What drives information asymmetry? What’s the relationship between information asymmetry and competitiveness? Based on a large dataset of China’s automobile insurance market, we find solid evidence of the existence of asymmetric information in China’s automobile insurance market according...

The purpose of this paper is an analysis of the presence of self-selection mechanisms on the market that could bring the market closer to the separating equilibrium state, in line with the Rothschild-Stiglitz equilibrium model and its subsequent modifications. An example is the Polish market of...

Quantile regression provides a way to estimate a driver's risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender,...

This paper examines various explanations for the increase in the degree of regulation of the auto industry in the last ten years. Using cross section data for the State of California, the paper confirms earlier findings for the State of Massachusetts that the demand for auto insurance is highly...

Purpose - The insurance industry has not been able to effectively retain its customers and struggles to establish and maintain long-lasting relationships with them. The purpose of this paper is thus to identify the main factors that explain the cancellation of motor insurance policies by...

This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study the effects of combining different methods using five measures of prediction accuracy. The results show the...

This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a...

Implementation of the Affordable Care Act (ACA) has been linked to an increase in the population covered by health insurance through expansions of the Medicaid program and revisions to private health insurance markets. We add to the body of literature on unintended consequences of the ACA by...

This article introduces an estimation method for the conditional joint distribution of bivariate outcomes, based on the distribution regression approach and the factorization method. The proposed method can apply for discrete, continuous or mixed distribution outcomes. It is semiparametric in...

Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In...

The aim of this paper is to propose a multivariate INAR(1) model for addressing all the challenges in high-dimensional non-life claim count data sets that exhibit time and cross dependence and a zero-inflation attribute. In particular, the innovation terms are modelled using a multivariate...

The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor...

This study analyses the long- and short-term dynamics of the determinants of insurance penetration for the period 1999Q1 to 2019Q4 in 15 West African countries. The panel auto regressive distributed lag model was used on the quarterly data gathered. A cointegrating and short-run momentous...

The growing trend in the number and severity of auto insurance claims creates a needfor new methods to efficiently handle these claims. Machine learning (ML) is one of the methodsthat solves this problem. As car insurers aim to improve their customer service, these companieshave started adopting...

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model...

A comprehensive auto insurance policy usually provides the broadest protection for the most common events for which the policyholder would file a claim. On the other hand, some insurers offer extended third-party car insurance to adapt to the personal needs of every policyholder. The extra...

This paper analyzes the impact of rate regulation on the structure of insurance markets for private passenger automobile insurance. The paper argues that states' restrictions on automobile insurers' rates of return will distort the structure of the market by distorting insurers' entry and output...

We use a large data set of deductible choices in auto insurance contracts to estimate the distribution of risk preferences in our sample. To do so, we develop a structural econometric model, which accounts for adverse selection by allowing for unobserved heterogeneity in both risk (probability...

This paper investigates the incentive effects of automobile insurance, compulsory insurance laws, and no-fault liability laws on driver behavior and traffic fatalities. We analyze a panel of 50 U.S. states and the District of Columbia from 1970-1998, a period in which many states adopted...

Microeconomic theory predicts that under certain regularity conditions higher idiosyncratic risk increases the propensity to insure against independent marketable risks. We apply these predictions to the specific case of labor income risk and car insurance using data from the UK. The main...

The effects of asymmetric information are often difficult to detect empirically, such as in insurance settings. We show that allowing for reference-dependent preferences can assist with this empirical challenge. Using detailed auto insurance claims data, we show that policyholders exhibit...

A key argument for providing unauthorized immigrants with driver licenses is that such policies are expected to increase auto insurance uptake and lower premiums. The paper tests this expectation in the context of California's Assembly Bill (AB) 60, which gave about 2.6 million unauthorized...

This paper tests for regulation-induced adverse selection in the Massachusetts automobile insurance market during the regulated period 1990-2005. The paper demonstrates the application of the Poterba-Finkelstein (2014) unused-observables test for adverse selection in a regulated insurance market...

Bias resulting from model misspecification is a concern when predicting insurance claims. Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible distributions would...

This article discusses a decision both European Union Member States and states in the United States must make: whether to raise their compulsory automobile insurance minimum amounts. The authors review a case study from the United States, the Commonwealth of Pennsylvania, and conclude a proposed...

A fundamental idea in modern asset pricing is that risk aversion may be time varying and countercyclical (Campbell and Cochrane 1999). Existing evidence is scant and based mostly on experimental or survey data. We aim to test this key assumption using a large panel of real-world transaction...

This paper examines the current No-Claim Discount (NCD) system used in Ghana’s auto insurance market as inefficient and outmoded and, therefore, proposes an alternative optimal Bonus-Malus System (BMS) intended to meet the present market conditions and demand. It appears that the existing BMS...

Kfz-Versicherungen können durch aktives Schadenmanagement die Kosten senken sowie die Kundenzufriedenheit und Kundenbindung steigern. Dies gilt auch im Rahmen des betrieblichen Flottenmanagements, das in diesem Beitrag behandelt wird. Vor diesem Hintergrund ist es wichtig, die Kundenerwartungen...

In this paper, a flexible count regression model based on a bivariate compound Poisson distribution is introduced in order to distinguish between different types of claims according to the claim size. Furthermore, it allows us to analyse the factors that affect the number of claims above and...

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models....

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic...