Tag Archives: property catastrophe

Low risk premia and leverage

The buzz from the annual insurance speed dating festival in Monte Carlo last week seems to have been subdued. Amid all the gossip about the M&A bubble, insurers and reinsurers tried to talk up a slowing of the rate of price decreases. Matt Weber of Swiss Re said “We’ve seen a slowing down of price decreases, although prices are not yet stable. We believe the trend will continue and we’ll see a stabilisation very soon”. However, analysts are not so sure. Moody’s stated that “despite strong signs that a more rational marketplace is emerging in terms of pricing, the expansion of alternative capital markets continues to threaten the traditional reinsurance business models”.  Fitch commented that “a number of fundamental factors that influence pricing remain negative” and that “some reinsurers view defending market share by writing business below the technical price floor as being an acceptable risk”. KBW comment that on-going pricing pressures will “eventually compressing underwriting margins below acceptable returns”.

It is no surprise then that much of the official comments from firms focused on new markets and innovation. Moody’s states that “innovation is a defence against ongoing disintermediation, which is likely to become more pronounced in areas in which reinsurers are not able to maintain proprietary expertise”. Munich Re cited developing new forms of reinsurance cover and partnering with hi-tech industries to create covers for emerging risks in high growth industries. Aon Benfield highlighted three areas of potential growth – products based upon advanced data and analytics (for example in wider indemnification for financial institutions or pharmaceuticals), emerging risks such as cyber, and covering risks currently covered by public pools (like flood or mortgage credit). Others think the whole business model will change fundamentally. Stephan Ruoff of Tokio Millennium Re said “the traditional insurance and reinsurance value chain is breaking up and transforming”. Robert DeRose of AM Best commented that reinsurers “will have a greater transformer capital markets operation”.

Back in April 2013, I posed the question of whether financial innovation always ends in reduced risk premia (here). The risk adjusted ROE today from a well spread portfolio of property catastrophe business is reportingly somewhere between 6% and 12% today (depending upon who you ask and on how they calculate risk adjusted capital). Although I’d be inclined to believe more in the lower range, the results are likely near or below the cost of capital for most reinsurers. That leads you to the magic of diversification and the over hyped “non-correlated” feature of certain insurance risks to other asset classes. There’s little point in reiterating my views on those arguments as per previous posts here, here and here.

In the last post cited above, I commented that “the use by insurers of their economic capital models for reinsurance/retrocession purchases is a trend that is only going to increase as we enter into the risk based solvency world under Solvency II”. Dennis Sugrue of S&P said “we take some comfort from the strength of European reinsurers’ capital modelling capabilities”, which can’t but enhance the reputation of regulatory approved models under Solvency II. Some ILS funds, such as Twelve Capital, have set up subordinated debt funds in anticipation of the demand for regulatory capital (and provide a good comparison of sub-debt and reinsurance here).

One interesting piece of news from Monte Carlo was the establishment of a fund by Guy Carpenter and a new firm founded by ex-PwC partners called Vario Partners. Vario states on their website they were “established to increase the options to insurers looking to optimise capital in a post-Solvency II environment” and are proposing private bonds with collateral structured as quota share type arrangements with loss trigger points at 1-in-100 or 1-in-200 probabilities. I am guessing that the objective of the capital relief focussed structures, which presumably will use Vario proprietary modelling capabilities, is to allow investors a return by offering insurers an ability to leverage capital. As their website saysthe highest RoE is one where the insurer’s shareholders’ equity is geared the most, and therefore [capital] at it’s thinnest”. The sponsors claim that the potential for these bonds could be six times that of the cat bond market. The prospects of allowing capital markets easy access to the large quota share market could add to the woes of the current reinsurance business model.

Low risk premia and leverage. Now that’s a good mix and, by all accounts, the future.

Follow-on (13th October 2015): Below are two graphs from the Q3 report from Lane Financial LLC which highlight the reduced risk premia prevalent in the ILS public cat bond market.

click to enlargeILS Pricing September 2015

click to enlargeILS Price Multiples September 2015

US Hurricane Follow-up

Following up on the topic of the last post, I previously discussed the importance of looking at historical experience adjusted for today’s exposure. Roger Pielke Jr is one source that has looked to “normalise” historical hurricane insured losses through the prism of today’s building types and densities and I highlighted Pielke’s work in my June 2013 post.

Another market expert is Karen Clark who used to work for one of the main catastrophe modelling firms, AIR Worldwide, and who now runs a consultancy firm. In August 2012, her firm published a report on the exposure adjusted insured cost of historical storms that would cost $10 billion or more. The graph below reproduces the results of the report showing the cost per year for hurricanes greater than $10 billion up to 2011, with the 20 year average loss cost.

click to enlargeHistorical US Hurricanes greater than $10 billion Karen Clark

The graph below, also from the Karen Clark report, shows where the storms hit.

click to enlargeLandfall Points of Historical US Hurricanes Karen Clark

Roger Pielke continues to issue interesting insights on his blog and in a recent post he stated:

“We shouldn’t let the past 9 years of abnormally low hurricane activity lull us into a sense of complacency.  It is only a matter of time before the long streak with no US Cat 3+ and Florida hurricanes is broken.”

That is a message that the current reinsurance market is happily ignoring.

The imperfect art of climate change modelling

The completed Group I report from the 5th Intergovernmental Panel on Climate Change (IPCC) assessment was published in January (see previous post on summary report in September). One of the few definite statements made in the report was that “global mean temperatures will continue to rise over the 21st century if greenhouse gas (GHG) emissions continue unabat­ed”. How we measure the impact of such changes is therefore incredibly important. A recent article in the FT by Robin Harding on the topic which highlighted the shortcomings of models used to assess the impact of climate change therefore caught my attention.

The article referred to two academic papers, one by Robert Pindyck and another by Nicholas Stern, which contained damning criticism of models that integrate climate and economic models, so called integrated assessment models (IAM).

Pindyck states that “IAM based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading”. Stern also criticizes IAMs stating that “assumptions built into the economic modelling on growth, damages and risks, come close to assuming directly that the impacts and costs will be modest and close to excluding the possibility of catastrophic outcomes”.

These comments remind me of Paul Wilmott, the influential English quant, who included in his Modeller’s Hippocratic Oath the following: “I will remember that I didn’t make the world, and it doesn’t satisfy my equations” (see Quotes section of this website for more quotes on models).

In his paper, Pindyck characterised the IAMs currently used into 6 core components as the graphic below illustrates.

click to enlargeIntegrated Assessment Models

Pindyck highlights a number of the main elements of IAMs which involve a considerable amount of arbitrary choice, including climate sensitivity, the damage and social welfare (utility) functions. He cites important feedback loops in climates as difficult, if not impossible, to determine. Although there has been some good work in specific areas like agriculture, Pindyck is particularly critical on the damage functions, saying many are essentially made up. The final piece on social utility and the rate of time preference are essentially policy parameter which are open to political forces and therefore subject to considerable variability (& that’s a polite way of putting it).

The point about damage functions is an interesting one as these are also key determinants in the catastrophe vendor models widely used in the insurance sector. As a previous post on Florida highlighted, even these specific and commercially developed models result in varying outputs.

One example of IAMs directly influencing current policymakers is those used by the Interagency Working Group (IWG) which under the Obama administration is the entity that determines the social cost of carbon (SCC), defined as the net present damage done by emitting a marginal ton of CO2 equivalent (CO2e), used in regulating industries such as the petrochemical sector. Many IAMs are available (the sector even has its own journal – The Integrated Assessment Journal!) and the IWG relies on three of the oldest and most well know; the Dynamic Integrated Climate and Economy (DICE) model, the Policy Analysis of the Greenhouse Effect (PAGE) model, and the fun sounding Climate Framework for Uncertainty, Negotiation, and Distribution (FUND) model.

The first IWG paper in 2010 included an exhibit, reproduced below, summarizing the economic impact of raising temperatures based upon the 3 models.

click to enlargeClimate Change & Impact on GDP IWG SCC 2010

To be fair to the IWG, they do highlight that “underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and economic research characterizing these relationships”.

The IWG released an updated paper in 2013 whereby revised SCC estimates were presented based upon a number of amendments to the underlying models. Included in these changes are revisions to damage functions and to climate sensitivity assumptions. The results of the changes on average and 95th percentile SCC estimates, at varying discount rates (which are obviously key determinants to the SCC given the long term nature of the impacts), can be clearly seen in the graph below.

click to enlargeSocial Cost of Carbon IWG 2010 vrs 2013

Given the magnitude of the SCC changes, it is not surprising that critics of the charges, including vested interests such as petrochemical lobbyists, are highlighting the uncertainty in IAMs as a counter against the charges. The climate change deniers love any opportunity to discredit the science as they demonstrated so ably with the 4th IPCC assessment. The goal has to be to improve modelling as a risk management tool that results in sensible preventative measures. Pindyck emphasises that his criticisms should not be an excuse for inaction. He believes we should follow a risk management approach focused on the risk of catastrophe with models updated as more information emerges and uses the threat of nuclear oblivion during the Cold War as a parallel. He argues that “one can think of a GHG abatement policy as a form of insurance: society would be paying for a guarantee that a low-probability catastrophe will not occur (or is less likely)”. Stern too advises that our focus should be on potential extreme damage and that the economic community need to refocus and combine current insights where “an examination and modelling of ways in which disruption and decline can occur”.

Whilst I was looking into this subject, I took the time to look over the completed 5th assessment report from the IPCC. First, it is important to stress that the IPCC acknowledge the array of uncertainties in predicting climate change. They state the obvious in that “the nonlinear and chaotic nature of the climate system imposes natu­ral limits on the extent to which skilful predictions of climate statistics may be made”. They assert that the use of multiple scenarios and models is the best way we have for determining “a wide range of possible future evolutions of the Earth’s climate”. They also accept that “predicting socioeconomic development is arguably even more difficult than predicting the evolution of a physical system”.

The report uses a variety of terms in its findings which I summarised in a previous post and reproduce below.

click to enlargeIPCC uncertainty

Under the medium term prediction section (Chapter 11) which covers the period 2016 to 2035 relative to the reference period 1986 to 2005, a number of the notable predictions include:

  • The projected change in global mean surface air temperature will likely be in the range 0.3 to 0.7°C (medium confidence).
  • It is more likely than not that the mean global mean surface air temperature for the period 2016–2035 will be more than 1°C above the mean for 1850–1900, and very unlikely that it will be more than 1.5°C above the 1850–1900 mean (medium confidence).
  • Zonal mean precipitation will very likely increase in high and some of the mid-latitudes, and will more likely than not decrease in the subtropics. The frequency and intensity of heavy precipitation events over land will likely increase on average in the near term (this trend will not be apparent in all regions).
  • It is very likely that globally averaged surface and vertically averaged ocean temperatures will increase in the near term. It is likely that there will be increases in salinity in the tropical and (especially) subtropical Atlantic, and decreases in the western tropical Pacific over the next few decades.
  • In most land regions the frequency of warm days and warm nights will likely increase in the next decades, while that of cold days and cold nights will decrease.
  • There is low confidence in basin-scale projections of changes in the intensity and frequency of tropical cyclones (TCs) in all basins to the mid-21st century and there is low confidence in near-term projections for increased TC intensity in the North Atlantic.

The last bullet point is especially interesting for the insurance sector involved in providing property catastrophe protection. Graphically I have reproduced two interesting projections below (Note: no volcano activity is assumed).

click to enlargeIPCC temperature near term projections

Under the longer term projections in Chapter 12, the IPCC makes the definite statement that opened this post. It also states that it is virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean temper­atures increase and that, in the long term, global precipitation will increase with increased global mean surface temperature.

I don’t know about you but it seems to me a sensible course of action that we should be taking scenarios that the IPCC is predicting with virtual certainty and applying a risk management approach to how we can prepare for or counteract extremes as recommended by experts such as Pindyck and Stern.

The quote “it’s better to do something imperfectly than to do nothing perfectly” comes to mind. In this regard, for the sake of our children at the very least, we should embrace the imperfect art of climate change modelling and figure out how best to use them in getting things done.

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.