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The risk retention rule requires issuers to retain part of their securities and share the interests of investors. The different forms of risk retention chosen lead to different financing effects of enterprises. In order to explore the optimal choice of risk retention forms in different environments, according to asset pricing theory and asset securitization practice combined with risk retention rules, we obtain issuer payoff models under three forms of risk retention. Through numerical simulation and economic meaning analysis, we draw the following conclusions: hybrid retention can not only alleviate the side effects of horizontal retention but also reduce the proportion of vertical retention, which can improve the issuer payoffs; horizontal retention is more suitable for situations where asset pool losses are small or asset volatility is large, while vertical retention is the opposite. Therefore, we suggest that securities issuers should consider the assets of enterprises and macroeconomic situations to choose the optimal form of risk retention.

Purpose – The purpose of this paper is to propose a product-service systems (PSS) risk management decision-making framework for PSS operation, which can enable global manufacturing companies to offer PSS successfully. Thus, the authors aim to contribute primarily to developing the PSS literature by integrating insights from the literatures on risk management and decision making. Design/methodology/approach – This study is based on an exploratory, single case study with a Swedish manufacturing company that has long-term experience with providing PSS. In total, the authors conducted 25 semi-structured interviews with diverse respondents from different functional units. Findings – The study’s main findings include identifying and proposing an interconnection between the operational risks associated with providing PSS, possible risk management responses, and decision criteria, all of which enable decision makers to select an appropriate risk management response. Research limitations/implications – The study contributes to the literature in three ways. First, the authors classify PSS operational risks into three categories related to delivery competence risks, technical risks, and behavioural risks. Second, the authors explain conditions under which each risk can be mitigated using different risk management strategies (avoidance, reduction, sharing/transfer, and retention). Finally, the authors combine different risk categorizations, decision criteria, and risk responses into an integrated decision framework of PSS risk management. Originality/value – The novel contribution is developing a PSS risk management decision support framework, which holds theoretical and practical value.

More than 50 countries rely on hydropower for over 25% of their electricity generation, making them vulnerable to drought and resulting revenue losses. Governments can offset financial losses for publicly-owned hydropower generators, but this can create fiscal pressures and lead to negative consequences, such as lower bond ratings. Index-based financial instruments, used to manage weather-related risk, offer an alternative, though data collection and index design are challenging. Using remotely sensed hydrometeorological data, we develop index insurance contracts to manage drought-related financial risk for hydropower-dependent countries. Low correlations in drought across these countries allow cost reductions when risks are pooled. Pooling the contracts yields average savings of 54% compared to individual risk management via reserves. These findings indicate that pooled index insurance can strengthen financial resilience in countries dependent on hydropower and support governments in mitigating drought-related economic risks. Using remotely sensed hydrometeorological data, the study designs index insurance contracts for hydropower-dependent countries. Pooling them globally cuts costs by 54%, strengthening financial resilience and helping governments manage drought-related economic risks.

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R2) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions.

We study the impact of the risk retention rule - requiring 5% of underlying credit risk for commercial mortgage backed securities - on commercial real estate markets. Since the primary objective of this rule is for the deal sponsors to have skin in the game, we expect that underwriting standards should tighten following the implementation of the rule. Consistent with this notion, we find the reform led to a decrease in price premium and probability of rating shopping by the sponsors, as well as longer time-to-securitization and lower default probability. We also show that the Dodd-Frank risk retention rule can impact banks’ credit supply by curtailing credit growth. As a result, we provide novel evidence on the effect of the risk retention rule on underwriters most exposed to the regulation.

This article considers the policy of retaining low-achieving children in kindergarten rather than promoting them to first grade. Under the stable unit treatment value assumption (SUTVA) as articulated by Rubin, each child at risk of retention has two potential outcomes: Y(1) if retained and Y(0) if promoted. But SUTVA is questionable, because a child's potential outcomes will plausibly depend on which school that child attends and also on treatment assignments of other children. We develop a causal model that allows school assignment and peer treatments to affect potential outcomes. We impose an identifying assumption that peer effects can be summarized through a scalar function of the vector of treatment assignments in a school. Using a large, nationally representative sample, we then estimate (1) the effect of being retained in kindergarten rather than being promoted to the first grade in schools having a low retention rate, (2) the retention effect in schools having a high retention rate, and (3) the effect of being promoted in a low-retention school as compared to being promoted in a high-retention school. This third effect is not definable under SUTVA. We use multilevel propensity score stratification to approximate a two-stage experiment. At the first stage, intact schools are blocked on covariates and then, within blocks, randomly assigned to a policy of retaining comparatively more or fewer children in kindergarten. At the second stage, \"at-risk\" students within schools are blocked on covariates and then assigned at random to be retained. We find evidence that retainees learned less on average than did similar children who were promoted, a result found in both high-retention and low-retention schools. We do not detect a peer treatment effect on low-risk students.

Loan screening and monitoring are critical to loan performance, but incentives are diminished for securitized loans. Risk retention is intended to harmonize the interests of originators and investors; however, it is unclear to what extent investors anticipate and respond to originators’ screening and monitoring incentives, particularly with respect to different types of risk retention. The theoretical literature suggests that equity retention is optimal in terms of screening efforts; thus, if investors anticipate these incentives, equity retention should lead to low credit spreads. Employing OLS and instrumental variables regressions, we empirically examine the effect of retention on spreads. Our analysis, based on a unique dataset of securitizations, reveals that the effects highly depend on the considered investment type. Credit spreads decrease by approx. 26 to 39 bps if the originator retains a material fraction of at least 5% of the deal’s nominal value. For tranches with high information sensitivity—where screening and monitoring incentives are most critical—investors, though, impose an additional risk premium of 120 basis points when originators fail to retain a substantial portion of the securitizations. In addition, we find that transactions with vertical slice retention are associated with a notably higher risk premium than those with equity retention, demonstrating the differential impact of retention structures on investor perceptions. Overall, our results underline that the extent of asymmetric information, particularly with respect to different types of investments and risk retention, is an important component in the pricing of securitizations.

The risk retention rule was introduced in the US and the EU as a mechanism to curb the originate-to-distribute model, associated with securitisations and the financial crisis of 2008. This article argues that besides its original financial stability rationale, the rule has positive spillovers on debt governance and specifically on the incentives to monitor, the design of covenants and the lender’s stance during renegotiation and bankruptcy (the ‘empty creditor’ problem). Risk retention in true sale securitisations makes the strongest case for debt governance, although the existence of various options of retention appears to be associated with varying incentives. For cases where monitoring is performed by a party different than the originator, the introduction of retention by the servicer is a promising although partial solution. The mechanism and effects of risk retention on synthetic securitisations remain ambivalent, given the perverse incentives associated with over-insurance (negative economic ownership). However, the upcoming restriction of double hedging for synthetic STS transactions is a positive development. Law & Finance, Financial Regulation, Debt Governance, Securitisation, Risk Retention

This paper investigates an optimal reinsurance problem for an insurance company with self-exciting claims, where the insurer’s historical claims affect the claim intensity itself. We focus on a claim-dependent proportional reinsurance contact, where the term “claim-dependent” signifies that the insurer’s risk retention ratio is allowed to depend on claim size. The insurer aims to maximize the expected utility of terminal wealth. By utilizing the dynamic programming principle and verification theorem, we obtain the optimal reinsurance strategy and corresponding value function in closed-form from the Hamilton–Jacobi–Bellman equation under an exponential utility function. We show that the claim-dependent proportional reinsurance is optimal among all types of reinsurance under the exponential utility maximization criterion. In addition, we present several analytical properties and numerical examples of the derived optimal strategy and provide economic insights through analytical and numerical analyses. In particular, we show the optimal claim-dependent proportional reinsurance can be considered as a continuous approximation of the step-wise risk sharing rule between the insurer and the reinsurer.