This study examines the asset pricing implications of systemic cyber risk. We consider an insurer exposed to systemic cyber events that jointly affect insurance losses and financial market conditions in a regime-switching economy. The insurer transfers part of its cyber exposure to financial investors through a cyber-catastrophe bond, under which investors receive fixed coupon payments while optimally choosing consumption and portfolio allocations. Using a general equilibrium framework, we show that systemic cyber risk is priced through a covariance channel in the equilibrium equity premium. The resulting risk premium depends not only on aggregate dividend risk but crucially also on the covariance between systemic cyber events and market conditions.

Extant literature has documented that catastrophe risk overall drives reinsurance demand, yet it has not been addressed whether a firm’s persistent underwriting exposure to catastrophe-prone lines drives reinsurance demand temporarily with the shocks of actual disaster losses. To fill this gap, this study examines whether two dimensions of catastrophe risk—underwriting exposure and disaster shocks—each independently drive reinsurance demand. We further examine whether these responses vary with firm financial characteristics, such as capital-to-asset ratios. We analyze 17,909 firm-year observations of U.S. property-liability insurers from 1996 to 2021 using NAIC filings and SHELDUS loss data. Underwriting exposure captures each insurers concentration in catastrophe-prone lines and disaster shocks scale realized property damage by each insurer’s market share in those lines. We estimate each measure in a separate OLS specification with firm and year fixed effects. Which dimension dominates as the primary driver has direct regulatory implications—exposure-driven demand calls for ex-ante portfolio-based oversight, while shock-driven demand necessitates post-disaster buffer requirements. We find that both underwriting exposure and disaster shocks independently and positively drive the reinsurance ratio. The results also show that insurers with lower capital-to-asset ratios demand significantly more reinsurance, indicating heterogeneous reinsurance responses across insurers. Our findings are useful for regulators and risk managers who seek firm-specific, risk-based criteria for evaluating catastrophe reinsurance adequacy and supporting the long-run sustainability of property-liability insurance markets.

This study investigates whether firms’ cyber risk management (CRM) activities mitigate the adverse effects of cyber incidents, which may generate substantial downside risk for firm value. We measure the impact of cyber incidents using the tail risk of firms’ stock returns, which captures changes in downside risk reflected in the stock market. Using OLS regressions, we first show that the occurrence of a cyber incident is associated with an increase in the tail risk of the affected firm’s stock returns over the post-incident period. Having established that cyber incidents increase firms’ tail risk, we next examine whether firms’ CRM activities mitigate this increase within a difference-in-difference-in-differences (DDD) framework. Using firm-level data spanning a broad cross-section of industries, we implement propensity score matching (PSM) prior to estimation in order to construct comparable treatment and control samples. The results indicate that firms engaging in CRM experience a smaller post-incident increase in tail risk than similar firms without such activities. Additional analyses by incident type reveal that the mitigating effect of CRM varies across different types of cyber incidents, suggesting that CRM is more effective in addressing some cyber risk channels than others. This heterogeneity highlights both the potential and the limitations of CRM as a firm-level risk management mechanism. Overall, the findings indicate that corporate risk management can play a substantive role in mitigating cyber-related downside risk and offer useful implications for the design of firm-level cyber risk response strategies and related policy frameworks.

Insurance-linked securities such as catastrophe (CAT) bonds are designed to transfer tail risk to capital markets, but this mechanism can fail when insured losses coincide with financial market stress. Motivated by evidence of market-level stress after major catastrophe incidents, we develop a two-regime continuous-time framework in which catastrophe-loss intensity is stochastic, and dependence between catastrophe claims and equity returns is present in the disaster regime. On the insurer’s side, the reinsurance-portfolio problem delivers closed-form regime-dependent policies and determines the maximum affordable spread for CAT bond issuance under a cost-parity benchmark. On the investor’s side, CAT bond principal losses concentrated in low-wealth states raise investors’ required spreads. Combining these two sides yields a bid-ask feasibility condition under which standard CAT bonds can fail to clear despite greater market depth. We then propose a catastrophe put option (CatPut) as a hybrid mechanism, where an insurer-funded equity-index put option is triggered only when both catastrophe and equity market thresholds are breached. We show in simulations that the CatPut can restore market clearing over a nontrivial, but layer-specific design space, implying that crisis-contingent redesign of CAT bonds can be effective in increasingly contagious risk landscapes.

This study examines how firms respond to carbon regulations in their ESG management and whether ESG ratings are properly aligned with actual emissions. Using 592 publicly listed Korean firms from 2015 to 2024, we estimate Difference-in-Differences (DiD) models with firm and year fixed effects and address staggered adoption of carbon regulations. We find evidence that the earlier Target Management System (TMS), as a weaker regulation than the Emissions Trading Scheme (ETS), has no significant effect on firms’ ESG environmental scores, whereas entry into the stricter ETS is associated with a statistically significant and persistent increase in environmental scores. Yet these rating gains do not translate into real mitigation by showing no statistical evidence on a change in the rate of greenhouse gas (GHG) emissions reduction. Moreover, Granger causality tests indicate that increases in environmental scores do not precede subsequent reductions in emissions intensity. Our results suggest a decoupling between environmental scores and actual emissions outcomes after ETS entry. These findings highlight the need for regulatory designs that can induce firms to initiate measurable and verifiable reductions in greenhouse gas emissions.

This study investigates whether investors distinguish between contagious and non-contagious cyber risks in the cross-section of stock returns. Although recent literature establishes cyber risk as a priced factor in financial markets, asset pricing theory predicts that only its non-diversifiable component should command a risk premium, suggesting that the key dimension is not cyber risk broadly, but its potential for systemic propagation. We propose a novel text-based framework using a transformer-based language model to classify contagious cyber incidents and estimate firm-level ex-ante exposure via a machine learning algorithm. Our event-study analysis reveals that contagious incidents are associated with significantly larger and more persistent negative abnormal returns over an extended event windows, with these effects particularly pronounced for highly leveraged firms. Firms with higher exposure to contagious cyber risks earn a significant positive risk premium of approximately 33 basis points per month, whereas exposure to non-contagious risk is not priced. We conclude that investors selectively price cyber risk, identifying contagion as the primary channel through which cyber threats become a systematic, non-diversifiable risk factor.

We examine how managerial preferences, internal risk-control practices, and financial frictions jointly shape insurance demand among small and medium-sized enterprises (SMEs). Using a unique dataset of 693 Korean SMEs that links 2023 survey measures of managerial risk aversion and risk-control orientation with 2020-2022 financial statements, we estimate baseline OLS models, a nonlinear CEO-ownership threshold model, and finite mixture regressions to capture latent heterogeneity in insurance-demand behavior. The OLS results show that SMEs led by more risk-averse managers purchase significantly more discretionary insurance, and that internal risk-control activities complement rather than substitute for insurance. Insurance demand also rises with tax incentives and financial constraints but declines with firm size. Threshold estimation reveals a non-monotonic ownership effect: insurance demand decreases with CEO ownership below 37% but increases beyond it. Finite mixture models uncover three distinct behavioral regimes - reference-and-control, preference-only, and financial-driven -each characterized by different mechanisms linking preferences, controls, and financial conditions to insurance purchases. These findings demonstrate that SMEs do not follow a single representative demand rule and that pooled estimates obscure economically meaningful heterogeneity. The results offer implications for targeted insurance design, SME risk-management policy, and credit-risk assessment.

Traditional indemnity-based cyber insurance struggles to keep pace with the escalating frequency and complexity of cyber threats, resulting in a widening protection gap for industry. To address this, we propose a novel cyber parametric insurance framework based on firm-specific cyber vulnerability that is estimated using a dynamic factor model incorporating geographic location, firm size, and industry sector. Our framework helps cyber insurers reduce basis risk by accounting for temporal dynamics and firm heterogeneity into cyber risk assessment. It can also facilitate to tailor a risk mitigation solution by considering substantial variations in corporate vulnerability that is integrated into the parametric index with the duration of cyberattacks. We provide supporting evidence on this benefit of the proposed framework by demonstrating a reduction in basis risk of our parametric insurance without increasing insolvency, compared to a benchmark. This study can contribute to the development of sustainable and scalable cyber parametric insurance markets.

Climate change risk emerges as a critical concern of Property & Casualty (P&C) insurers due to its causal effect on increasing natural disasters across the globe. However, a challenge to quantify ongoing climate change can make it difficult to assess how this progress may affect their underwriting performance. To address this concern, we employ the Actuaries Climate Index (ACI) to examine the impact of climate change risk on P&C insurers’ performance. We propose a local-level ACI by using meteorological information of regions in South Korea and employ a copula simulation to implement a stress test to determine the impact of climate change on insurers’ exposures. Our findings indicate that insurers with high geographic concentration in the capital region are more vulnerable to climate risk exposure.

This study investigates whether corporate environmental, social, and governance (ESG) activities enhance or hinder operational efficiency in the insurance industry, a sector widely regarded as inherently aligned with sustainability objectives. Contrary to the common expectation that ESG activities improve operational performance, our results show that insurers with higher ESG scores tend to exhibit lower business efficiency, particularly in terms of pure technical efficiency. This negative association persists when one-year lagged ESG measures are considered. We also provide evidence that while ESG activities are found to contribute to risk management and, in some cases, financial performance,these benefits do not translate into improvements in operational efficiency in the short term. Our findings suggest that ESG initiatives in the insurance industry may involve substantial operational and organizational costs that outweigh their efficiency-enhancing effects, at least in the near term.