Tag Archives: uncertainties PMLs

Lancashire…so much to answer for.

My bearishness on the reinsurance and specialty insurance sector is based upon my view of a lack of operating income upside due to the growing pricing pressures and poor investment income. I have posted many times (most recently here) on the book value multiple expansion that has driven valuations over the past few years. With operating income under pressure, further multiple expansion represents the only upside in valuations from here and that’s not a very attractive risk/reward profile in my view. So I am happy to go to the sidelines to observe from here.

So, what does this mean for my previously disclosed weak spot for Lancashire, one the richest valued names in the sector? Lancashire posted YE2013 results last week and disappointed the market on the size of its special dividend. As previously highlighted, its Cathedral acquisition marked a change in direction for Lancashire, one which has confused observers as to its future. During the conference call, in response to anxious analysts, management assured the market that M&A is behind it and that its remains a nimble lead specialist high risk/high return underwriter dedicated to maximising shareholder returns from a fixed capital base, despite the lower than expected final special dividend announced for 2013.

The graph below illustrates the past success of Lancashire. Writing large lead lines on property, energy, marine and aviation business has resulted in some astonishingly good underwriting returns for Lancashire in the past. The slowly increasing calendar year combined ratios for the past 5 years and the lack of meaningful reserve releases for the past two year (2013 even saw some reserve deterioration on old years) show the competitive pressures that have been building on Lancashire’s business model.

click to enlargeLancashire Combined Ratio Breakdown 2006 to 2013

The Cathedral acquisition offers Lancashire access to another block of specialist business (which does look stickier than some of Lancashire’s business, particularly on the property side). It also offers Lancashire access to Lloyds which could have some capital arbitrage advantages if Lancashire starts to write the energy and terrorism business through the Lloyds’ platform (as indicated by CEO Richard Brindle on the call). Including the impact of drastically reducing the property retrocession book for 2014, I estimate that the Cathedral deal will add approx 25% to GWP and NEP for 2014. Based upon indications during the call, I estimate that GWP breakdown for 2014 as per the graph below.

click to enlargeLancashire GWP Split

One attractive feature of Lancashire is that it has gone from a net seller of retrocession to a net buyer. Management highlighted the purchase of an additional $100 million in aggregate protection. This is reflected in the January 1 PML figures. Although both Lancashire and Cathedral write over 40% of their business in Q1, I have taken the January 1 PML figures as a percentage of the average earned premium figures from the prior and current year in the exhibit below.

click to enlargeLancashire PMLs January 2010 to January 2014

The graphs above clearly show that Lancashire is derisking its portfolio compared to the higher risk profile of the past two years (notably in relation to Japan). This is a clever way to play the current market. Notwithstanding this de-risking, the portfolio remains a high risk one with significant natural catastrophic exposure.

It is hard to factor in the Cathedral results without more historical data than the quarterly 2013 figures provided in the recent supplement (another presentation does provide historical ultimate loss ratio figures, which have steadily decreased over time for the acquired portfolio) and lsome of the CFO comments on the call referring to attritional loss ratios & 2013 reserve releases. I estimate a 68% combined ratio in 2014, absent significant catastrophe losses, which means an increase in the 2013 underwriting profit of $170 million to $220 million. With other income, such as investment income and fee income from the sidecar, 2014 could offer a return of the higher special dividend.

So, do I make an exception for Lancashire? First, even though the share price hasn’t performed well and currently trades around Stg7.30, the stock remains highly valued around 180% tangible book.  Second, pricing pressures mean that Lancashire will find it hard to make combined ratios for the combined entities significantly lower than the 70% achieved in 2013, in my view. So overall, although Lancashire is tempting (and will be more so if it falls further towards Stg7.00), my stance remains that the upside over the medium term does not compensate for the potential downside. Sometimes it is hard to remain disciplined……

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.

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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”.

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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.