Tag Archives: reinsurance 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.

The New Normal (Again)

I expect that next week’s reinsurance jamboree in Monte Carlo will be full of talk of innovative and technology streaming-lining business models (as per this post on AI and insurance). This recent article from the FT is just one example of claims that technology like blockchain can reduce costs by 30%. The article highlights questions about whether insurers are prepared to give up ownership of data, arguably their competitive advantage, if the technology is really to be scaled up in the sector.

As a reminder of the reinsurance sector’s cost issues, as per this post on Lloyds’, the graph below illustrates the trend across Lloyds’, the Aon Benfield Aggregate portfolio, and Munich’s P&C reinsurance business.

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Until the sector gets serious about cutting costs, such as overpaid executives on luxury islands or expensive cities and antiquated business practises such as holding get togethers in places like Monte Carlo, I suspect expenses will remain an issue. In their July review, Willis stated that a “number of traditional carriers are well advanced in their plans to reduce their costs, including difficult decisions around headcount” and that “in addition to cost savings, the more proactively managed carriers are applying far greater rigor in examining the profitability of every line of business they are accepting”. Willis highlighted the potential difficulties for the vastly inefficient MGA business that many have been so actively pursuing. As an example of the type of guff executives will trot out next week, Swiss Re CEO, Christian Mumenthaler, said “we remain convinced that technology will fundamentally change the re/insurance value chain”, likely speaking from some flash office block in one of the most expensive cities in the world!

On market conditions, there was positive developments on reinsurance pricing at the January renewals after the 2017 losses with underlying insurance rates improving, as illustrated by the Marsh composite commercial rate index (example from US below).

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However, commentators have been getting ever more pessimistic as the year progresses, particularly after the mid-year renewals. Deutsche Bank recently called the reinsurance pricing outlook “very bleak”. A.M. Best stated that “the new normal for reinsurers appears to be one with returns that are less impressive and underwriting and fee income becoming a larger contributor to profits” and predicts, assuming a normal large loss level, an 8% ROE for 2018 for the sector. Willis, in their H1 report, puts the sectors ROE at 7.7% for H1 2018. S&P, in the latest report that is part of their Global Highlights series, also expects a ROE return for 2018 around 6% to 8% and estimates that “reinsurers are likely to barely cover their cost of capital in 2018 and 2019”.

S&P does question why “the market values the industry at a premium to book value today (on average at 1.24x at year-end 2017), and at near historical highs, given the challenges” and believes that potential capital returns, M&A and interest rate rises are all behind elevated valuations.  The recent Apollo PE deal for Aspen at 1.12 times book seems a large way off other recent multiples, as per this post, but Aspen has had performance issues. Still its interesting that no other insurer was tempted to have a go at Aspen with the obvious synergies that such a deal could have achieved. There is only a relatively small number of high quality players left for the M&A game and they will not be cheap!

As you are likely aware, I have been vocal on the impact the ILS sector has had in recent years (most recently here and here). With so-called alternative capital (at what size does it stop being alternative!) now at the $95 billion-mark according to Aon, A.M. Best makes the obvious point that “any hope for near-term improvement in the market is directly correlated to the current level of excess capacity in the overall market today, which is being compounded by the continued inflow of alternative capacity”. Insurers and reinsurers are not only increasing their usage of ILS in portfolio optimisation but are also heavily participating in the sector. The recent purchase by Markel of the industry leading and oldest ILS fund Nephila is an interesting development as Markel already had an ILS platform and is generally not prone to overpaying.

I did find this comment from Bob Swarup of Camdor in a recent Clear Path report on ILS particularly telling – “As an asset class matures it inevitably creates its own cycle and beta. At this point you expect fees to decline both as a function of the benefits of scale but also as it becomes more understood, less of it becomes alpha and more of it becomes beta” and “I do feel that the fees are most definitely too high right now and to a large extent this is because people are trying to treat this as an alternative asset class whereas it is large enough now to be part of the general mix”. Given the still relatively small size of the ILS sector, it’s difficult for ILS managers to demonstrate true alpha at scale (unless they are taking crazy leveraged bets!) and therefore pressure on current fees will become a feature.

A.M. Best articulated my views on ILS succinctly as follows: “The uncorrelated nature of the industry to traditional investments does appear to have value—so long as the overall risk-adjusted return remains appropriate”. The graph below from artemis.bm shows the latest differential between returns and expected cost across the portfolio they monitor.

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In terms of the returns from ILS funds, the graph below shows the underlying trend (with 2018 results assuming no abnormal catastrophic activity) of insurance only returns from indices calculated by Lane Financial (here) and Eurekahedge (here). Are recent 5 year average returns of between 500 and 250 basis points excess risk free enough to compensation for the risk of a relatively concentrated portfolio? Some think so. I don’t.

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Whether reinsurers and specialty insurers will be able to maintain superior (albeit just above CoC) recent returns over ILS, as illustrated in this post, through arbitrating lower return ILS capital or whether their bloated costs structures will catch them out will be a fascinating game to watch over the coming years. I found a section of a recent S&P report, part of their Global Highlights series, on cat exposures in the sector, amusing. It stated that in 2017 “the reinsurance industry recorded an aggregate loss that was assessed as likely to be incurred less than once in 20 years” whilst “this was the third time this had happened in less than 20 years“.

So, all in all, the story is depressingly familiar for the sector. The new normal, as so many commentators have recently called it, amounts to overcapacity, weak pricing power, bloated cost structures, and optimistic valuations. Let’s see if anybody has anything new or interesting to say in Monte Carlo next week.

As always, let’s hope there is minimal human damage from any hurricanes such as the developing Hurricane Florence or other catastrophic events in 2018.

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“.

Insurance M&A Pickup

It’s been a while since I posted on the specialty insurance sector and I hope to post some more detailed thoughts and analysis when I get the time in the coming months. M&A activity has picked up recently with the XL/AXA and AIG/Validus deals being the latest examples of big insurers bulking up through M&A. Deloitte has an interesting report out on some of the factors behind the increased activity. The graph below shows the trend of the average price to book M&A multiples for P&C insurers.

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As regular readers will know, my preferred metric is price to tangible book value and the exhibit below shows that the multiples on recent deals are increasing and well above the standard multiple around 1.5X. That said, the prices are not as high as the silly prices of above 2X paid by Japanese insurers in 2015. Not yet anyway!

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Unless there are major synergies, either on the operating side or on the capital side (which seems to be AXA’s justification for the near 2X multiple on the XL deal), I just can’t see how a 2X multiple is justified in a mature sector. Assuming these firms can earn a 10% return on tangible assets over multiple cycles, a 2X multiple equates to 20X earnings!

Time will tell who the next M&A target will be….

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.