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This article briefly reviews the background of weather derivatives. The primary goal is to develop a pricing scheme that accommodates and reflects their unique characteristics. Because the underlying indexes of weather derivatives are not traded, a no‐arbitrage model cannot be directly applied for the purpose of pricing. The actuarial technique is a feasible choice but cannot be applied in the traditional fashion, because the historical data are characterized by long‐term variability and trends that are difficult to define and correct based purely on the data. The pricing scheme developed in this article attempts to address these concerns by combining information from both empirical analysis of historical data and numerical simulations.

This article provides a general introduction to using catastrophe models to optimally manage the risk of a portfolio of Property & Casualty (P&C) liabilities. There is increasing emphasis on the enterprise‐wide allocation of risk capacity for all financial intermediaries, e.g. banks, pensions, investment funds, as well as life and P&C insurers. The optionality (the skewness, kurtosis, and correlation with asset risk) of liability risks contribute substantially to earnings volatility. The severity of low‐probability events, i.e. natural catastrophes (e.g. hurricanes, earthquakes), combined with increases in geographic concentrations of wealth can adversely affect the diversification of the liability risk at the portfolio level. Since in both finance and insurance, optimally allocating risk at the portfolio level is generally based on (linear) combinations of nonlinear risks, finding an optimal allocation is not always tractable. The author describes a well‐established optimization algorithm and produces a reasonable approximation for an optimal solution.

Industry loss index‐based risk transfer and management instruments such as the industry loss warranty (ILW) and other catastrophe insurance derivative products have proliferated in recent years. This article introduces an alternative measure of the ILW basis risk, specifically the conditional probability that the ILW policy does not pay out, given an actual loss sustained by the policyholder that exceeds some critical level. The author also discusses the effectiveness of upwardly oriented basis risk in reducing loss volatility. After introducing guidelines for choosing between an ILW and traditional reinsurance, the article concludes that a properly structured ILW can be an effective and innovative instrument for a large insurer or reinsurer to manage the severity and volatility of catastrophe losses, but not necessarily, for a medium‐sized or small (re)insurer. Although this article focuses on ILWs, the general methodology and conclusions presented are applicable to other index‐based risk transfer products.

Demonstrates the feasibility of, and introduces a practical approach to enhancing, reinsurance efficiency using index‐based instruments.

First reviews the general mathematical framework of reinsurance optimization. Next, illustrates how index‐based instruments can potentially enhance reinsurance efficiency through a simple yet self‐contained example. The simplicity allows the analytical examination of the cost and benefits of an index‐based contract. Finally, introduces a real‐world model that optimizes index‐based reinsurance instruments using the genetic algorithm.

Identifies the key factors that determine the efficiency of index‐based reinsurance contracts and demonstrates that, in the property catastrophe reinsurance market, the combined effect of these factors frequently allows the construction of an index‐based hedging program that is more efficient than a traditional excess‐of‐loss reinsurance contract. A robust optimization model based on the genetic algorithm is introduced and shown to be effective in optimizing index‐based reinsurance contracts.

Most financial optimization procedures are subject to parameter risk, which can adversely affect the robustness of their solutions. The reinsurance optimization approach presented in this paper is not completely immune from this problem. It remains a challenging problem for actuarial researchers and practitioners.

The concept and method proposed in this paper can be applied to designing real‐world reinsurance programs.

This paper makes two contributions to the risk finance literature: a systematic approach for evaluating the costs and benefits of index‐based reinsurance instruments, and an innovative and practical model for optimizing reinsurance efficiency.

In this article, the authors develop an arbitrage approach to valuing insurance‐linked securities (ILS) for non‐catastrophic events within a framework of stochastic interest rates. The prices of these transactions are driven by both an interest rate process and a non‐trivial actuarial risk process. The authors find that the duration of ILS is, in most cases, higher than the Macaulay duration of risk‐free bonds, which implies that the alleged relative out‐performance of ILS is illusory.

The recent trend of integrated risk management has resulted in corporations reassessing their risk management practices. Insurance derivatives and insurance‐linked securities are emerging as alternatives or complements to traditional resisurance capacity. Despite its theoretical benefits, the market for insurance‐linked transactions has not matured, due to problems of information asymmetry and lack of transparency. This article proposes a solution to resolve the conflicting interests preventing insurers/reinsurers and investors from more widely trading insurance risk.

Insurance companies exist to manage the risk of others, which is why they are perceived to be competitive in risk management (RM). Considering this, we investigate how different RM capabilities make insurers effective in RM. These capabilities include understanding risk and risk management (URRM), risk identification (RI), risk assessment and analysis (RAA) and risk monitoring (RMON) activities in insurance companies. In addition, the authors probe how these capabilities can jointly yield a competitive advantage for the insurance industry under the resource-based view (RBV) and dynamic capabilities perspective (DCP).

The authors present a latent variable RM model for the insurance industry and employ structural equation modeling (SEM) to test the hypotheses. Furthermore, the authors also conduct confirmatory factor analysis (CFA) and convergent and discriminant validity analysis for model fit and invariance testing, respectively.

The results show that insurers who investigated RM-related capabilities directly influence their risk management practices (RMPs). Moreover, improving these capabilities will make insurers more effective in managing the risks of others. Thus, RM as a business process will yield a competitive advantage for the insurance sector. The findings are supported by the theoretical insights presented by the RBV and DCP. Furthermore, the model also adheres to the convergent and discriminant validity cut-off values.

To the best of the authors’ knowledge, this is the first study examining insurers' RM practices as a source of a competitive advantage.

保險公司存在的目的是為其它公司或個人管理其風險；因此，保險公司在風險管理方面、被認為具有競爭能力。故此、我們擬研究不同的風險管理能力是如何能使保險公司有效地管理風險的呢？這些風險管理能力包括對風險及風險管理之了解、風險辨識、風險評估和分析，以及在保險公司內的風險監控活動。再者，我們探究這些風險管理能力如何根據資源基礎觀點及動態能力理論共同為保險業創造競爭優勢。

我們為保險業展示一個潛在變項風險管理模型，並使用結構方程模型，來測試我們的假設；而且，我們為模型適配度而進行了驗證性因素分析，又為不變性檢定而進行了驗證輻合及驗證區別效度分析。

研究結果顯示、若保險公司審査與風險管理相關之能力，這會直接影響其對風險管理之措施；而且，若保險公司能改善其風險管理之能力，這會使它們更有效地管理其它公司或個人的風險。因此，作為業務過程的一環、風險管理會為保險業創造競爭優勢。我們的研究結果，得到資源基礎理論及動態能力理論提供之理論見解所支持；而且，我們的模型從附驗證輻合及驗證區別效度的截止值。

據我們所知，本研究為首個研究、去探討保險公司的風險管理措施如何為它們創造競爭優勢。

The main goal of this paper is to present a synthetic multiple criteria group decision-making (MCGDM) methodology for assessing the enterprise digital maturity with linear Diophantine fuzzy (LDF) setting.

This paper utilizes the presented LDF generalized Dombi operator to aggregate assessment information of experts. The developed combined weight model through merging the rank sum (RS) model and symmetry point of criterion (SPC) method is used to ascertain the comprehensive importance of criterion. The evaluation based on distance from average solution (EDAS) approach based upon regret theory (RT) is presented to achieve the sorting of candidate enterprises.

Firstly, the proposed method has strong stability. Secondly, the proposed method takes into consideration the psychological behavior of experts during the decision-making process which further enhances the rationality of the decision results. Finally, the proposed method integrates expert and criterion weight determination models which provides a practical evaluation framework for assessing the digital maturity of enterprises. The research outcomes confirm that the proposed approach fails to resolve the decision problems with unknown weight information flexibly, but also reflect the psychological behavior of expert in decision process. The presented weight approach also provides a rational algorithm to ascertain the weight more accurate.

A composite LDF group decision-making approach is presented by aggregating the proposed generalized Dombi operator, combined weight model and the EDAS model, which make the outcome more reasonable. Sensitivity analysis and comparison study are conducted to reflect the superiority of the proposed approach.

Drawing from the resource-based view (RBV), this study aims to develop a parsimonious model in the context of digital platforms that links strategic network resources (SNR) and firm performance through considering dynamic capabilities (DC) as important mediating mechanisms. In addition, we also investigate how platform monitoring shapes the relationship between SNR and DC.

This study uses the survey data from 162 firms in eastern China.

The findings indicate that both two DC dimensions (i.e., sensing and reconfiguring) significantly mediate the relationship of SNR-performance. Moreover, platform monitoring positively moderates the relationship of SNR and sensing as well as SNR and reconfiguring.

With these findings, this study advances SNR and digital platform research and provides insights into how to transform SNR into superior performance through DC.

This paper aims to investigate how perpetrators who engage in unethical pro-organizational behavior (UPB) feel and respond in the aftermath of such behavior.

This paper used a two-wave time-lagged design and collected data from 260 full-time employees working in different industries in China.

The results indicated that UPB was negatively and indirectly associated with internal whistle-blowing through shame. Perceived moral leadership weakened the effect of shame on internal whistle-blowing.

Based on affective events theory, this paper explored an integrated behavior-emotion-behavior sequence. This paper proposed that the negative emotion, shame, evoked by UPB subsequently influences the extent to which UPB perpetrators engage in internal whistle-blowing.