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Across a wide set of nongroup insurance markets, applicants are rejected based on observable, often high-risk, characteristics. This paper argues that private information, held by the potential applicant pool, explains rejections. I formulate this argument by developing and testing a model in which agents may have private information about their risk. I first derive a new no-trade result that theoretically explains how private information could cause rejections. I then develop a new empirical methodology to test whether this no-trade condition can explain rejections. The methodology uses subjective probability elicitations as noisy measures of agents' beliefs. I apply this approach to three nongroup markets: long-term care, disability, and life insurance. Consistent with the predictions of the theory, in all three settings I find significant amounts of private information held by those who would be rejected; I find generally more private information for those who would be rejected relative to those who can purchase insurance, and I show it is enough private information to explain a complete absence of trade for those who would be rejected. The results suggest that private information prevents the existence of large segments of these three major insurance markets.

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance.

This paper extends the theoretical literature on underwriting cycles by assuming insurers have heterogeneous exposure to a catastrophe. Distinct from the existing literature on insurance cycles, we model optimal contracting by competitive insurers. Since losses take time to pay out, and insurers are better informed about their catastrophe exposure than external investors, catastrophes compromise the capital-raising ability of insurers by increasing asymmetric information. Capital is restricted following a catastrophe because investors do not know the catastrophe exposure of each insurer, not because of explicit costs of raising capital. Thus, insurers decide to hold less capital following a catastrophe, giving rise to the insurance cycle.

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring.

We show that state corruption and political connections have strong effects on municipal bond sales and underwriting. Higher state corruption is associated with greater credit risk and higher bond yields. Corrupt states can eliminate the corruption yield penalty by purchasing credit enhancements. Underwriting fees were significantly higher during an era when underwriters made political contributions to win underwriting business. This pay-toplay underwriting fee premium exists only for negotiated bid bonds where underwriting business can be allocated on the basis of political favoritism. Overall, our results show a strong impact of corruption and political connections on financial market outcomes.

The life care annuity—the integration of the life annuity with long-term care insurance coverage—is intended to deal with major problems in the currently separate markets for life annuities and long-term care insurance. The integration would allow the inclusion of most of the population currently rejected by underwriting—those in poor health or lifestyles but who would not go immediately into long-term care claim—who also have lower life expectancies. We make use of the Health and Retirement Study, on individuals in retirement and their disability incidence, exploiting the panel nature of the survey to estimate transition probabilities in and out of disability states according to numerous demographic and health characteristics. This allows for analysis of disability and mortality risk across a number of dimensions. We find that different risk groups at age 65 have similar projected long-term care expenses, but that the level-periodic-premium structure of most long-term care insurance policies creates incentives for individuals to separate into different risk pools according to observable characteristics, justifying the underwriting observed on the market. Yet we also find that gender-rated life care annuities could succeed in pooling risks currently segmented in the market for long-term care insurance, thus qualifying individuals at or near retirement for permanent long-term care insurance coverage who do not currently qualify, and allowing for life annuities to be purchased more cheaply than in the stand-alone annuity market now subject to adverse selection.

We examine the long-standing question of whether firms derive value from investment bank relationships by studying how the Lehman collapse affected industrial firms that received underwriting, advisory, analyst, and market-making services from Lehman. Equity underwriting clients experienced an abnormal return of around -5%, on average, in the 7 days surrounding Lehman's bankruptcy, amounting to $23 billion in aggregate risk-adjusted losses. Losses were especially severe for companies that had stronger and broader security underwriting relationships with Lehman or were smaller, younger, and more financially constrained. Other client groups were not adversely affected.

This paper examines whether there are efficiencies that benefit issuers and underwriters when a financial intermediary concurrently lends to an issuer while also underwriting its public securities offering. We find issuers, particularly noninvestment-grade issuers for whom informational economies of scope are likely to be large, benefit through lower underwriter fees and discounted loan yield spreads. Underwriters, both commercial banks as well as investment banks, engage in concurrent lending and provide price discounts, albeit in different ways. We find concurrent lending helps underwriters build relationships, increasing the probability of receiving current and future business.

We study the effect of the Global Analyst Research Settlement and related regulations on sell-side research. These regulations attempted to mitigate the interdependence between research and investment banking. We document that following the regulations many brokerage houses have migrated from the traditional five-tier rating system to a three-tier system. Optimistic recommendations have become less frequent and more informative, whereas neutral and pessimistic recommendations have become more frequent and less informative. Importantly, the overall informativeness of recommendations has declined. The likelihood of issuing optimistic recommendations no longer depends on affiliation with the covered firm, although affiliated analysts are still reluctant to issue pessimistic recommendations.

We study the effect of financial relationships on firms' investment decisions and access to external finance. In the early twentieth century, securities underwriters commonly held directorships with American corporations. Section 10 of the Clayton Antitrust Act prohibited bankers from serving on the boards of railroads for which they underwrote securities. We find that following the implementation of Section 10, railroads with strong preexisting relationships with underwriters saw declines in their investment rates, valuations, and leverage, and increases in their costs of external funds. Reassuringly, we do not observe similar effects among industrials and utilities, which were not subject to Section 10. Our results are consistent with underwriters on corporate boards acting as delegated monitors, and highlight the potential for regulations intended to address conflicts of interest to disrupt valuable information flows.