Tag Archives: too big to fail

Beautiful Models

It has been a while since I posted on dear old Solvency II (here). As highlighted in the previous post on potential losses, the insurance sector is perceived as having robust capital levels that mitigates against the current pricing and investment return headwinds. It is therefore interesting to look at some of detail emerging from the new Solvency II framework in Europe, including the mandatory disclosures in the new Solvency and Financial Condition Report (SFCR).

The June 2017 Financial Stability report from EIOPA, the European insurance regulatory, contains some interesting aggregate data from across the European insurance sector. The graph below shows solvency capital requirement (SCR) ratios, primarily driven by the standard formula, averaging consistently around 200% for non-life, life and composite insurers. The ratio is the regulatory capital requirement, as calculated by a mandated standard formula or a firm’s own internal model, divided by assets excess liabilities (as per Solvency II valuation rules). As the risk profile of each business model would suggest, the variability around the average SCR ratio is largest for the non-life insurers, followed by life insurers, with the least volatile being the composite insurers.

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For some reason, which I can’t completely comprehend, the EIOPA Financial Stability report highlights differences in the SCR breakdown (as per the standard formula, expressed as a % of net basic SCR) across countries, as per the graph below, assumingly due to the different profiles of each country’s insurance sector.

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A review across several SFCRs from the larger European insurers and reinsurers who use internal models to calculate their SCRs highlights the differences in their risk profiles. A health warning on any such comparison should be stressed given the different risk categories and modelling methodologies used by each firm (the varying treatment of asset credit risk or business/operational risk are good examples of the differing approaches). The graph below shows each main risk category as a percentage of the undiversified total SCR.

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By way of putting the internal model components in context, the graph below shows the SCR breakdown as a percentage of total assets (which obviously reflects insurance liabilities and the associated capital held against same). This comparison is also fraught with difficulty as an (re)insurers’ total assets is not necessarily a reliable measure of extreme insurance exposure in the same way as risk weighted assets is for banks (used as the denominator in bank capital ratios). For example, some life insurers can have low insurance related liabilities and associated assets (e.g. for mortality related business) compared to other insurance products (e.g. most non-life exposures).

Notwithstanding that caveat, the graph below shows a marked difference between firms depending upon whether they are a reinsurer or insurer, or whether they are a life, non-life or composite insurer (other items such as retail versus commercial business, local or cross-border, specialty versus homogeneous are also factors).

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Initial reactions by commentators on the insurance sector to the disclosures by European insurers through SFCRs have been mixed. Some have expressed disappointment at the level and consistency of detail being disclosed. Regulators will have their hands full in ensuring that sufficiently robust standards relating to such disclosures are met.

Regulators will also have to ensure a fair and consistent approach across all European jurisdictions is adopted in calculating SCRs, particularly for those calculated using internal models, whilst avoiding the pitfall of forcing everybody to use the same assumptions and methodology. Recent reports suggest that EIOPA is looking for a greater role in approving all internal models across Europe. Systemic model risk under the proposed Basel II banking regulatory rules published in 2004 is arguably one of the contributors to the financial crisis.

Only time will tell if Solvency II has avoided the mistakes of Basel II in the handling of such beautiful models.

Divine Diversification

There have been some interesting developments in the US insurance sector on the issue of systemically important financial institutions (SIFIs). Metlife announced plans to separate some of their US life retail units to avoid the designation whilst shareholder pressure is mounting on AIG to do the same. These events are symptoms of global regulations designed to address the “too big to fail” issue through higher capital requirements. It is interesting however that these regulations are having an impact in the insurance sector rather than the more impactful issue within the banking sector (this may have to do with the situation where the larger banks will retain their SIFI status unless the splits are significant).

The developments also fly in the face of the risk management argument articulated by the insurance industry that diversification is the answer to the ills of failure. This is the case AIG are arguing to counter calls for a breakup. Indeed, the industry uses the diversification of risk in their defences against the sector being deemed of systemic import, as the exhibit below from a report on systemic risk in insurance from an industry group, the Geneva Association, in 2010 illustrates. Although the point is often laboured by the insurance sector (there still remains important correlations between each of the risk types), the graph does make a valid point.

click to enlargeEconomic Capital Breakdown for European Banks and Insurers

The 1st of January this year marked the introduction of the new Solvency II regulatory regime for insurers in Europe, some 15 years after work begun on the new regime. The new risk based solvency regime allows insurers to use their own internal models to calculate their required capital and to direct their risk management framework. A flurry of internal model approvals by EU regulators were announced in the run-up to the new year, although the amount of approvals was far short of that anticipated in the years running up to January 2016. There will no doubt be some messy teething issues as the new regime is introduced. In a recent post, I highlighted the hoped for increased disclosures from European insurers on their risk profiles which will result from Solvency II. It is interesting that Fitch came out his week and stated that “Solvency II metrics are not comparable between insurers due to their different calculation approaches and will therefore not be a direct driver of ratings” citing issues such as the application of transitional measures and different regulator approaches to internal model approvals.

I have written many times on the dangers of overtly generous diversification benefits (here, here, here, and here are just a few!) and this post continues that theme. A number of the large European insurers have already published details of their internal model calculations in annual reports, investor and analyst presentations. The graphic below shows the results from 3 large insurers and 3 large reinsurers which again illustrate the point on diversification between risk types.

click to enlargeInternal Model Breakdown for European Insurers and Reinsurers

The reinsurers show, as one would expect, the largest diversification benefit between risk types (remember there is also significant diversification benefits assumed within risk types, more on that later) ranging from 35% to 40%. The insurers, depending upon business mix, only show between 20% and 30% diversification across risk types. The impact of tax offsets is also interesting with one reinsurer claiming a further 17% benefit! A caveat on these figures is needed, as Fitch points out; as different firms use differing terminology and methodology (credit risk is a good example of significant differences). I compared the diversification benefits assumed by these firms against what the figure would be using the standard formula correlation matrix and the correlations assuming total independence between the risk types (e.g. square root of the sum of squares), as below.

click to enlargeDiversification Levels within European Insurers and Reinsurers

What can be seen clearly is that many of these firms, using their own internal models, are assuming diversification benefits roughly equal to that between those in the standard formula and those if the risk types were totally independent. I also included the diversification levels if 10% and 25% correlations were added to the correlation matrix in the standard formula. A valid question for these firms by investors is whether they are being overgenerous on their assumed diversification. The closer to total independence they are, the more sceptical I would be!

Assumed diversification within each risk type can also be material. Although I can understand arguments on underwriting risk types given different portfolio mixes, it is hard to understand the levels assumed within market risk, as the graph below on the disclosed figures from two firms show. Its hard for individual firms to argue they have material differing expectations of the interaction between interest rates, spreads, property, FX or equities!

click to enlargeDiversification Levels within Market Risk

Diversification within the life underwriting risk module can also be significant (e.g. 40% to 50%) particularly where firms write significant mortality and longevity type exposures. Within the non-life underwriting risk module, diversification between the premium, reserving and catastrophe risks also add-up. The correlations in the standard formula on diversification between business classes vary between 25% and 50%.

By way of a thought experiment, I constructed a non-life portfolio made up of five business classes (X1 to X5) with varying risk profiles (each class set with a return on equity expectation of between 10% and 12% at a capital level of 1 in 500 or 99.8% confidence level for each), as the graph below shows. Although many aggregate profiles may reflect ROEs of 10% to 12%, in my view, business classes in the current market are likely to have a more skewed profile around that range.

click to enlargeSample Insurance Portfolio Profile

I then aggregated the business classes at varying correlations (simple point correlations in the random variable generator before the imposition of the differing distributions) and added a net expense load of 5% across the portfolio (bringing the expected combined ratio from 90% to 95% for the portfolio). The different resulting portfolio ROEs for the different correlation levels shows the impact of each assumption, as below.

click to enlargePortfolio Risk Profile various correlations

The experiment shows that a reasonably diverse portfolio that can be expected to produce a risk adjusted ROE of between 14% and 12% (again at a 1 in 500 level)with correlations assumed at between 25% and 50% amongst the underlying business classes. If however, the correlations are between 75% and 100% then the same portfolio is only producing risk adjusted ROEs of between 10% and 4%.

As correlations tend to increase dramatically in stress situations, it highlights the dangers of overtly generous diversification assumptions and for me it illustrates the need to be wary of firms that claim divine diversification.

Stressing the scenario testing

Scenario and stress testing by financial regulators has become a common supervisory tool since the financial crisis. The EU, the US and the UK all now regularly stress their banks using detailed adverse scenarios. In a recent presentation, Moody’s Analytics illustrated the variation in some of the metrics in the adverse scenarios used in recent tests by regulators, as per the graphic below of the peak to trough fall in real GDP.

click to enlargeBanking Stress Tests

Many commentators have criticized these tests for their inconsistency and flawed methodology while pointing out the political conflict many regulators with responsibility for financial stability have. They cannot be seen to be promoting a draconian scenario for stress testing on the one hand whilst assuring markets of the stability of the system on the other hand.

The EU tests have particularly had a credibility problem given the political difficulties in really stressing possible scenarios (hello, a Euro break-up?). An article last year by Morris Goldstein stated:

“By refusing to include a rigorous leverage ratio test, by allowing banks to artificially inflate bank capital, by engaging in wholesale monkey business with tax deferred assets, and also by ruling out a deflation scenario, the ECB produced estimates of the aggregate capital shortfall and a country pattern of bank failures that are not believable.”

In a report from the Adam Smith Institute in July, Kevin Dowd (a vocal critic of the regulator’s approach) stated that the Bank of England’s 2014 tests were lacking in credibility and “that the Bank’s risk models are worse than useless because they give false risk comfort”. Dowd points to the US where the annual Comprehensive Capital Assessment and Review (CCAR) tests have been supplemented by the DFAST tests mandated under Dodd Frank (these use a more standard approach to provide relative tests between banks). In the US, the whole process has been turned into a vast and expensive industry with consultants (many of them ex-regulators!) making a fortune on ever increasing compliance requirements. The end result may be that the original objectives have been somewhat lost.

According to a report from a duo of Columba University professors, banks have learned to game the system whereby “outcomes have become more predictable and therefore arguably less informative”. The worry here is that, to ensure a consistent application across the sector, regulators have been captured by their models and are perpetuating group think by dictating “good” and “bad” business models. Whatever about the dangers of the free market dictating optimal business models (and Lord knows there’s plenty of evidence on that subject!!), relying on regulators to do so is, well, scary.

To my way of thinking, the underlying issue here results from the systemic “too big to fail” nature of many regulated firms. Capitalism is (supposedly!) based upon punishing imprudent risk taking through the threat of bankruptcy and therefore we should be encouraging a diverse range of business models with sensible sizes that don’t, individually or in clusters, threaten financial stability.

On the merits of using stress testing for banks, Dowd quipped that “it is surely better to have no radar at all than a blind one that no-one can rely upon” and concluded that the Bank of England should, rather harshly in my view, scrap the whole process. Although I agree with many of the criticisms, I think the process does have merit. To be fair, many regulators understand the limitations of the approach. Recently Deputy Governor Jon Cunliffe of the Bank of England admitted the fragilities of some of their testing and stated that “a development of this approach would be to use stress testing more counter-cyclically”.

The insurance sector, particularly the non-life sector, has a longer history with stress and scenario testing. Lloyds of London has long required its syndicates to run mandatory realistic disaster scenarios (RDS), primarily focussed on known natural and man-made events. The most recent RDS are set out in the exhibit below.

click to enlargeLloyds Realistic Disaster Scenarios 2015

A valid criticism of the RDS approach is that insurers know what to expect and are therefore able to game the system. Risk models such as the commercial catastrophe models sold by firms like RMS and AIR have proven ever adapt at running historical or theoretical scenarios through today’s modern exposures to get estimates of losses to insurers. The difficulty comes in assigning probabilities to known natural events where the historical data is only really reliable for the past 100 years or so and where man-made events in the modern world, such as terrorism or cyber risks, are virtually impossible to predict. I previously highlighted some of the concerns on the methodology used in many models (e.g. on correlation here and VaR here) used to assess insurance capital which have now been embedded into the new European regulatory framework Solvency II, calibrated at a 1-in-200 year level.

The Prudential Regulatory Authority (PRA), now part of the Bank of England, detailed a set of scenarios last month to stress test its non-life insurance sector in 2015. The detail of these tests is summarised in the exhibit below.

click to enlargePRA General Insurance Stress Test 2015

Robert Childs, the chairman of the Hiscox group, raised some eye brows by saying the PRA tests did not go far enough and called for a war game type exercise to see “how a serious catastrophe may play out”. Childs proposed that such an exercise would mean that regulators would have the confidence in industry to get on with dealing with the aftermath of any such catastrophe without undue fussing from the authorities.

An efficient insurance sector is important to economic growth and development by facilitating trade and commerce through risk mitigation and dispersion, thereby allowing firms to more effectively allocate capital to productive means. Too much “fussing” by regulators through overly conservative capital requirements, maybe resulting from overtly pessimistic stress tests, can result in economic growth being impinged by excess cost. However, given the movement globally towards larger insurers, which in my view will accelerate under Solvency II given its unrestricted credit for diversification, the regulator’s focus on financial stability and the experiences in banking mean that fussy regulation will be in vogue for some time to come.

The scenarios selected by the PRA are interesting in that the focus for known natural catastrophes is on a frequency of large events as opposed to an emphasis on severity in the Lloyds’ RDS. It’s arguable that the probability of the 2 major European storms in one year or 3 US storms in one year is significantly more remote than the 1 in 200 probability level at which capital is set under Solvency II. One of the more interesting scenarios is the reverse stress test such that the firm becomes unviable. I am sure many firms will select a combination of events with an implied probability of all occurring with one year so remote as to be impossible. Or select some ultra extreme events such as the Cumbre Vieja mega-tsunami (as per this post). A lack of imagination in looking at different scenarios would be a pity as good risk management should be open to really testing portfolios rather than running through the same old known events.

New scenarios are constantly being suggested by researchers. Swiss Re recently published a paper on a reoccurrence of the New Madrid cluster of earthquakes of 1811/1812 which they estimated could result in $300 billion of losses of which 50% would be insured (breakdown as per the exhibit below). Swiss Re estimates the probability of such an event at 1 in 500 years or roughly a 10% chance of occurrence within the next 50 years.

click to enlarge1811 New Madrid Earthquakes repeated

Another interesting scenario, developed by the University of Cambridge and Lloyds, which is technologically possible, is a cyber attack on the US power grid (in this report). There have been a growing number of cases of hacking into power grids in the US and Europe which make this scenario ever more real. The authors estimate the event at a 1 in 200 year probability and detail three scenarios (S1, S2, and the extreme X1) with insured losses ranging from $20 billion to $70 billion, as per the exhibit below. These figures are far greater than the probable maximum loss (PML) estimated for the sector by a March UK industry report (as per this post).

click to enlargeCyber Blackout Scenario

I think it will be a very long time before any insurer willingly publishes the results of scenarios that could cause it to be in financial difficulty. I may be naive but I think that is a pity because insurance is a risk business and increased transparency could only lead to more efficient capital allocations across the sector. Everybody claiming that they can survive any foreseeable event up to a notional probability of occurrence (such as 1 in 200 years) can only lead to misplaced solace. History shows us that, in the real world, risk has a habit of surprising, and not in a good way. Imaginative stress and scenario testing, performed in an efficient and transparent way, may help to lessen the surprise. Nothing however can change the fact that the “unknown unknowns” will always remain.