Management Strategies in Multi-year Enterprise Risk Management

Diers, Dorothea

doi:10.1057/gpp.2010.39

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…The DFA model oriented to the structure of, e.g., [11] and [26] has been created and developed to simulate the evolution of the financial and risk situation of an insurer for a wide range of possible scenarios. 1 The simulation results demonstrate how internal and external conditions can influence the financial results of the company [9].…”

Section: Integration Of Stress Testing In Dfamentioning

confidence: 99%

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Identification of Company-Specific Stress Scenarios in Non-Life Insurance

Weidner

Schulenburg²

2016

ACM

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This paper provides an effective approach, known as dynamic financial analysis, to the systematic development of stress scenarios for the risk profile of non-life insurers, which can be used in risk analysis for the regulatory and rating assessment. The determination of company-specific stress scenarios is demonstrated, the resulting critical scenarios are described. Non-linear dependencies have a significant impact on the scenarios, some of which have not previously been adequately considered are introduced. The recent global financial crisis illustrates that the analysis of extreme events, which can affect both sides of the balance sheet, is essential in an asset-liability management context.

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Section: Integration Of Stress Testing In Dfamentioning

confidence: 99%

“…Along these lines, a multi-year risk capital concept assuming a time horizon of five periods is considered [26]. The random variable MaxLoss, which corresponds to the maximum of the cumulative loss of each simulation, is defined as follows: .…”

Section: Simulation Modelmentioning

confidence: 99%

Identification of Company-Specific Stress Scenarios in Non-Life Insurance

Weidner

Schulenburg²

2016

ACM

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“…In the following we use the multi-year model framework presented in Diers (2011), which was extended in Diers (2012). In a multi-year framework, premium risk is measured from the cumulated…”

Section: Framework For Modeling Parameter Risk In Premium Riskmentioning

confidence: 99%

“…The remainder of this paper is structured as follows: Section 2 contains a brief summary of statistical estimation theory, demonstrates the sources and relevance of parameter risk in non-life insurance, and introduces the multi-year model framework for internal risk models to allow for parameter risk, where we extend the multi-year model approach presented in Diers (2011) and Diers (2012). Section 3 presents three approaches to modeling parameter risk: the asymptotic normality approach, the bootstrap method, and the Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

Modeling parameter risk in premium risk in multi‐year internal models

Diers¹,

Eling

Linde

2013

The Journal of Risk Finance

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The purpose of this paper is to illustrate the importance of modeling parameter risk in premium risk, especially when data are scarce and a multi-year projection horizon is considered. Internal risk models often integrate both process and parameter risks in modeling reserve risk, whereas parameter risk is typically omitted in premium risk, the modeling of which considers only process risk. We present a variety of methods for modeling parameter risk (asymptotic normality, bootstrap, Bayesian) with different statistical properties. We then integrate these different modeling approaches in an internal risk model and compare our results with those from modeling approaches that measure only process risk in premium risk. We show that parameter risk is substantial, especially when a multi-year projection horizon is considered and when there is only limited historical data available for parameterization (as is often the case in practice). Our results also demonstrate that parameter risk substantially influences risk-based capital and strategic management decisions, such as reinsurance. Our findings emphasize that it is necessary to integrate parameter risk in risk modeling. Our findings are thus not only of interest to academics, but of high relevance to practitioners and regulators working toward appropriate risk modeling in an enterprise risk management and solvency context.

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“…Diers (2011) proposes a multi-year model approach to enable the company’s ORSA as a part of ERM, and Doff (2016) tenders specific prescribed and targeted stress tests through the ORSA to improve the effectiveness of Solvency II.…”

Section: Introductionmentioning

confidence: 99%

Enterprise risk management and Solvency II: the system of governance and the Own Risk and Solvency Assessment

Santomil

González

2020

JRF

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Purpose The purpose of this paper is to analyze how enterprise risk management (ERM), the system of governance and the Own Risk and Solvency Assessment (ORSA) have been boosted with the entry of Solvency II. Design/methodology/approach For this analysis, the authors have undertaken a survey of chief risk officers (CROs) working in Spanish insurance companies. Findings The results show that Solvency II has definitely promoted ERM in the European insurance industry and improved the system of governance of the insurance companies, and that the perceived value of the ORSA for the companies is higher than the cost. It is clear that the quality of ERM implemented by companies is higher in those that face more complex risks and with greater interdependencies – that is, larger companies, foreign insurers and insurers with several lines of business – but is unaffected by the legal form of the entity (mutual/corporation). Originality/value This study conducts primary research with surveys of CROs and develops a measure of the quality of ERM implemented by insurance companies.

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Management Strategies in Multi-year Enterprise Risk Management

Cited by 16 publications

References 15 publications

Identification of Company-Specific Stress Scenarios in Non-Life Insurance

Identification of Company-Specific Stress Scenarios in Non-Life Insurance

Modeling parameter risk in premium risk in multi‐year internal models

Enterprise risk management and Solvency II: the system of governance and the Own Risk and Solvency Assessment

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