Enterprise risk management (ERM) has been the topic of increased media attention in recent years. The objective of this study is to measure the extent to which specific firms have implemented ERM programs and, then, to assess the value implications of these programs. We focus our attention in this study on U.S. insurers in order to control for differences that might arise from regulatory and market differences across industries. We simultaneously model the determinants of ERM and the effect of ERM on firm value. We estimate the effect of ERM on Tobin's Q, a standard proxy for firm value. We find a positive relation between firm value and the use of ERM. The ERM premium of roughly 20 percent is statistically and economically significant.

https://www.jstor.org/stable/info/41350401

The Value of Enterprise Risk Management

Companies in the same industry sector are usually more correlated than firms in different sectors, as they are similarly affected by macroeconomic effects, political decisions, and consumer trends. Despite the many stock return models taking this fact into account, there are only a few credit default models that take it into consideration. In this paper we present a default model based on nested Archimedean copulas that is able to capture hierarchical dependence structures among the obligors in a credit portfolio. Nested Archimedean copulas have a surprisingly simple and intuitive interpretation. The dependence among all companies in the same sector is described by an inner copula and the sectors are then coupled via an outer copula. Consequently, our model implies a larger default correlation for companies in the same industry sector than for companies in different sectors. A calibration to CDO tranche spreads of the European iTraxx portfolio is performed to demonstrate the fitting capability of the mode...

/pdf/cdo-pricing-with-nested-archimedean-copulas-cvo2wuq7oy.pdf

CDO pricing with nested Archimedean copulas

Risks exist in all aspects of our lives. Using data in both Scopus and ISI Web of Science, this review paper identifies pioneer work and pioneer scholars in enterprise risk management (ERM). Being ranked the first based on the review data, Desheng Wu has been active in this area by serving as a good academic network manager on the global research network, His global efforts with diverse networking have enabled him to publish outstanding papers in the field of ERM. Therefore, this paper also conducts a literature review of his papers and critical analysis of the work of Wu and Olson, from the perspective of the ERM, to glean implications and suggestions for the optimization and customization of the ERM.

Optimizing enterprise risk management: a literature review and critical analysis of the work of Wu and Olson

The regulation of insurance companies in the United States and the European Union (EU) continues to evolve in response to market forces and the changing nature of risk but with somewhat different philosophies and at different rates. One important area where both economic realities and markets are changing is catastrophe risk and its financing. This article examines and compares regulatory and other government policies in the United States and the EU generally and their approaches to the financing of catastrophe risk specifically. It is important to understand the fundamental differences between the two systems to gain insights into their disparate treatment of catastrophe risk financing. Although policies could be improved in both jurisdictions, we argue that the much greater reform is needed in the United States relative to the EU regulatory policies that are being developed. We offer recommendations on how U.S. policies could be significantly improved as well as comment on issues facing the EU. We conclude with some observations on the needs for further progress in the U.S. and EU regulatory systems.

Catastrophe Risk Financing in the United States and the European Union: A Comparative Analysis of Alternative Regulatory Approaches

Purpose – The objectives of this paper are to: define business risk; identify whether economic capital could be used to mitigate this risk; and investigate business‐risk measurement methodologies.Design/methodology/approach – The paper analyzes definitions used in theory and practice and derived a definition. It analyzes three measurement methodologies: analogue companies/peer group analysis, statistical methods, and scenario analysis. These methodologies are tested against the criteria of effective management control, because economic capital is increasingly used as a management control instrument.Findings – Economic capital can be used as business‐risk mitigant albeit not the only one. The measurement methodology of scenario analysis satisfies most of the criteria for effective control.Practical implications – This paper opens a discussion to further develop the scenario approach in theory and practice.Originality/value – Despite the amount of economic capital that financial institutions hold to cover b...

Defining and measuring business risk in an economic‐capital framework

This paper illustrates the modeling of dependence structures of non-life insurance risks using the Bernstein copula. We conduct a goodness-of-fit analysis and compare the Bernstein copula with other widely used copulas. Then, we illustrate the use of the Bernstein copula in a value-at-risk and tail-value-at-risk simulation study. For both analyses we utilize German claims data on storm, flood, and water damage insurance for calibration. Our results highlight the advantages of the Bernstein copula, including its flexibility in mapping inhomogeneous dependence structures and its easy use in a simulation context due to its representation as mixture of independent Beta densities. Practitioners and regulators working toward appropriate modeling of dependences in a risk management and solvency context can benefit from our results.

Dependence modeling in non-life insurance using the Bernstein copula

Purpose – The purpose of this paper is to transfer the concept of market‐consistent embedded value (MCEV) from life to non‐life insurance. This is an important undertaking since differences in management techniques between life and non‐life insurance make management at the group level very difficult. The purpose of this paper is to offer a solution to this problem.Design/methodology/approach – After explaining MCEV, the authors derive differences between life and non‐life insurance and develop a MCEV model for non‐life business. The model framework is applied to a German non‐life insurance company to illustrate its usefulness in different applications.Findings – The authors show an MCEV calculation based on empirical data and set up an economic balance sheet. The value implications of varying loss ratios, cancellation rates, and costs within a sensitivity analysis are analyzed. The usefulness of the model is illustrated within a value‐added analysis. The authors also embed the MCEV concept in a simplified...

/pdf/market-consistent-embedded-value-in-non-life-insurance-how-29wxfjhum7.pdf

Market‐consistent embedded value in non‐life insurance: how to measure it and why

The aim of this paper is to expand on recent contributions in the field of risk modelling for non-life insurance companies by modelling insurance risk in a multi-year context Academic literature on non-life insurance risk to date has only considered an ultimo perspective (using traditional methods) and, more recently, a one-year perspective (for solvency purposes) However, strategic management in an insurance company requires a multi-year time horizon for economic decision making, providing the motivation for this paper This is the first paper to derive analytically closed formulae for multi-year non-life insurance risk in the additive loss reserving model as defined by variation of multi-year claims development results Embedding future accident years leads to an integrated approach to quantifying multi-year risk arising from the settlement of outstanding claims (reserve risk) and future claims yet to occur (premium risk) An application study will serve to illustrate the usefulness of the new multi-year horizon

/pdf/the-multi-year-non-life-insurance-risk-in-the-additive-loss-46piedgcud.pdf

The multi-year non-life insurance risk in the additive loss reserving model

This is the first study to derive closed-form analytical expressions for multi-year non-life insurance risk in the chain ladder model. Extending on previous research on the additive reserving model, we define multi-year risk via prediction errors of multi-year claims development results including both observed and future accident years. A resampling argument and a first-order Taylor approximation address the quantification of estimation errors and multiplicative dependencies in the chain ladder framework, respectively. From our generalized multi-year approach, we deduce estimators for reserve and premium risks in multi-year view and their implicit correlation. We reproduce well-known results from literature for the special cases of one-year and ultimo view. Further, we comment on how to obtain estimators for generalized versions of the chain ladder method. A case study demonstrates the applicability of our analytical formulae.

Addendum to ‘The multi-year non-life insurance risk in the additive reserving model’ [Insurance Math. Econom. 52(3) (2013) 590–598]: Quantification of multi-year non-life insurance risk in chain ladder reserving models

The aim of this paper is to extend recent contributions in the field of risk modeling for non-life insurance companies by modeling insurance risk in a multi-year context. Academic literature on nonlife insurance risk to date has only considered an ultimo perspective (using traditional methods) and, more recently, a one-year perspective (for solvency purposes). This paper is motivated by the fact that strategic management in an insurance company requires a multi-year time horizon for economic decision making, e.g., in the context of internal risk models. We extend the simulation-based method for quantifying the one-year non-life insurance risk presented in Ohlsson and Lauzeningks (2009) to a multi-year perspective. Moreover, we present a simulation approach for calculating the risk margin which can be consistently integrated in the model so that approximation approaches are no longer needed. The usefulness of the new multi-year horizon is illustrated in the context of internal risk models using an application to a claims development triangle based on Mack (1993) and England and Verrall (2006).

/pdf/the-multi-year-non-life-insurance-risk-a0zso0jbml.pdf

The Multi-Year Non-Life Insurance Risk

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