Difficult risks and capital models

Frankland, R.; Eshun, S.; Hewitt, L.; Jakhria, Parit; Jarvis, Stuart; Rowe, Andrew J.; Smith, Aaron; Sharp, A. C.; Sharpe, James; Wilkins, Trevor

doi:10.1017/s1357321713000524

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…In this situation, there are pressures of various kinds pushing you towards the cheaper option. The importance of behavioural issues, in the face of uncertainty, to organisations in general has been recognised for a long time – see, for example, Kahneman and Lovallo (1993) – and lately also by the banking (Pollock, 2018) and the insurance industry (Frankland et al, 2014; Haddrill et al, 2016; Jakhria, 2014; Tredger et al, 2016; Tsanakas et al, 2016; Weick et al, 2012). Many of the issues described in the following appear indeed in more than one of these references.…”

Section: Decisions In the Face Of Uncertainty And Incentivesmentioning

confidence: 99%

“…how to deal properly with copulas – see Donnelly and Embrechts (2010) who provide besides a comprehensive list of references) to regulation issues, for example how to enhance the stability of the banking system, see Hellwig (2009) and Sinn (2010). More recent literature, such as MacKenzie and Spears (2014a), looks more closely at organisational issues, and ultimately at social interaction; interestingly, a part of this literature was initiated and/or produced by the (predominantly quantitative) actuarial profession, and in particular in the UK, see Haddrill et al (2016), Frankland et al (2014), Tredger et al (2016).…”

Section: Introductionmentioning

confidence: 99%

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The Global Financial Crisis – risk transfer, insurance layers, and (lack of?) reinsurance culture

Fackler

2023

Br. Actuar. J.

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The Global Financial Crisis of 2007–2008 has elicited various debates, ranging from ethics over the stability of the banking system to subtle technical issues regarding the Gaussian and other copulas. We want to look at the crisis from a particular perspective. Credit derivatives have much in common with treaty reinsurance, including risk transfer via pooling and layering, scarce data, skewed distributions, and a limited number of specialised players in the market. This leads to a special mixture of mathematical/statistical and behavioural challenges. Reinsurers have been struggling to cope with these, not always successfully, but they have learned some lessons over the course of more than one century in business. This has led to certain rules being adopted by the reinsurance market and to a certain mindset being adopted by the individuals working in the industry. Some cultures established in the reinsurance world could possibly inspire markets like the credit derivatives market, but the subtle differences between the two worlds matter. We will see that traditional reinsurance has built-in incentives for (some) fairness, while securitisation can foster opportunism.

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Section: Decisions In the Face Of Uncertainty And Incentivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Global Financial Crisis – risk transfer, insurance layers, and (lack of?) reinsurance culture

Fackler

2023

Br. Actuar. J.

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Section: Expertisementioning

confidence: 99%

“…1.3.2. The importance of expert judgement has been recognised by other working parties; for example, the Extreme Events Working Party in their recent Difficult Risk and Capital Models paper (Frankland et al , 2013) commented: any model will necessarily require some degree of expert judgement; expert judgement and data-driven assumptions are not mutually exclusive concepts; and capital models contain big inherent risks that are often ignored, for example, model risk, what risks to model, etc. and so understanding the expert judgement that arrived at the current modelling approach can be critical to getting comfort on the approach taken. …”

Section: Introductionmentioning

confidence: 99%

Expert judgement

Ashcroft¹,

Austin²,

Barnes³

et al. 2016

Br. Actuar. J.

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Expert judgement has been used since the actuarial profession was founded. In the past, there has often been a lack of transparency regarding the use of expert judgement, even though those judgements could have a very significant impact on the outputs of calculations and the decisions made by organisations. The lack of transparency has a number of dimensions, including the nature of the underlying judgements, as well as the process used to derive those judgements. This paper aims to provide a practical framework regarding expert judgement processes, and how those processes may be validated. It includes a worked example illustrating how the process could be used for setting a particular assumption. It concludes with some suggested tools for use within expert judgement. Although primarily focussed on the insurance sector, the proposed process framework could be applied more widely without the need for significant changes.

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“…As a consequence, the estimate of the risk measure (and thus the required capital) will depend on observed data and generally differ from its theoretical value. Limited data do not allow the identification of the correct model, for example due to the low power of statistical Goodness-of-Fit tests observed in practical applications (Frankland et al, 2014), an issue made worse by structural changes in the data generating process.…”

Section: Introductionmentioning

confidence: 99%

Model uncertainty in risk capital measurement

Bignozzi¹,

Tsanakas²

2016

JOR

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The required solvency capital for a financial portfolio is typically given by a tail risk measure such as Value-at-Risk. Estimating the value of that risk measure from a limited, often small, sample of data gives rise to potential errors in the selection of the statistical model and the estimation of its parameters. We propose to quantify the effectiveness of a capital estimation procedure via the notions of residual estimation risk and estimated capital risk. It is shown that for capital estimation procedures that do not require the specification of a model (eg historical simulation) or for worst-case scenario procedures the impact of model uncertainty is substantial, while capital estimation procedures that allow for multiple candidate models using Bayesian methods, partially eliminate model error. In the same setting, we propose a way of quantifying model error that allows to disentangle the impact of model uncertainty from that of parameter uncertainty. We illustrate these ideas by simulation examples considering standard loss and return distributions used in banking and insurance.

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Difficult risks and capital models

Cited by 11 publications

References 15 publications

The Global Financial Crisis – risk transfer, insurance layers, and (lack of?) reinsurance culture

The Global Financial Crisis – risk transfer, insurance layers, and (lack of?) reinsurance culture

Expert judgement

Model uncertainty in risk capital measurement

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