Intragroup transfers, intragroup diversification and their risk assessment

Haier, Andreas; Molchanov, Ilya; Schmutz, Michael

doi:10.1007/s10436-016-0284-6

Cited by 13 publications

(9 citation statements)

References 25 publications

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“…Our paper confirms the economic implications that are discussed in Embrechts et al (2017) and Wang (2016). A description and analysis of corporate groups can be found in Keller (2007), Filipovic & Kupper (2008) and Haier, Molchanov & Schmutz (2016).…”

Section: Literaturesupporting

confidence: 86%

Solvency II, or how to sweep the downside risk under the carpet

Weber

2018

Insurance: Mathematics and Economics

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Under Solvency II the computation of capital requirements is based on value at risk (V@R). V@R is a quantile-based risk measure and neglects extreme risks in the tail. V@R belongs to the family of distortion risk measures. A serious deficiency of V@R is that firms can hide their total downside risk in corporate networks, unless a consolidated solvency balance sheet is required for each economic scenario. In this case, they can largely reduce their total capital requirements via appropriate transfer agreements within a network structure consisting of sufficiently many entities and thereby circumvent capital regulation. We prove several versions of such a result for general distortion risk measures of V@R-type, explicitly construct suitable allocations of the network portfolio, and finally demonstrate how these findings can be extended beyond distortion risk measures. We also discuss why consolidation requirements cannot completely eliminate this problem. Capital regulation should thus be based on coherent or convex risk measures like average value at risk or expectiles.

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Section: Literaturesupporting

confidence: 86%

Solvency II, or how to sweep the downside risk under the carpet

Weber

2018

Insurance: Mathematics and Economics

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“…Here, we used the standard double arrow notation ⇒ to denote a map taking values in the power set of M. The map R can be seen as a generalisation of the set-valued risk measures studied by Jouini et al [32], Kulikov [35], Hamel and Heyde [28], Hamel et al [30] and Molchanov and Cascos [37] in the context of markets with transaction costs; by Haier et al [27] in the context of intragroup transfers; by Feinstein et al [22], Armenti et al [3] and Ararat and Rudloff [1] in the context of systemic risk. We refer to these contributions for a discussion about the financial interpretation of set-valued risk measures in the respective fields of application and to Sect.…”

Section: Introductionmentioning

confidence: 99%

Multi-utility representations of incomplete preferences induced by set-valued risk measures

Munari

2020

Finance Stoch

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We establish a variety of numerical representations of preference relations induced by set-valued risk measures. Because of the general incompleteness of such preferences, we have to deal with multi-utility representations. We look for representations that are both parsimonious (the family of representing functionals is indexed by a tractable set of parameters) and well behaved (the representing functionals satisfy nice regularity properties with respect to the structure of the underlying space of alternatives). The key to our results is a general dual representation of set-valued risk measures that unifies the existing dual representations in the literature and highlights their link with duality results for scalar risk measures.

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“…Optimal risk sharing between insurance and reinsurance companies has been considered by various authors. Research either takes the perspective of individual entities [10,17,7] or, more recently, insurance groups [21,18,23,15,5]. Because of the ownership structure and related transparency of risk exposures, an optimisation of risk transfer for insurance groups can start from the comprehensive group standpoint.…”

Section: Introductionmentioning

confidence: 99%

Optimal risk-sharing across a network of insurance companies

Ettlin

Farkas

Kull

et al. 2020

Insurance: Mathematics and Economics

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Risk transfer is a key risk and capital management tool for insurance companies. Transferring risk between insurers is used to mitigate risk and manage capital requirements. We investigate risk transfer in the context of a network environment of insurers and consider capital costs and capital constraints at the level of individual insurance companies. We demonstrate that the optimisation of profitability across the network can be achieved through risk transfer. Considering only individual insurance companies, there is no unique optimal solution and, a priori, it is not clear which solutions are fair. However, from a network perspective, we derive a unique fair solution in the sense of cooperative game theory. Implications for systemic risk are briefly discussed.

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Intragroup transfers, intragroup diversification and their risk assessment

Cited by 13 publications

References 25 publications

Solvency II, or how to sweep the downside risk under the carpet

Solvency II, or how to sweep the downside risk under the carpet

Multi-utility representations of incomplete preferences induced by set-valued risk measures

Optimal risk-sharing across a network of insurance companies

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