FRECUENCIA DE USO DE LAS ESCALAS CLÍNICAS

We have claimed that the technical availability of prices for every level of risk (individual and enterprise-wide) assumed by an insurance company has opened a field with a new set of possibilities. Most crucially, new possibilities arose for a scientific and technical self-governance via legitimate risk management practices on an enterprise

83 _{Typically, users keep a record of successive period results. Differences in successive periods results} are always checked and analyzed since they carry much informational value about the appropriateness of data and assumptions used. For example, an unanticipated peak or fall of a result beyond a reasonably expected range relative to its historical development is thoroughly scrutinized. Such thorough investigations may dictate for a different perspective on the way a specific set of data or assumptions has been historically used or currently interpreted.

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level, beyond silo barriers which typically obscured interconnections. The possibilities disclosed do not restrict themselves to current assessments but rather institute a decisive forward-looking and anticipating organizational infrastructure, suitable to come to terms with a potentially disruptive and catastrophic future. Such possibilities are thought to serve the main objectives of S2, which are policyholder protection and financial stability.

However, a performative analysis of the S2 model revealed a paradoxical aspect: the more risk is managed technocratically, i.e., the more insurers strive to implement and use the S2 model in an efficient self-disciplinary way, the more the insurer herself (due to efficient gaming) or her macro environment (due to procyclical effects) turn unstable, endangering solvency and policyholder protection. This, we claim, is an inherent aporia within the S2 project itself, serving as an aspect of a more general aporia in the concept of solvency more broadly.

An aporia for Derrida is an event that prevents a metaphysical discourse from fulfilling its promised unity – not a contradiction that can be eventually resolved into the unity of the concept, but an “untotalizable” problem at the heart of the concept, disrupting its trajectory and opening out its closure (Burke, 2002:4-5). It is not just a rhetorical term to denote doubt or “difficulty in choosing” (Royle, 2008:92); it is more radical, a sort of absolute blockage – not in the way a huge rock suddenly collapses and blocks the road indefinitely, but more of an event whereby “success fails” and “failure succeeds” (Derrida, 1986:35). Such an aporetic structure denotes that no notion, including that of solvency, is identical to itself, endowed that is, with an essential, autonomous being (Hill, 2010:40).

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This point differentiates the approach taken here from other analyses in the Social Studies of Finance that equally account for performativity effects. As already mentioned in the introduction, Lockwood (2015) is keen on stressing the (counter) performative effects of Value-at-Risk financial models, which share the same ontological architecture as the S2 model. Although Lockwood (2015) acknowledges that she does “not advocate specific financial regulatory reform”, her performative critique is offered to reveal current shortcomings of VaR and diagnose how its dominance makes it difficult “to create space for alternative or additional ways [such as subjective judgments and macroprudential regulation84] to acknowledge, act in, and respond to a world of risk, uncertainty, and reflexivity.” (2015:749). After all, the first step towards a healthy organization is to have a clear diagnosis of the particular disease that endangers it: then, and only then, does it become possible to fight the disease and restore or preserve the organization’s healthy status. Performativity is thus used as a resource in such a noble cause: to enhance solvency and stave off instability.

Our perspective is closer to the work of Svetlova (2012) who claims that “models are not performative per se. The performative power of models depends on the way they are used.” (2012:421). Her point is that many models,

“fail to be performative because they are part of ‘calculative cultures’ where market participants must adjust them to market complexity. … Model users account for unrealistic assumptions and neglected factors by applying their own judgements. Precisely because this judgement is necessary, the model is not an ultimate determinant of decision and action.” (Svetlova, 2012:422)

84 _{Macroprudential regulation is an extra layer of regulation focused on the macroeconomic curbing of} the procyclical effects that are created by the microeconomic use of S2 and Value-at-Risk-type models.

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Our argument against Lockwood’s strategy is that her appeal to a “soft” rationality capable of policing, or delimiting, or substituting “hard” modeling cannot restore any healthy solvency status. This is not because something alternative, more (or perhaps less) is still needed. The point is to move beyond notions of deficiencies, not by attempting to compensate for them but by precisely letting them be, by realizing that we are always already “deficient” – and we call that “efficient”. For Svetlova, we simply cannot know whether a model may perform, even if widely adopted, since models are always and already open to soft adjustments to assume relevance in the “meshwork” (Ingold, 2011) and complexities of the market life happening (Introna, 2018:8). In a similar vein, counter-performance alternatives, such as subjective judgments and macroprudential regulation (the ones Lockwood appeals to), may simply fail, even if widely adopted, since such soft imperatives are always and already open to hard adjustments in order to assume reliability and allow for responsible decision making.

In fact, S2, despite its commonly recognized science-based, technical and hard structure, is surprisingly keen on providing a wide range of soft alternatives to manage its own potential model performativity. For example, as we have already discussed in the introduction, despite the commitment of S2 to market consistency (that is, to fairness and transparency), calls for prudence (which is subjective and opaque) are central within its standards (as if the co-existence of market consistency and prudence is unproblematic or natural). Further demands for soft qualitative overlays, such as proportionality, simplifications and the four-eyes principle, are scattered throughout the S2 Directive.

What is more, a surprisingly extended range of tools is provided to manage market consistency’s tendency to beget procyclicality. For example, the equity stress in the standard formula includes a countercyclical capital requirement (called “symmetric

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adjustment”); its effect is to temporarily reduce the capital requirement and provide breathing space for insurers to manage the timing of any asset disposals (Rae et al., 2017:36). Additionally, a so-called Long-Term Guarantees (LTG) package includes a set of measures with the aim of eliminating “artificial” volatility from the balance sheet of insurers by removing S2 away from “full” market consistency. Such a package has received fierce criticism on the grounds that it is not “economic” (Danielson et al., 2011; Swarup, 2012; Wuthrich, 2011), i.e., not scientifically sound.

Another issue is the so-called Ultimate Forward Rate (UFR), which has a material impact on the discounting of deep long-term liabilities of the insurance industry for which no liquid financial instrument is available. In this case, the S2 yield curve is extrapolated from a stipulated Last Liquid Point (LLP) to the UFR in such a way that the extrapolated curve converges to the UFR over a period of many years (Rae et al., 2017:11). Obviously, both the determination of the UFR and the rate of convergence towards it can only be “imagined” and are heavily dependent on the selected method of calculation. This is why their nature is widely considered more as the locus of political compromises and less as science. In fact, Sven Giegold, a member of the European Parliament, came so far as to argue that “the setting of the UFR (the expected long-term level of future interest rates) requires democratic legitimation as it represents a collective bet on long-term economics. It [thus] should not be left to the discretion of a [technical] authority [such as EIOPA]” (Solvency II Wire, 2013, italics added).

Another soft issue is the use of regulatory flexibility: although under S2 it looks as though insurers would go technically insolvent before any forbearance is possible (Rae et al., 2017:40), a new article added to the S2 Directive after the financial crisis (Article 28) stipulates that “authorities shall duly consider the potential impact of their decisions on the stability of the financial system... In times of exceptional movements in financial

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markets, supervisory authorities shall take into account the potential pro-cyclical effects of their actions.” (Hulle, 2017:317). What is “exceptional” obviously rests on imaginary discretion; for example, one cannot but wonder, under the current low interest rate environment, how low and how long interest rates need to remain for an “exceptional” market condition to be declared (Rae et al., 2017:39).

That said, beyond such explicit soft transgressions which blur the scientific application of S2’s standards, we would like to suggest that S2 incorporates in its application an inexplicit, and thus more radical, play between the technical and the prudential, the scientific and non-scientific, deep inside its algorithmic code.

Our case can be found in the calibration of the equity risk submodule (one of the market risk submodules). In a simplified view, the whole point of the equity risk submodule is to come up with a risk charge for the equity holdings of a company that will ensure a 99.5% market calibration. In simple terms, if an insurer holds an equity portfolio of, e.g., €1 million, the point is to find out the most the insurer could expect to lose over the next year with a 99.5% level of confidence. Would it be, for example, €300k, thus resulting in a risk charge of 30%? Or €400k, thus resulting in a risk charge of 40%? As already discussed, in the S2 scientific framework, only historical, empirical market data can answer this question, not political narratives about how markets work or should work. In the calibration study that was carried out by EIOPA85, we read that, based on similar technical analyses, there exist two views; one is called the “majority view” (because it is supported by the majority of member states) and calls for a 45% risk charge while the other, called the “minority view”, calls for a 39% risk charge (CEIOPS, 2010:41). What is interesting is how, within the study, it is demonstrated in an evidence-

85 _{See “Solvency II Calibration Paper”, CEIOPS-SEC-40-10, 15 April 2010; quoted hereafter as CEIOPS,} 2010.

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based, technical and scientific way that the 39% minority view “understates the equity stress” (CEIOPS, 2010:39), i.e., it does not constitute a “truly” 99.5% market-based calibration, and thus should not be opted for. However, within the final S2 requirements, the risk charge that was finally selected was indeed the 39%.86 So, what happened between the calibration proposal of the 45% scientific view and the final legislative selection of the 39% risk charge? Obviously, a range of explanations can be provided, from those that advance a “conspiracy/power theory” (i.e., the insurance industry lobby overpowering the scientific, economic and evidence-based calibration of the EIOPA) to less suspicious ones, that simply point to expert judgments and concerns ultimately tipping the balance in favor of the minority view. 87

Whatever the reasons for the adoption of the lower risk charge of the minority view, our point has to do with the “aporia” involved in the calibration/application project. Specifically, an economically pure calibration at such a high level of 99.5% renders capital requirements so burdensome for the industry that it will necessarily end up reducing its diversity by allowing only larger and thus fewer insurers to achieve solvency by S2 standards, which in itself implies an increase of the insurance industry’s systemic risk – precisely what is to be avoided in the first place. On the other hand, a looser, non-economic calibration may preserve the industry’s diversity by allowing more and smaller insurers to achieve solvency by S2 standards; however, it opens up the possibility for a higher rate of future insolvencies, eventually increasing the insurance industry’s systemic risk. Therefore, in the case of S2 calibration/application, it seems that it is impossible to hold the lines between the economic and non-economic, the scientific and the non-scientific. The only way to achieve a scientific/economic

86 _{Article 169 of the final Commission Delegated Regulation 2015/35.}

87 _{What is equally interesting to note is how the technical analysis of the calibration project is supported} by a “majority” and “minority” view – as if the technical does not enjoy any “automatic” legitimacy.

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solvency calibration is precisely by breaching the economic and evidence-based conditions that make it possible and allow for non-economic, political and expert judgments (its “other”) to be heard.

In other words, for the S2 model to preserve its ideal technical character (which legitimates solvency capital calculations and its self-governance capabilities), it needs to be “de-idealized” (Svetlova, 2013) to account precisely for the aporetic structure of its raison d’être (i.e., solvency). In that respect, as we move deeper into the “tails of the future”, that is, into a world that is getting more complex, more interconnected and thus potentially more destructive and disruptive, “the relationships between science, expertise, and decision are radically rearticulated so that distinctions between ‘science’ and ‘non-science’ become more malleable.” (Amoore, 2013:9).