Journal of Risk and Insurance最新文献

This paper introduces a two-pillar cyber risk management framework to address the pervasive challenges in managing cyber risk. The first pillar, cyber risk assessment, combines insurance frequency-severity models with cybersecurity cascade models to capture the unique nature of cyber risk. The second pillar, cyber capital management, facilitates informed allocation of capital for a balanced cyber risk management strategy, including cybersecurity investments, insurance coverage, and reserves. A case study, based on historical cyber incident data and realistic assumptions, demonstrates the necessity of comprehensive cost–benefit analysis for budget-constrained companies with competing objectives in cyber risk management. In addition, sensitivity analysis highlights the dependence of the optimal strategy on factors such as the price of cybersecurity controls and their effectiveness. The framework's implementation across a diverse range of companies yields general insights on cyber risk management.

This paper studies the impact of disaster experiences on communities' engagement in risk mitigation actions, focusing on flooding in the United States. We measure risk mitigation actions using communities' scores in the Community Rating System, an incentive program that scores flood preparedness and mitigation activities and rewards communities with flood insurance premium discounts. Leveraging a panel of communities from 1998 to 2019, we find a significant increase in risk mitigation activities following flood events, in both participation rates and intensity of actions. The effects continue to increase up to 10 years. Communities with greater capacity, particularly those in urban areas, exhibit a much stronger response. The findings highlight the adaptive capacity of communities but also raise several concerns regarding the inefficiency of disaster-driven responses and inequitable outcomes across communities.

This paper uses survey methods to assess the impact of access to housing wealth on long-term care (LTC) insurance demand. We compare two new mechanisms to finance LTC insurance with housing wealth: reverse mortgage loans and home reversion (partial sale and leaseback). Using data from an online survey of urban Chinese homeowners, we find that housing liquidity increases the stated demand for LTC insurance. On average, the survey participants spent 15.7% of their given total wealth to buy LTC insurance when they could use savings and a reverse mortgage, 12.8% when they could use savings and home reversion, and 5.2% when they could only use savings. Product demand was linked to economic factors, demographic variables, health, bequest motives, house price expectations, product understanding, and financial capability. Our results inform the design of new public or private sector programs that allow individuals to access their housing wealth while ‘aging in place’.

We develop a rich life-cycle model to assess the demand for life annuities, critical illness insurance, and long-term care (LTC) insurance by retirees in a portfolio-allocation setting. We calibrate our model to urban China, where retirees face limited public insurance and undeveloped private markets. We show that retirees with a low public pension should allocate at least 30% of their financial wealth at retirement to a life annuity. Those with an average pension should allocate at least 30% to critical illness insurance. The allocation to LTC insurance ranges from 5% to 33% across all economic profiles considered. Access to critical illness and LTC insurance does not necessarily increase annuity demand. However, access to annuities decreases LTC insurance demand. Our results suggest that countries with limited public insurance should first ensure the adequacy of retirement income and then focus on covering catastrophic medical expenses while providing basic LTC services for all.

This study proposes a comprehensive and general framework for examining discrepancies in textual content using large language models (LLMs), broadening application scenarios in the insurtech and risk management fields, and conducting empirical research based on actual needs and real-world data. Our framework integrates OpenAI's interface to embed texts and project them into external categories while utilizing distance metrics to evaluate discrepancies. To identify significant disparities, we design prompts to analyze three types of relationships: identical information, logical relationships and potential relationships. Our empirical analysis shows that 22.1% of samples exhibit substantial semantic discrepancies, and 38.1% of the samples with significant differences contain at least one of the identified relationships. The average processing time for each sample does not exceed 4 s, and all processes can be adjusted based on actual needs. Backtesting results and comparisons with traditional NLP methods further demonstrate that our proposed method is both effective and robust.

Two distinct strands of research focus on decentralized risk sharing plans. One strand centers on classic risk sharing and decentralized insurance, including peer-to-peer insurance, mutual aid, and DeFi insurance. The other explores decentralized annuities, such as tontine and group self-annuitization. Despite their disparate development paths, both involve decentralized risk sharing and transfer among participants. This paper aims to unify these domains by extending a theory of decentralized insurance to encompass multi-period decentralized insurances and decentralized annuities. The framework illuminates connections between spatial risk sharing and intertemporal risk transfer, and enables economic analysis of diverse scheme designs. The paper also provides a comprehensive review of various notions of temporal and spatial actuarial fairness, elucidating how different designs emerge from these distinct notions.

Flood insurance take-up remains low outside of the 100-year flood zone (SFHA), where purchasing insurance is entirely voluntary, despite the availability of affordable policies. Merging building footprints and inundation data for a large-scale flooding episode, we document substantial flood risk in the periphery of the SFHA and show that the storm led to large increases in take-up. But, while in the SFHA, the increase vanished after 3 years, it was highly persistent in the periphery. The extent of flooding and the type of policies purchased indicate that periphery residents who experienced flooding for the first time revised upwardly their beliefs about flood risk and began purchasing flood insurance. We also argue that increased granularity in flood risk communication could increase take-up before catastrophic flooding occurs.

We use the 2008 crisis as an exogenous shock to the pension funding status of U.S. corporate defined benefit (DB) pension plans to examine its impact on the assumption of the expected return on pension assets (ER). Contrary to prior literature, we find that DB pension plans transitioning from funded to underfunded status make expense-reducing assumptions by increasing their ER. We also document that the ER manipulation is larger for plans whose funding after 2008 drops significantly. The funding deterioration conservatively generates a 28–79 basis points increase in ER, reducing the pension accounting expense by about 5.23%–14.76% ($3.49 to $9.85 million). Our results are robust to controlling for the shock induced by the global financial crisis on corporate performance and other potential channels affecting ER.

This paper evaluates the effectiveness of Peak Health Alliance, a public–private initiative in Colorado aimed at lowering health care costs for employers and enrollees by increased bargaining power through the formation of a health care purchasing alliance. Using 2017–2021 plan data provided by the Colorado Department of Regulatory Affairs: Division of Insurance, we use difference-in-differences, event study, and synthetic control methods to compare changes in premiums in counties where Peak operated to other counties in Colorado before and after its implementation. The results suggest that Peak was associated with an increase in insurer market power and led to a 13%–17% decrease in average premiums, depending on the empirical specification. We further assess mechanisms underlying these effects and find evidence that lower prices were the most likely mechanism behind the estimated effect of Peak. Study results provide insights about the future of such public–private partnerships and their potential effectiveness.