found that entrepreneurial risk in economic activity is a generalizing term for a group of risks that arise at various stages of the capital cycle as a result of the actions of the enterprise itself, competitors, counterparties, changes in the market situation, technological failures and other internal and external factors. Types of business risks are systematized. Four main groups of enterprise losses due to business risks were identified, with the selection of types of insurance policies that can be useful for enterprises in case of corresponding losses. Different conditions for insurance against cyber threats and liability for environmental damage in Ukraine were disclosed under four main costs depending on the type of damage subject to compensation. The results of the study show that business risk is a complex category that covers various aspects of business activity, protection against which occurs mainly within the scope of property insurance and liability insurance, but at the same time can go beyond these fields of insurance, if it is determined by the needs of business activity Business risk includes financial risks, such as market instability, changes in the economic environment, exchange rate fluctuations, rising costs, insufficient financial resources. It also covers operational risks related to business management, including supply chain issues, staffing issues, technology failures, legal conflicts, and more.

Assignment of Benefits (“AOB”) abuse has proliferated throughout the Florida property insurance market, contributing to excessive litigation, insurance company insolvencies, and higher premiums for insureds. The issue became so severe that the Florida legislature recently prohibited the practice of assigning property insurance benefits. This problem has been exacerbated in Florida by environmental, legislative, and judicial conditions, but AOB abuse across the country is a growing concern that cannot be ignored. AOB litigation has already reached the Iowa Supreme Court, and more cases will likely follow in other Iowa courts. While AOB abuse has not reached the magnitude that it has in Florida, the Iowa legislature can proactively address the problem by reforming Iowa’s existing AOB statute to closely reflect recent statutory amendments the Florida legislature enacted to correct the Sunshine State’s troubled property insurance market. Iowa lawmakers should learn a lesson from Florida and make the necessary legislative changes before further difficulties with AOB abuse arise. Consumers will benefit from stable premiums and higher quality insurance products; insurers will benefit from predictability and market stability; and the many honest contractors in the field will be able to operate knowing they will receive fair and just compensation for their work. [ABSTRACT FROM AUTHOR]

While previous studies have discussed how much should be given by firms, less is known about how firms would spend these investments, such as strategically allocating these philanthropy activities across geographic markets. This study examines the impact of multimarket contact on corporate philanthropy in different geographic markets. Using Chinese property insurance firms from 2007 to 2015 as samples, the results show that firms are less likely to initiate philanthropy activities in geographic markets with high multimarket contact. We also found that the negative impact of multimarket competition on corporate philanthropy is stronger in highly concentrated markets. Moreover, this negative relationship is stronger when institutional distance at a firm level is low. Our study contributes to the literature on corporate philanthropy and multimarket contact. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Fire insurance is a type of insurance that provides financial protection against property damage caused by events such as fire, theft, and weather-related damage. The use of modern analytics methods in analyzing policies and predicting potential losses is of great interest to insurance companies and those involved in property insurance services. Claim prediction is a crucial measure for predicting future losses in insurance companies, particularly in property insurance, where it helps evaluate the Probable Maximum Loss (PML) based on risk theory. METHODS: This study focuses on developing claim prediction models for occurrence probability, severity, and time by creating a dataset, training various algorithms, and comparing their performance. Exploratory Data Analysis (EDA) is conducted to select relevant features, resulting in the selection of 44 fields from insurance policy and claim information. Recursive Field Elimination (RFE) is used to reduce the dataset dimensions, and informative fields are selected for each prediction model. The dataset consists of over 780,000 policy records and approximately 70,000 loss payments from the Iran Insurance Company's actual fire application database spanning 10 years (2011-2021). Linear regression, random forest regression (RFR), support vector regression (SVR), and deep neural network algorithms are implemented for each prediction model. The accuracy of the algorithms is evaluated using mean squared error (MSE) and mean absolute error (MAE) values. FINDINGS: The results of the prediction models show that the deep multi-layer perception (MLP) algorithm performs the best. After hyperparameter tuning and multiple runs, the final MSE values are determined as 0.117 for occurrence probability prediction, 0.042 for loss severity prediction, and 0.106 for claim time prediction. The comparison of the innovative model results with test data demonstrates that intelligent models provide more accurate predictions. Accurate prediction is highly valuable for insurance companies as it allows them to mitigate financial losses. The use of deep learning in loss time prediction, in addition to severity and probability prediction, is a significant innovation in this research. CONCLUSION: Machine learning techniques, such as deep learning, can help insurance companies optimize their services with higher accuracy, strengthen risk management practices, and provide tools for better decision-making. The application of deep learning in loss prediction can replace complex and inaccurate manual processes in insurance, leading to advancements in risk management, reinsurance management, and improvement of fire insurance pricing. [ABSTRACT FROM AUTHOR]

We provide sufficient conditions for semi-nonparametric point identification of a mixture model of decision making under risk, when agents make choices in multiple lines of insurance coverage (contexts) by purchasing a bundle. As a first departure from the related literature, the model allows for two preference types. In the first one, agents behave according to standard expected utility theory with CARA Bernoulli utility function, with an agent-specific coefficient of absolute risk aversion whose distribution is left completely unspecified. In the other, agents behave according to the dual theory of choice under risk combined with a one-parameter family distortion function, where the parameter is agent-specific and is drawn from a distribution that is left completely unspecified. Within each preference type, the model allows for unobserved heterogeneity in consideration sets, where the latter form at the bundle level—a second departure from the related literature. Our point identification result rests on observing sufficient variation in covariates across contexts, without requiring any independent variation across alternatives within a single context. We estimate the model on data on households' deductible choices in two lines of property insurance, and use the results to assess the welfare implications of a hypothetical market intervention where the two lines of insurance are combined into a single one. We study the role of limited consideration in mediating the welfare effects of such intervention. [ABSTRACT FROM AUTHOR]

Fire insurance is a crucial component of property insurance, and its rating depends on the forecast of insurance loss claim data. Fire insurance loss claim data have complicated characteristics such as skewness and heavy tail. The traditional linear mixed model is commonly difficult to accurately describe the distribution of loss. Therefore, it is crucial to establish a scientific and reasonable distribution model of fire insurance loss claim data. In this study, the random effects and random errors in the linear mixed model are firstly assumed to obey the skew-normal distribution. Then, a skew-normal linear mixed model is established using the Bayesian MCMC method based on a set of U.S. property insurance loss claims data. Comparative analysis is conducted with the linear mixed model of logarithmic transformation. Afterward, a Bayesian skew-normal linear mixed model for Chinese fire insurance loss claims data is designed. The posterior distribution of claim data parameters and related parameter estimation are employed with the R language JAGS package to obtain the predicted and simulated loss claim values. Finally, the optimization model in this study is used to determine the insurance rate. The results demonstrate that the model established by the Bayesian MCMC method can overcome data skewness, and the fitting and correlation with the sample data are better than the log-normal linear mixed model. Hence, it can be concluded that the distribution model proposed in this paper is reasonable for describing insurance claims. This study innovates a new approach for calculating the insurance premium rate and expands the application of the Bayesian method in the fire insurance field. [ABSTRACT FROM AUTHOR]

The article focuses on the changes to the long-standing attorney fee and bad faith statutes in the context of property insurance policies. Topics discussed include the history, intent, and application of such laws by courts throughout the state of Florida, legislation altering these long-standing laws, and the potential retroactive application of the enacted laws to the existing contracts and claims.

This paper investigates the impact of an earthquake on households' perception of the seismic risks associated with residential locations—and, consequently, the impact of this change in perceptions on real estate prices—by performing revealed preference analysis on a unique data set of house prices and damage claims after the 2010/2011 Canterbury earthquake in New Zealand. We show that both informational and heuristic obstacles could have caused households to underestimate location earthquake risk before the quake and overreact to it after the quake. Our findings highlight the importance of quake-related information for seismic risk management and are robust to households' risk preferences in neighborhood propensity score matching. [ABSTRACT FROM AUTHOR]

There is growing evidence that physical climate hazards—such as floods and wildfires—affect property prices. Climate change scenarios suggest more frequent and severe physical climate hazards in the future, coinciding with greater exposure of populations to such threats. This raises concern because changes in property prices pose risks to homeowners' financial status, as well as to the insurance and mortgage industries, bank portfolios, and thereby financial systems. We begin with a new definition of climate gentrification (CG) that captures links between physical climate hazards, perceptions of risk and resilience, and capital flows in property markets. This is followed by a structured assessment of the key drivers of CG, and an empirical case study of property data for a flood-prone UK city to demonstrate how CG depressed house price growth over the period from 2005 to 2018 by up to 50 percent in flood-exposed (relative to unexposed) locations. We then provide a discussion of ethical concerns around CG research, with suggested ways forward. Such price signals have potential ramifications for the long-term stability of real estate markets and raise policy implications for private and public sectors. We conclude with some priorities for further research into CG, recognizing key information and data gaps, and noting how existing knowledge and tools could contribute toward improved resilience to climate change. Key Words: climate gentrification, climate hazards, flood, hedonic model, property prices. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel family of multivariate mixed Poisson-Generalized Inverse Gaussian INAR(1), MMPGIG-INAR(1), regression models for modelling time series of overdispersed count response variables in a versatile manner. The statistical properties associated with the proposed family of models are discussed and we derive the joint distribution of innovations across all the sequences. Finally, for illustrative purposes different members of the MMPGIG-INAR(1) class are fitted to Local Government Property Insurance Fund data from the state of Wisconsin via maximum likelihood estimation. [ABSTRACT FROM AUTHOR]

In conditions of high competition and variability of economic conditions, timely insurance of business risks can help the enterprise to preserve its financial resources and ensure stability and security in the market. The article aims to systematize business risks by groups of losses and generalize and characterize the types of insurance policies that can be useful to the enterprise due to their manifestations. It was found that entrepreneurial risk in economic activity is a generalizing term for a group of risks that arise at various stages of the capital cycle as a result of the actions of the enterprise itself, competitors, counterparties, changes in the market situation, technological failures and other internal and external factors. Types of business risks are systematized. Four main groups of enterprise losses due to business risks were identified, with the selection of types of insurance policies that can be useful for enterprises in case of corresponding losses. Different conditions for insurance against cyber threats and liability for environmental damage in Ukraine were disclosed under four main costs depending on the type of damage subject to compensation. The results of the study show that business risk is a complex category that covers various aspects of business activity, protection against which occurs mainly within the scope of property insurance and liability insurance, but at the same time can go beyond these fields of insurance, if it is determined by the needs of business activity Business risk includes financial risks, such as market instability, changes in the economic environment, exchange rate fluctuations, rising costs, insufficient financial resources. It also covers operational risks related to business management, including supply chain issues, staffing issues, technology failures, legal conflicts, and more. [ABSTRACT FROM AUTHOR]