Journal of Risk and Insurance最新文献

Registered Index-Linked Annuities (RILAs) have quickly become one of the most popular retirement savings and investment vehicles in the United States. Researchers have analyzed their ability to help investors accumulate wealth—and have praised them for their relatively low cost and transparency—but have not yet considered whether RILAs can be a suitable component of retirement planning during the decumulation phase as well. This study aims to fill that gap by embedding RILAs in a lifecycle utility framework that models an investor's optimal decision-making post-retirement. In that context, I find that RILAs are essentially a like-for-like substitute for traditional mutual funds, in terms of both the total utility provided to the retiree and his optimal consumption and annuitization decisions.

I investigate how property and casualty insurers affected by the mega natural disasters of 2005, 2012, and 2017 adjusted their capital ratios and operations during the post-disaster period. After the 2005 and 2012 events, affected insurers raised their capital ratios more than non-affected insurers, relative to the period immediately preceding the events. However, following the 2017 event, affected insurers did not significantly increase their capital ratios relative to the pre-event period. Further analysis reveals that this result is primarily driven by Florida-focused insurers, which have less diversified operations and lower AM Best ratings, reflecting higher insolvency risk. These insurers may have faced higher costs of raising capital or had incentives to take on more risk, suggesting risk-shifting behavior. Additionally, some affected insurers adjusted their asset portfolios and reinsurance usage. Overall, the results suggest that insurers aim to maintain target capital ratios and that specific firm characteristics influence their adjustment behaviors.

With the rise of emerging risks, model uncertainty poses a fundamental challenge in the insurance industry, making robust pricing a first-order question. This paper investigates how insurers' robustness preferences shape competitive equilibrium in a dynamic insurance market. Insurers optimize their underwriting and liquidity management strategies to maximize shareholder value, leading to equilibrium outcomes that can be analytically derived and numerically solved. Compared to a benchmark without model uncertainty, robust insurance pricing results in significantly higher premiums and equity valuations. Notably, our model yields three novel insights: (1) The minimum, maximum, and admissible range of aggregate capacity all expand, indicating that insurers' liquidity management becomes more conservative. (2) The expected length of the underwriting cycle increases substantially, far exceeding the range commonly reported in earlier empirical studies. (3) While the capacity process remains ergodic in the long run, the stationary density becomes more concentrated in low-capacity states, implying that liquidity-constrained insurers require longer to recover. Together, these findings provide a potential explanation for recent skepticism regarding the empirical evidence of underwriting cycles, suggesting that such cycles may indeed exist but are considerably longer than previously assumed.

As the origin of modern commercial insurance, ship insurance underpins global maritime supply chain stability. Yet shipping modernization and AI advances expose three flaws in traditional risk profiling: misalignment with frequency-severity pricing, inadequate for accommodating to complex risk factor system, and lack of data stream adjustment mechanisms. To address these, we propose POM principles (Personalized risk portrait, Omnispective risk factors, and Maneuverable calibration) and an AI framework with three cores: (1) extensible “retrospective + prospective” risk factors; (2) independent AI modules for premium rate/insurance amount prediction; (3) data steam calibration on historical data. Validated via 15,007 records (15% of China's 2016–2021 registered ships) using random forest regression, it outperforms traditional generalized linear models and mainstream machine learning models in accuracy and risk differentiation. This pioneers intelligent ship insurance profiling, fills gaps in individualized pricing, and offers insights for sectors like aviation insurance, sharing its “premium rate × insurance amount” logic.