Tag Archives: property catastrophe pricing

More ILS illuminations

A continuation of the theme in this post.

The pictures and stories that have emerged from the impact of the tsunami from the Sulawesi earthquake in Indonesia are heart-breaking. With nearly 2,000 officially declared dead, it is estimated that another 5,000 are missing with hundreds of thousands more severely impacted. This event will be used as an vivid example of the impact of soil liquefaction whereby water pressure generated by the earthquake causes soil to behave like a liquid with massive destructive impacts. The effect on so many people of this natural disaster in this part of the world contrasts sharply with the impact on developed countries of natural disasters. It again highlights the wealth divide within our world and how technologies in the western world could benefit so many people around the world if only money and wealth were not such a determinant of who survives and who dies from nature’s wrath.

The death toll from Hurricane Florence on the US, in contrast, is around 40 people. The possibility of another US hurricane making landfall this week, currently called Tropical Storm Michael, is unfolding. The economic losses of Hurricane Florence are currently estimated between $25 billion and $30 billion, primarily from flood damage. Insured losses will be low in comparison, with some estimates around $3-5 billion (one estimate is as high as $10 billion). The insured losses are likely to be incurred by the National Flood Insurance Program (NFIP), private flood insurers (surplus line players including some Lloyds’ Syndicates), crop and auto insurers, with a modest level of losses ceded to the traditional reinsurance and insurance-linked securities (ILS) markets.

The reason for the low level of insured loss is the low take-up rate of flood policies (flood is excluded from standard homeowner policies), estimated around 15% of insurance policies in the impacted region, with a higher propensity on the commercial side. Florence again highlights the protection gap issue (i.e. percentage difference between insured and economic loss) whereby insurance is failing in its fundamental economic purpose of spreading the economic impact of unforeseen natural events. Indeed, the contrast with the Sulawesi earthquake shows insurance failings on a global inequality level. If insurance and the sector is not performing its economic purpose, then it simply is a rent taker and a drag on economic development.

After that last sentiment, it may therefore seem strange for me to spend the rest of this blog highlighting a potential underestimating of risk premia for improbable events when a string of events has been artfully dodged by the sector (hey, I am guilty of many inconsistencies)!

As outlined in this recent post, the insurance sector is grappling with the effect of new capital dampening pricing after the 2017 losses, directly flattening the insurance cycle. It can be argued that this new source of low-cost capital is having a positive impact on insurance availability and could be the answer to protection gap issues, such as those outlined above. And that may be true, although under-priced risk premia have a way of coming home to roost with serious longer-term effects.

The objective of most business models in the financial services sector is to maximise the risk adjusted returns from a selected portfolio, whether that be stocks or bonds for asset managers, credit risks for banks or insurance risks for insurers. Many of these firms have many thousands of potential risks to select from and so the skill or alpha that each claim derives from their ability to select risks and to build a robust portfolio. If for example, a manager wants to build a portfolio of 20 risks from a possible 100 risks, the combinations are 536 trillion (with 18 zeros as per the British definition)! And that doesn’t consider the sizing of each of the 20 positions in the portfolio. It’s no wonder that the financial sector is embracing artificial intelligence (AI) as a tool to assist firms in optimizing portfolios and potential risk weighted returns (here and here are interesting recent articles from the asset management and reinsurance sectors). I have little doubt that AI and machine learning will be a core technique in any portfolio optimisation process of the future.

I decided to look at the mechanics behind the ILS fund sector again (previous posts on the topic include this post and this old post). I constructed an “average” portfolio that broadly reflects current market conditions. It’s important to stress that there is a whole variety of portfolios that can be constructed from the relatively small number of available ILS assets out there. Some are pure natural catastrophe only, some are focused at the high excess level only, the vintage and risk profile of the assets of many will reflect the length of time they have been in business, many consist of an increasing number of private negotiated deals. As a result, the risk-return profiles of many ILS portfolios will dramatically differ from the “average”. This exercise is simply to highlight the impact of the change of several variables on an assumed, albeit imperfect, sample portfolio. The profile of my “average” sample portfolio is shown below, by exposure, expected loss and pricing.

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The weighted average expected loss of the portfolio is 2.5% versus the aggregate coupon of 5%. It’s important to highlight that the expected loss of a portfolio of low probability events can be misleading and is often misunderstood. Its not the loss expected but simply the average over all simulations. The likelihood of there being any losses is low, by definition, and in the clear majority of cases losses are small.

To illustrate the point, using my assumed loss exceedance curves for each exposure, with no correlation between the exposures except for the multi-peril coverage within each region, I looked at the distribution of losses over net premium, as below. Net premium is the aggregate coupon received less a management fee. The management fee is on assets under management and is assumed to be 1.5% for the sample portfolio, resulting in a net premium of 3.5% in the base scenario. I also looked at the impact of price increases and decreases averaging approximate +/-20% across the portfolio, resulting in net premium of 4.5% and 2.5% respectively. I guesstimate that the +20% scenario is roughly where an “average” ILS portfolio was 5 years ago.

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I have no doubt that the experts in the field would quibble with my model assumptions as they are crude. However, experience has thought me that over-modelling can lead to false sense of security and an over optimistic benefit for diversification (which is my concern about the ILS sector in general). My distributions are based upon 250,000 simulations. Others will point out that I haven’t considered the return on invested collateral assets. I would counter this with my belief that investors should only consider insurance risk premium when considering ILS investments as the return on collateral assets is a return they could make without taking any insurance risk.

My analysis shows that currently investors should only make a loss on this “average” portfolio once every 4 years (i.e. 25% of the time). Back 5 years ago, I estimate that probability at approximately 17% or roughly once every 6 years. If pricing deteriorates further, to the point where net premium is equal to the aggregate expected loss on the portfolio, that probability increases to 36% or roughly once every 3 years

The statistics on the tail show that in the base scenario of a net premium of 3.5% the 1 in 500-year aggregate loss on the portfolio is 430% of net premium compared to 340% for a net premium of 4.5% and 600% for a net premium of 2.5%. At an extreme level of a 1 in 10,000-year aggregate loss to the portfolio is 600% of net premium compared to 480% for a net premium of 4.5% and 800% for a net premium of 2.5%.

If I further assume a pure property catastrophe reinsurer (of which there are none left) had to hold capital sufficient to cover a 1 in 10,000-year loss to compete with a fully collaterised ILS player, then the 600% of net premium equates to collateral of 21%. Using reverse engineering, it could therefore be said that ILS capital providers must have diversification benefits (assuming they do collaterise at 100% rather than use leverage or hedge with other ILS providers or reinsurers) of approximately 80% on their capital to be able to compete with pure property catastrophe reinsurers. That is a significant level of diversification ILS capital providers are assuming for this “non-correlating asset class”. By the way, a more likely level of capital for a pure property catastrophe reinsurer would be 1 in 500 which means the ILS investor is likely assuming diversification benefits of more that 85%. Assuming a mega-catastrophic event or string of large events only requires marginal capital of 15% or less with other economic-driven assets may be seen to be optimistic in the future in my view (although I hope the scenario will never be illustrated in real life!).

Finally, given the pressure management fees are under in the ILS sector (as per this post), I thought it would be interesting to look at the base scenario of an aggregate coupon of 5% with different management fee levels, as below. As you would expect, the portfolio risk profile improves as the level of management fees decrease.

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Given the ongoing pressure on insurance risk premia, it is likely that pressure on fees and other expenses will intensify and the use of machines and IA in portfolio construction will increase. The commodification of insurance risks looks set to expand and increase, all driven by an over-optimistic view of diversification within the insurance class and between other asset classes. But then again, that may just lead to the more wide-spread availability of insurance in catastrophe exposed regions. Maybe one day, even in places like Sulawesi.

Befuddled Lloyd’s

Lloyd’s of London always provides a fascinating insight into the London insurance market and beyond into the global specialty insurance market, as this previous post shows. It’s Chairman, Bruce Carnegie-Brown, commented in their 2017 annual report that he expects “2018 to be another challenging year for Lloyd’s and the Corporation continues to refine its strategy to address evolving market conditions”. Given the bulking up of many of its competitors through M&A, Willis recently called it a reinvigoration of the “big balance sheet” reinsurance model, Lloyd’s needs to get busy sharpening its competitive edge. In a blunter message Brown stressed that “the market’s 2017 results are proof, if any were needed, that business as usual is not sustainable”.

A looked at the past 15 years of underwriting results gives an indicator of current market trends since the underwriting quality control unit, called the Franchise Board, was introduced at the end of 2002 after the disastrous 1990’s for the 330-year-old institution.

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The trend of increasing non-CAT loss ratios after years of soft pricing coupled with declining prior year reserve releases is clear to see. That increases the pressure on the insurance sector to control expenses. To that end, Inga Beale, Lloyd’s CEO, is pushing modernisation via the London Market Target Operating Model programme hard, stating that electronic placement will be mandated, on a phased basis, “to speed up the adoption of the market’s modernisation programme, which will digitise processes, reduce unsustainable expense ratios, and make Lloyd’s more attractive to do business with”.

The need to reduce expenses in Lloyd’s is acute given its expense ratio is around 40% compared to around 30% for most of its competitors. Management at Lloyd’s promised to “make it cheaper and easier to write business at Lloyd’s, enabling profitable growth”. Although Lloyd’s has doubled its gross premium volumes over the past 15 years, the results over varying timeframes below, particularly the reducing underwriting margins, show the importance of stressing profitable growth and expense efficiencies for the future.

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A peer comparison of Lloyd’s results over the past 15 years illustrates further the need for the market to modernise, as below. Although the 2017 combined ratio for some of the peer groupings have yet to finalised and published (I will update the graph when they do so), the comparison indicates that Lloyd’s has been doing worse than its reinsurance and Bermudian peers in recent years. It is suspicious to see, along with the big reinsurers and Bermudians, Lloyd’s included Allianz, CNA, and Zurich (and excluded Mapfe) in their competitor group from 2017. If you can’t meet your target, just change the metric behind the target!

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A recent report from Aon Benfield shows the breakdown of the combined ratio for their peer portfolio of specialist insurers and reinsurers from 2006 to 2017, as below.

click to enlargeAon Benfield Aggregate Combined Ratio 2006 to 2017

So, besides strong competitors, increasing loss ratios and heavy expense loads, what does Lloyd’s have to worry about? Well, in common with many, Lloyd’s must contend with structural changes across the industry as a result of, in what Willis calls in their latest report, “the oversupply of capital” from investors in insurance linked securities (ILS) with a lower cost of capital, whereby the 2017 insured losses appears to have had “no impact upon appetite”, according to Willis.

I have posted many times, most recently here, on the impact ILS has had on property catastrophe pricing. The graph of the average multiple of coupon to expected loss on deals monitored by sector expert Artemis again illustrates the pricing trend. I have come up with another angle to tell the story, as per the graph below. I compared the Guy Carpenter rate on line (ROL) index for each year against an index of the annual change in the rolling 10-year average global catastrophe insured loss (which now stands at $66 billion for 2008-2017). Although it is somewhat unfair to compare a relative measure (the GC ROL index) against an absolute measure (change in average insured loss), it makes a point about the downward trend in property catastrophe reinsurance pricing in recent years, particularly when compared to the trend in catastrophic losses. To add potentially to the unfairness, I also included the rising volumes in the ILS sector, in an unsubtle finger point.

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Hilary Weaver, Lloyd’s CRO, recognises the danger and recently commented that “the new UK ILS regulation will, if anything, increase the already abundant supply of insurance capital” and “this is likely to mean that prices remain low for many risks, so we need to remain vigilant to ensure that the prices charged for them are proportionate to the risk”.

The impact extends beyond soft pricing and could impact Lloyd’s risk profile. The loss of high margin (albeit not as high as it once was) and low frequency/high severity business means that Lloyd’s will have to fish in an already crowded pond for less profitable and less volatile business. The combined ratios of Lloyd’s main business lines are shown below illustrating that all, except casualty, have had a rough 2017 amid competitive pressures and large losses.

As reinsurance business is commoditised further by ILS, in a prelude to an increase in machine/algorithm underwriting, Lloyd’s business will become less volatile and as a result less profitable. To illustrate, the lower graph below shows Lloyd’s historical weighted average combined ratio, using the 2017 business mix, versus the weighted average combined ratio excluding the reinsurance line. For 2003 to 2017, the result would be an increase in average combined ratio, from 95.8% to 96.5%, and a reduction in volatility, the standard deviation from 9.7% to 7%.

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To write off Lloyd’s however would be a big mistake. In my view, there remains an important role for a specialist marketplace for heterogeneous risks, where diverse underwriting expertise cannot be easily replicated by machines. Lloyd’s has shown its ability in the past to evolve and adapt, unfortunately however usually when it doesn’t have any choice. Hopefully, this legendary 330-year-old institution will get ahead of the game and dictate its own future. It will be interesting to watch.

 

Epilogue – Although this analogy has limitations, it occurs to me that the insurance sector is at a stage of evolution that the betting sector was at about a decade ago (my latest post on the sector is here). Traditional insurers, with over-sized expenses, operate like old traditional betting shops with paper slips and manual operations. The onset of online betting fundamentally changed the way business is transacted and, as a result, the structure of the industry. The upcoming digitalisation of the traditional insurance business will radically change the cost structure of the industry. Lloyd’s should look to the example of Betfair (see an old post on Betfair for more) as a means of digitalising the market platform and radically reducing costs.

Follow-on 28th April – Many thanks to Adam at InsuranceLinked for re-posting this post. A big welcome to new readers, I hope you will stick around and check out some other posts from this blog. I just came across this report from Oliver Wyam on the underwriter of the future that’s worth a read. They state that the “commercial and wholesale insurance marketplaces are undergoing radical change” and they “expect that today’s low-price environment will continue for the foreseeable future, continuing to put major pressure on cost“.

CAT Calls

Following on from a recent post on windstorms in the US, I have taken several loss preliminary estimates recently published by firms (and these are very early estimates and therefore subject to change) and overlaid them against the South-East US probable maximum loss (PML) curves and Atlantic hurricane scenarios previously presented, as below. The range of insured losses for Harvey, Irma and Maria (now referred to as HIM) are from $70 billion to $115 billion, averaging around $90 billion.

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The loss estimates by firm depend heavily upon the risk profile of each. As a generalisation, it could be said that the aggregate US wind losses are averaging around the 1 in 100 loss level.

Given there was over $20 billion of insured losses from H1 and factoring in developing losses such as the Mexico earthquake, the California wildfires and the current windstorm Ophelia hitting Ireland, annual insured losses for 2017 could easily reach $120 billion. The graph below shows the 2016 estimates from Swiss Re and my $120 billion 2017 guesstimate (it goes without saying that much could still happen for the remainder of the year).

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At a $120 billion level of insured loss for 2017, the 10 year average increase from around $55 billion to $65 billion. In a post in early 2016, I estimated that catastrophe pricing was about 25% too low based upon annual average losses of $40 billion per year. We will see whether the 2017 losses are enough to deplete the overcapitalisation in the market and return pricing towards their technical rate. I wouldn’t hold my breath on that as although there may be material aggregate losses in the private collateralised market and other pockets of the retrocession market, the appetite of yield seeking investors will likely remain unabated in the current interest rate environment.

Although the comparison between calendar year ratios and credit defaults is fraught with credibility issues (developed accident year ratios to developed default rates are arguably more comparable), I updated my previous underwriting cycle analysis (here in 2014 and here in 2013). Taking the calendar year net loss ratios of Munich Re and Lloyds of London excluding catastrophe and large losses (H1 results for 2017), I then applied a crude discount measure using historical risk-free rates plus 100 basis points to reflect the time value of money, and called the resulting metric the adjusted loss ratio (adjusted LR). I compared these adjusted LRs for Munich and Lloyds to S&P global bond credit default rates (by year of origin), as per the graph below.

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This shows that the years of relatively benign attritional claims together with the compounding impact of soft pricing over the past years may finally be coming to an end. Time will tell. All in all, it makes for a very interesting period for the market over the next 6 to 12 months.

In the interim, let’s hope for minimal human damage from the current California wildfires and windstorm Ophelia.

The Big Wind

With four US hurricanes and one earthquake in current times, mother nature is reminding us homo-sapiens of her power and her unpredictability. As the massive Hurricane Irma is about to hit Florida, we all hope that the loss of life and damage to people’s lives will be minimal and that the coming days will prove humane. Forgive me if it comes across as insensitive to be posting now on the likely impact of such events on the insurance industry.

For the insurance sector, these events, and particularly Hurricane Irma which is now forecast to move up the west coast of Florida at strength (rather the more destruction path of up the middle of Florida given the maximum forces at the top right-hand side of a hurricane like this one), may be a test on the predictive powers of its models which are so critical to pricing, particularly in the insurance linked securities (ILS) market.

Many commentators, including me (here, here and here are recent examples), have expressed worries in recent years about current market conditions in the specialty insurance, reinsurance and ILS sectors. On Wednesday, Willis Re reported that they estimate their subset of firms analysed are only earning a 3.7% ROE if losses are normalised and reserve releases dried up. David Rule of the Prudential Regulatory Authority in the UK recently stated that London market insurers “appear to be incorporating a more benign view of future losses into their technical pricing”, terms and conditions continued to loosen, reliance on untested new coverages such as cyber insurance is increasing and that insurers “may be too sanguine about catastrophe risks, such as significant weather events”.

With the reinsurance and specialty insurance sectors struggling to meet their cost of capital and pricing terms and conditions being so weak for so long (see this post on the impact of soft pricing on risk profiles), if Hurricane Irma impacts Florida as predicted (i.e. on Saturday) it has the potential to be a capital event for the catastrophe insurance sector rather than just an earnings event. On Friday, Lex in the FT reported that the South-East US makes up 60% of the exposures of the catastrophe insurance market.

The models utilised in the sector are more variable in their output as events get bigger in their impact (e.g. the higher the return period). A 2013 post on the variation in loss estimates from a selected portfolio of standard insurance coverage by the Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) illustrates the point and one of the graphs from that post is reproduced below.

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Based upon the most recent South-East US probable maximum losses (PML) and Atlantic hurricane scenarios from a group of 12 specialty insurers and reinsurers I selected, the graph below shows the net losses by return periods as a percentage of each firm’s net tangible assets. This graph does not consider the impact of hybrid or subordinate debt that may absorb losses before the firm’s capital. I have extrapolated many of these curves based upon industry data on US South-East exceedance curves and judgement on firm’s exposures (and for that reason I anonymised the firms).

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The results of my analysis confirm that specialty insurers and reinsurers, in aggregate, have reduced their South-East US exposures in recent years when I compare average figures to S&P 2014 data (by about 15% for the 1 in 100 return period). Expressed as a net loss ratio, the average for a 1 in 100  and a 1 in 250 return period respectively is 15% and 22%. These figures do look low for events with characteristics of these return periods (the average net loss ratio of the 12 firms from catastrophic events in 2005 and 2011 was 22% and 25% respectively) so it will be fascinating to see what the actual figures are, depending upon how Hurricane Irma pans out. Many firms are utilising their experience and risk management prowess to transfer risks through collaterised reinsurance and retrocession (i.e. reinsurance of reinsurers) to naïve capital market ILS investors.

If the models are correct and maximum losses are around the 1 in 100 return period estimates for Hurricane Irma, well capitalized and managed catastrophe exposed insurers should trade through recent and current events. We will see if the models pass this test. For example, demand surge (whereby labour and building costs increase following a catastrophic event due to overwhelming demand and fixed supply) is a common feature of widespread windstorm damage and is a feature in models (it is one of those inputs that underwriters can play with in soft markets!). Well here’s a thought – could Trump’s immigration policy be a factor in the level of demand surge in Florida and Texas?

The ILS sector is another matter however in my view due to the rapid growth of the private and unregulated collateralised reinsurance and retrocession markets to satisfy the demand for product supply from ILS funds and yield seeking investors. The prevalence of aggregate covers and increased expected loss attachments in the private ILS market resembles features of previous soft and overheated retrocession markets (generally before a crash) in bygone years. I have expressed my concerns on this market many times (more recently here). Hurricane Irma has the potential to really test underwriting standards across the ILS sector. The graph below from Lane Financial LLC on the historical pricing of US military insurer USAA’s senior catastrophe bonds again illustrates how the market has taken on more risk for less risk adjusted premium (exposures include retired military personnel living in Florida).

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The events in the coming days may tell us, to paraphrase Mr Buffet, who has been swimming naked or as Lex put it on Friday, “this weekend may be a moment when the search for uncorrelated returns bumps hard into acts of God”.

Hopefully, all parts of the catastrophe insurance sector will prove their worth by speedily indemnifying peoples’ material losses (nothing can indemnify the loss of life). After all, that’s its function and economic utility to society. Longer term, recent events may also lead to more debate and real action been taken to ensure that the insurance sector, in all its guises, can have an increased economic function and relevance in an increasingly uncertain world, in insuring perils such as floods for example (and avoiding the ridiculous political interference in risk transfer markets that has made the financial impact of flooding from Hurricane Harvey in Texas so severe).

Notwithstanding the insurance sector, our thoughts must be with the people who will suffer from nature’s recent wrath and our prayers are with all of those negatively affected now and in the future.

ILS illuminations

Insurance linked securities (ILS) are now well established in the insurance industry. ILS as an asset class offer, according to its many fans, the benefits of diversification and low correlation to other asset classes whilst offering a stable and attractive risk/reward return. The impact of the new capital on the traditional market has been profound and wide ranging (and a much posted upon topic in this blog – here, here & here for example).

ILS fund managers maintained an “aggressive posture” on price at the recent April renewals according to Willis Re as ILS capacity continues to demonstrate its cost of capital advantage. ILS fund managers are also looking to diversify, moving beyond pure short tail risks and looking at new previously uninsured or underinsured exposures, as well as looking to move their capital along the value-chain by sourcing primary risk more directly and in bulk.

An industry stalwart, John Kavanagh of Willis Re, commented that “with results on many diversifying non-catastrophe classes now marginal, there is greater pressure on reinsurers to address the pricing in these classes” and that “many reinsurers remain prepared to let their top line revenue growth stall and are opting to return excess capital to their shareholders”. The softening reinsurance market cycle is now in its fifth year and S&P estimates that “even assuming continued favourable prior-year reserve releases and benign natural catastrophe losses, we anticipate that reinsurers will barely cover their cost of capital over the next two years”.

Rather than fight the new capital on price, some traditional (re)insurers are, according to Brandan Holmes of Moody’s, “deploying third-party capital in their own capital structures in an effort to lower their blended cost of capital” and are deriving, according to Aon Benfield, “significant benefits from their ability to leverage alternative capital”. One can only fight cheap capital for so long, at some stage you just arbitrage against it (sound familiar!).

A.M. Best recently stated that “more collateralised reinsurance programs covering nonpeak exposures are ceded to the capital markets”. The precipitous growth in the private transacted collateralised reinsurance subsector can be seen in the graph from Aon below.

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Nick Frankland of Guy Carpenter commented that “the capital landscape is ever-changing” and that “such capital diversity also elevates the position of the broker”. Some argue that the all-powerful role of the dominant brokers is exacerbating market softness. These brokers would counterargue that they are simply fulfilling their role in an efficient market, matching buyers and sellers. As Frankland puts it, brokers are “in the strongest position to provide access to all forms of capital and so secure the more beneficial rates and terms and conditions”. Dominic Christian of Aon Benfield commented last year that “to some extent alternative sources of capital are already, and have already uberized insurance and reinsurance, by bringing increased sources of supply”.

Perhaps alone amongst industry participants, Weston Hicks of Alleghany, has questioned the golden goose of cheap ILS capital stating that “some new business models that separate the underwriting decision from the capital provider/risk bearer are, in our view, problematic because of a misalignment of incentives”. Such concerns are batted aside as old fashioned in this new world of endless possibilities. Frighteningly, John Seo of ILS fund manager Fermat Capital, suggests that “for every dollar of money that you see in the market right now, I think there is roughly 10 dollars on the sidelines waiting to come in if the market hardens”.

As an indicator of current ILS pricing, the historical market spread over expected losses in the public CAT bond market can be seen in the exhibit below with data sourced from Lane Financial. It is interesting to note that the average expected loss is increasing indicating CAT bonds are moving down the risk towers towards more working layer coverages.

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In a previous post, I argued that returns from an ILS fund index, with the net returns judgementally adjusted to get to comparable figures gross of management fees, were diverging against those from a pure CAT bond index. I argued that this divergence may illustrate that the ILS funds with exposure to the private collateralised reinsurance sector may be taking on higher risk exposures to pump returns (or may be passing risks amongst themselves in an embryonic spiral) and that ILS investors should be careful they understand the detail behind the risk profiles of the ILS funds they invest in.

Well, the final 2016 figures, as per the graph below, show that the returns in my analysis have in fact converged rather than diverged. On the face of it, this rubbishes my argument and I have to take that criticism on. Stubbornly, I could counter-argue that the ILS data used in the comparison may not reflect the returns of ILS funds with large exposure to collateralised reinsurance deals. Absent actual catastrophic events testing the range of current fund models, better data sources are needed to argue the point further.

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In their annual review of 2016, Lane Financial have an interesting piece on reducing transparency across both the public and private ILS sector. They characterise the private collateralised reinsurance sector as akin to a dark pool compared to the public CAT bond market which they likened to a lit exchange. Decreased transparency across the ILS sector “should send up warning flags” for all market participants as it makes calculating Net Asset Valuations (NAV) with monthly or quarterly frequency more difficult. They argue that the increased use of a relatively smaller public CAT bond market for pricing points across the ILS sector, the less credible is the overall valuation. This is another way of expressing my concern that the collateralised reinsurance market could be destabilising as it is hidden (and unregulated).

In the past, as per this post, I have questioned how the fully funded ILS market can claim to have a lower cost of capital against rated reinsurers who only have to hold capital against a percentage of their exposed limit, akin to fractional banking (see this post for more on that topic). The response is always down to the uncorrelated nature of ILS to other asset classes and therefore its attraction to investors such as pension funds who can apply a low cost of capital to the investment due to its uncorrelated and diversifying portfolio benefits. Market sponsors of ILS often use graphs such as the one below from the latest Swiss Re report to extoll the benefits of the asset class.

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A similar exhibit, this time from a Lombard Odier brochure, from 2016 shows ILS in an even more favourable light!

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As anybody who has looked through any fund marketing metrics knows, performance comparisons with other investment strategies are fraught with bias and generally postdictive. The period over which the comparison is made and the exact indices chosen (look at the differing equity indices used in the comparisons above) can make material differences. Also, the size and liquidity of a market is important, a point which may negate any reliance on ILS returns prior to 2007 for example.

I thought an interesting exercise would be to compare actual historical ILS returns, as represented by the Swiss Re Global Cat Bond Total Return Index, against total returns (i.e. share price annual change plus dividends paid in year) from equity investment in reinsurers across different time periods. The most applicable business model for comparison would be pure property catastrophe reinsurers but unfortunately there are not many of them left.

I have chosen RenRe (RNR) and Validus (VR), from 2007, as representatives of the pure property cat business model, although both have diversified their portfolios away from pure short tail business in recent years. I also selected three of the biggest European reinsurers – Munch Re, Swiss Re and Hannover Re – all of which are large diverse composite reinsurers. Finally, I constructed a US$ portfolio using equal shares of each of the five firms mentioned above (RenRe represents 40% of the portfolio until 2007 when Validus went public) with the € and CHF shares converted at each year end into dollars.

The construction of any such portfolio is postdictive and likely suffers from multiple biases. Selecting successful firms like RenRe and Validus could validly be criticised under survival bias. To counter such criticism, I would point out that the inclusion of the European reinsurers is a considerable historic drag on returns given their diverse composite footprint (and associated correlation to the market) and the exclusion of any specialist CAT firm that has been bought out in recent years, generally at a good premium, also drags down returns.

The comparison over the past 15 years, see graph below, shows that Munich and Swiss struggle to get over their losses from 2002 and 2003 and during the financial crisis. Hannover is the clear winner amongst the Europeans. The strong performance of Hannover, RenRe and Validus mean that the US$ portfolio matches the CAT bond performance after the first 10 years, albeit on a more volatile basis, before moving ahead on a cumulative basis in the last 5 years. The 15-year cumulative return is 217% for the CAT bond index and 377% return for the US$ equity portfolio.

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The comparison over the past 10 years, see graph below, is intriguing. Except for Validus, the CAT bond index beats all other firms and the US$ portfolio for non-volatile returns hands down in the first 5 years. Hannover, Validus and the portfolio each make a strong comeback in the most recent 5 years. The 10-year cumulative return is 125% for the CAT bond index and 189% return for the US$ equity portfolio.

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The comparison over the past 5 years, see graph below, shows that all firms and the portfolio handily beats the CAT bond index. Due to an absence in large loss activity over the recent past and much more shareholder friendly actions by all reinsurers, the equity returns have been steady and non-volatile. The 5-year cumulative return is 46% for the CAT bond index and 122% return for the US$ equity portfolio.

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Overall then, ILS may offer less volatile and uncorrelated returns but I would personally prefer the, often lumpier, historical equity returns from a selected portfolio of top reinsurers in my pension pot (we all could have both in our pension funds!). Then again, the influx of new capital into ILS has put the future viability of the traditional reinsurance business models into question so future equity returns from the sector may not be too rosy.

At the end of the day, the bottom line is whether current market risk premia is adequate, irrespective of being supplied by ILS fund managers or traditional reinsurers. Based upon what I see, I have grave misgivings about current market pricing and therefore have no financial exposure, ILS or equity or otherwise, to the market at present.

Additional Comment, 29th April 2017: The ILS website Artemis.bm had an interesting piece on comments from Torsten Jeworrek of Munich Re during their March conference call. The applicable comment is as follows:

“And now I give you another example, which is not innovation per se or not digitalization, but you know that more and more alternative capital came into the insurance industry over the last years; hedge funds, pension refunds, participating particularly in the cat business and as a trend that not all of the limits they provide, cat limits are fully collateralized anymore. That means there are 10 scenarios; hurricane, earthquake, [indiscernible], and so on; which are put together, but not 10 times the limit is collateralized, let’s say only 4 times, 5 times.

That means these hedge funds and pension funds so to speak in the future if they don’t have to provide full 100% collateralized for all the limits they provide, they need a certain credit risk for the buyer. The more they entertain, the more there’s a likelihood that this reinsurance can also fail. The question is how far will that go and this kind of not fully collateralized reinsurance, will that be then accepted as a reinsurance by the regulator or will that be penalized at a certain time otherwise we don’t have level playing field anymore, which means the traditional reinsurer who was strongly monitored and regulated and also reported as really expensive and a burden for our industry and for us and on the other hand, you have very lean pension and hedge funds who even don’t have to provide the same amount of capital for the same risk.”