Tag Archives: Renaissance Re

Thoughts on ILS Pricing

Valuations in the specialty insurance and reinsurance sector have been given a bump up with all of the M&A activity and the on-going speculation about who will be next. The Artemis website reported this week that Deutsche Bank believe the market is not differentiating enough between firms and that even with a lower cost of capital some are over-valued, particularly when lower market prices and the relaxation in terms and conditions are taken into account. Although subject to hyperbole, industry veteran John Charman now running Endurance, stated in a recent interview that market conditions in reinsurance are the most “brutal” he has seen in his 44 year career.

One interesting development is the re-emergence of Richard Brindle with a new hybrid hedge fund type $2 billion firm, as per this Bloomberg article. Given the money Brindle made out of Lancashire, I am surprised that he is coming back with a business plan that looks more like a jump onto the convergence hedge fund reinsurer band wagon than anything more substantive given current market conditions. Maybe he has nothing to lose and is bored! It will be interesting to see how that one develops.

There have been noises coming out of the market that insurance linked securities (ILS) pricing has reached a floor. Given that the Florida wind exposure is ground zero for the ILS market, I had a look through some of the deals on the Artemis website, to see what pricing was like. The graph below does only have a small number of data points covering different deal structures so any conclusions have to be tempered. Nonetheless, it does suggest that rate reductions are at least slowing in 2015.

click to enlargeFlorida ILS Pricing

Any review of ILS pricing, particularly for US wind perils, should be seen in the context of a run of low storm recent activity in the US for category 3 or above. In their Q3-2014 call, Renaissance Re commented (as Eddie pointed out in the comments to this post) that the probability of a category 3 or above not making landfall in the past 9 years is statistically at a level below 1%. The graph below shows some wind and earthquake pricing by vintage (the quake deals tend to be the lower priced ones).

click to enlargeWind & Quake ILS Pricing by year

This graph does suggest that a floor has been reached but doesn’t exactly inspire any massive confidence that pricing in recent deals is any more adequate than that achieved in 2014.

From looking through the statistics on the Artemis website, I thought that a comparison to corporate bond spreads would be interesting. In general (and again generalities temper the validity of conclusions), ILS public catastrophe bonds are rated around BB so I compared the historical spreads of BB corporate against the average ILS spreads, as per the graph below.

click to enlargeILS Spreads vrs BB Corporate Spread

The graph shows that the spreads are moving in the same direction in the current environment. Of course, it’s important to remember that the price of risk is cheap across many asset classes as a direct result of the current monetary policy across the developed world of stimulating economic activity through encouraging risk taking.

Comparing spreads in themselves has its limitation as the underlying exposure in the deals is also changing. Artemis uses a metric for ILS that divides the spread by the expected loss, referred to herein as the ILS multiple. The expected loss in ILS deals is based upon the catastrophe modeller’s catalogue of hurricane and earthquake events which are closely aligned to the historical data of known events. To get a similar statistic to the ILS multiple for corporate bonds, I divided the BB spreads by the 20 year average of historical default rates from 1995 to 2014 for BB corporate risks. The historical multiples are in the graph below.

click to enlargeILS vrs BB Corporate Multiples

Accepting that any conclusions from the graph above needs to consider the assumptions made and their limitations, the trends in multiples suggests that investors risk appetite in the ILS space is now more aggressive than that in the corporate bond space. Now that’s a frightening thought.

Cheap risk premia never ends well and no fancy new hybrid business model can get around that reality.

Follow-up: Lane Financial LLC has a sector report out with some interesting statistics. One comment that catch my eye is that they estimate a well spread portfolio by a property catastrophic reinsurer who holds capital at a 1-in-100 and a 1-in-250 level would only achieve a ROE of 8% and 6.8% respectively at todays ILS prices compared to a ROE of 18% and 13.3% in 2012. They question “the sustainability of the independent catastrophe reinsurer” in this pricing environment and offer it as an explanation “why we have begun to see mergers and acquisitions, not between two pure catastrophe reinsurers but with cat writers partnering with multi-lines writers“.

Uncorrelated CaT capital “is the cheapest”

One of the reasons given by market participants for competitive pricing in the ILS markets is the lower cost of capital required by such instruments due to the uncorrelated nature of the underlying exposure with other classes. I previously posted on the lower risk return for an ILS fully collaterised portfolio against a similar portfolio written by a mono-line property catastrophe reinsurer. The ILS investor may be prepared to accept a lower return due to the uncorrelated nature of the exposure. It is nonetheless resulting in lower prices for risk which has always ended badly in the past.

Twelve Capital are a well known ILS investment manager and recently published a white paper on the impact of ILS capital on the reinsurance industry. I liked the way they described the lower cost of capital issue, as below:

“Equity is the most expensive form of capital for the (re)insurance industry. Thanks to its diversification benefits, ILS is the cheapest. The most popular form of investment for those looking to enter the reinsurance market was, prior to the birth of ILS, equity offered by traditional reinsurers. However, returns on equity are eroded by company management costs and the tendency of reinsurers to diversify into less profitable lines of business. In addition, financial market investments on the asset side of the balance sheet expose reinsurance shareholders to additional financial market risks. A listed reinsurance stock thus has the disadvantage of being highly correlated to equity markets in general.

So, what ought to be a fundamentally uncorrelated investment gets transformed into a correlated investment, and the diversification benefit is lost. The investor is also exposed to the risk that the management of reinsurance companies might not always act in the best interests of shareholders.

As insurance investors focus on those lines of business that are favourably priced and soundly modelled, reinsurance companies might end up losing their most profitable lines to the ILS market. And it is this source of profit that reinsurers have traditionally relied upon to support and cross-subsidise substantial volumes of business that generally only break even. With profitable lines taken away by more efficient investors, reinsurance companies are left with business models that cannot sustain conventional cross-subsidisation.”

The comment on reinsurer’s management is a bit below the belt! The impact of the loss of the low frequency/high severity business to the traditional market is a valid one though. However, the long histories of the largest tier 1 reinsurers with large diverse portfolios and the ability to provide products and services across most business lines and jurisdictions indicate more robust business models than the commentary suggests in my opinion.

My previous post looked at the capital return of a fully collaterised provider such as an ILS fund against a mono-line catastrophe provider such as a property cat reinsurer. To see if the commentary above on a correlated investment is reflective of actual experience, the graph below shows the S&P500 against the share prices of the property catastrophe reinsurers Renaissance Re, Validus Re, Montpelier Re and Platinum Re since late 2002. Excluding Montpelier Re, which obviously had some company specific issues after the 2005 wind losses, the R2 for the other firms is remarkably similar around 65%. This suggests investing in the equity of these firms has indeed been a correlated investment in the past.

click to enlargePropCaT Reinsurers correlated to SP500

It emphasises that the traditional reinsurance market needs to focus on reducing such correlation, whether real or wrongly perceived, to compete better for this cheap capital.

Assessing reinsurers’ catastrophe PMLs

Prior to the recent market wobbles on what a post QE world will look like, a number of reinsurers with relatively high property catastrophe exposures have suffered pullbacks in their stock due to fears about catastrophe pricing pressures (subject of previous post). Credit Suisse downgraded Validus recently stating that “reinsurance has become more of a commodity due to lower barriers to entry and vendor models.”

As we head deeper into the US hurricane season, it is worth reviewing the disclosures of a number of reinsurers in relation to catastrophe exposures, specifically their probable maximum losses or PMLs . In 2012 S&P’s influential annual publication – Global Reinsurance Highlights – there is an interesting article called “Just How Much Capital Is At Risk”. The article looked at net PMLs as a percentage of total adjusted capital (TAC), an S&P determined calculation, and also examined relative tail heaviness of PMLs disclosed by different companies. The article concluded that “by focusing on tail heaviness, we may have one additional tool to uncover which reinsurers could be most affected by such an event”. In other words, not only is the amount of the PMLs for different perils important but the shape of the curve across different return periods (e.g. 1 in 50 years, 1 in 100 years, 1 in 250 years, etc.) is also an important indicator of relative exposures. The graphs below show the net PMLs as a percentage of TAC and the net PMLs as a percentage of aggregate limits for the S&P sample of insurers and reinsurers.

click to enlarge

PML as % of S&P capital

PML as % of aggregate limit

Given the uncertainties around reported PMLs discussed in this post, I particularly like seeing PMLs as a percentage of aggregate limits. In the days before the now common use of catastrophic models (by such vendor firms as RMS, AIR and Eqecat), underwriters would subjectively calculate their PMLs as a percentage of their maximum possible loss or MPL (in the past when unlimited coverage was more common an estimate of the maximum loss was made whereas today the MPL is simply the sum of aggregate limits). This practise, being subjective, was obviously open to abuse (and often proved woefully inadequate). It is interesting to note however that some of the commonly used MPL percentages applied for peak exposures in certain markets were higher than those used today from the vendor models at high return periods.

The vendor modellers themselves are very open about the limitations in their models and regularly discuss the sources of uncertainty in their models. There are two main areas of uncertainty – primary and secondary – highlighted in the models. Some also refer to tertiary uncertainty in the uses of model outputs.

Primary uncertainty relates to the uncertainty in determining events in time, in space, in intensity, and in spatial distribution. There is often limited historical data (sampling error) to draw upon, particularly for large events. For example, scientific data on the physical characteristics of historical events such as hurricanes or earthquakes are only as reliable for the past 100 odd years as the instruments available at the time of the event. Even then, due to changes in factors like population density, the space over which many events were recorded may lack important physical elements of the event. Also, there are many unknowns relating to catastrophic events and we are continuously learning new facts as this article on the 2011 Japan quake illustrates.

Each of the vendor modellers build a catalogue of possible events by supplementing known historical events with other possible events (i.e. they fit a tail to known sample). Even though the vendor modellers stress that they do not predict events, their event catalogues determine implied probabilities that are now dominant in the catastrophe reinsurance pricing discovery process. These catalogues are subject to external validation from institutions such as Florida Commission which certifies models for use in setting property rates (and have an interest in ensuring rates stay as low as possible).

Secondary uncertainty relates to data on possible damages from an event like soil type, property structures, construction materials, location and aspect, building standards and such like factors (other factors include liquefaction, landslides, fires following an event, business interruption, etc.). Considerable strides, especially in the US, have taken place in reducing secondary uncertainties in developed insurance markets as databases have grown although Asia and parts of Europe still lag.

A Guy Carpenter report from December 2011 on uncertainty in models estimates crude confidence levels of -40%/+90% for PMLs at national level and -60%/+170% for PMLs at State level. These are significant levels and illustrate how all loss estimates produced by models must be treated with care and a healthy degree of scepticism.

Disclosures by reinsurers have also improved in recent years in relation to specific events. In the recent past, many reinsurers simply disclosed point estimates for their largest losses. Some still do. Indeed some, such as the well-respected Renaissance Re, still do not disclose any such figures on the basis that such disclosures are often misinterpreted by analysts and investors. Those that do disclose figures do so with comprehensive disclaimers. One of my favourites is “investors should not rely on information provided when considering an investment in the company”!

Comparing disclosed PMLs between reinsurers is rife with difficulty. Issues to consider include how firms define zonal areas, whether they use a vendor model or a proprietary model, whether model options such as storm surge are included, how model results are blended, and annual aggregation methodologies. These are all critical considerations and the detail provided in reinsurers’ disclosures is often insufficient to make a detailed determination. An example of the difficulty is comparing the disclosures of two of the largest reinsurers – Munich Re and Swiss Re. Both disclose PMLs for Atlantic wind and European storm on a 1 in 200 year return basis. Munich Re’s net loss estimate for each event is 18% and 11% respectively of its net tangible assets and Swiss Re’s net loss estimate for each event is 11% and 10% respectively of its net tangible assets.  However, the comparison is of limited use as Munich’s is on an aggregate VaR basis and Swiss Re’s is on the basis of pre-tax impact on economic capital of each single event.

Most reinsurers disclose their PMLs on an occurrence exceedance probability (OEP) basis. The OEP curve is essentially the probability distribution of the loss amount given an event, combined with an assumed frequency of an event. Other bases used for determining PMLs include an aggregate exceedance probability (AEP) basis or an average annual loss (AAL) basis. The AEP curves show aggregate annual losses and how single event losses are aggregated or ranked when calculating (each vendor has their own methodology) the AEP is critical to understand for comparisons. The AAL is the mean value of a loss exceedance probability distribution and is the expected loss per year averaged over a defined period.

An example of the potential misleading nature of disclosed PMLs is the case of Flagstone Re. Formed after Hurricane Katrina, Flagstone’s business model was based upon building a portfolio of catastrophe risks with an emphasis upon non-US risks. Although US risks carry the highest premium (by value and rate on line), they are also the most competitive. The idea was that superior risk premia could be delivered by a diverse portfolio sourced from less competitive markets. Flagstone reported their annual aggregate PML on a 1 in 100 and 1 in 250 year basis. As the graph below shows, Flagstone were hit by a frequency of smaller losses in 2010 and particularly in 2011 that resulted in aggregate losses far in excess of their reported PMLs. The losses invalidated their business model and the firm was sold to Validus in 2012 at approximately 80% of book value. Flagstone’s CEO, David Brown, stated at the closing of the sale that “the idea was that we did not want to put all of our eggs in the US basket and that would have been a successful approach had the pattern of the previous 30 to 40 years continued”.

click to enlarge

Flagstone CAT losses

The graphs below show a sample of reinsurer’s PML disclosures as at end Q1 2013 as a percentage of net tangible assets. Some reinsurers show their PMLs as a percentage of capital including hybrid or contingent capital. For the sake of comparisons, I have not included such hybrid or contingent capital in the net tangible assets calculations in the graphs below.

US Windstorm (click to enlarge)

US windstorm PMLs 2013

US & Japan Earthquake (click to enlarge)

US & Japan PMLs 2013

As per the S&P article, its important to look at the shape of PML curves as well as the levels for different events. For example, the shape of Lancashire PML curve stands out in the earthquake graphs and for the US gulf of Mexico storm. Montpelier for US quake and AXIS for Japan quakes also stand out in terms of the increased exposure levels at higher return periods. In terms of the level of exposure, Validus stands out on US wind, Endurance on US quake, and Catlin & Amlin on Japan quake.

Any investor in this space must form their own view on the likelihood of major catastrophes when determining their own risk appetite. When assessing the probabilities of historical events reoccurring, care must be taken to ensure past events are viewed on the basis of existing exposures. Irrespective of whether you are a believer in the impact of climate changes (which I am), graphs such as the one below (based off Swiss Re data inflated to 2012) are often used in industry. They imply an increasing trend in insured losses in the future.

Historical Insured Losses (click to enlarge)1990 to 2012 historical insured catastrophe losses Swiss ReThe reality is that as the world population increases resulting in higher housing density in catastrophe exposed areas such as coast lines the past needs to be viewed in terms of todays exposures. Pictures of Ocean Drive in Florida in 1926 and in 2000 best illustrates the point (click to enlarge).

Ocean Drive Florida 1926 & 2000

There has been interesting analysis performed in the past on exposure adjusting or normalising US hurricane losses by academics most notably by Roger Pielke (as the updated graph on his blog shows). Historical windstorms in the US run through commercial catastrophe models with todays exposure data on housing density and construction types shows a similar trend to those of Pielke’s graph. The historical trend from these analyses shows a more variable trend which is a lot less certain than increasing trend in the graph based off Swiss Re data. These losses suggest that the 1970s and 1980s may have been decades of reduced US hurricane activity relative to history and that more recent decades are returning to a more “normal” activity levels for US windstorms.

In conclusion, reviewing PMLs disclosed by reinsurers provides an interesting insight into potential exposures to specific events. However, the disclosures are only as good as the underlying methodology used in their calculation. Hopefully, in the future, further detail will be provided to investors on these PML calculations so that real and meaningful comparisons can be made. Notwithstanding what PMLs may show, investors need to understand the potential for catastrophic events and adapt their risk appetite accordingly.