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.
Posted in General
Tagged 1 in 100 event, 1 in 200 capital, 1 in 250 event, 99.5% VaR, adjusted premium, AIR, Atlantic hurricane, California wildfires, catastrophe insurance sector, catastrophe risks, collateralised reinsurance, cost of capital, credit cycles, Eqecat, exceedance curves, fat tail, Florida windstorm, Hurricane Harvey, Hurricane Irma, Hurricane Jose, hybrid capital, ILS, ILS fund, ILS funds, ILS investor, ILS market, ILS multiples, ILS pricing, insurance linked securities, insurance sector, LMX spiral, London market insurers, loss exceedance estimates, Mexico earthquake, model uncertainty, natural catastrophes, nature unpredictability, net tangible assets, PML, probable maximum losses, property catastrophe pricing, rate on line, reinsurance pricing, reinsurance rates, reserve releases, return periods, RMS, ROE normalised, ROL, sources of uncertainty, South-East US catastrophe exposure, specialty insurance, subordinate debt, tail risk, tail VaR, TVaR, underwriting cycles, US hurricanes, US wind perils, vendor models, west coast Florida, Willis Re, windstorm Ophelia, yield seeking investors
I have posted numerous times now (to the point of boring myself!) on the dangers of relying on a single model for estimating losses from natural catastrophes. The practise is reportedly widespread in the rapidly growing ILS fund sector. The post on assessing probable maximum losses (PMLs) outlined the sources of uncertainty from such models, especially the widely used commercial vendors models from RMS, AIR and EqeCat.
The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) was created in 1995 as an independent panel of experts to evaluate computer models used for setting rates for residential property insurance. The website of the FCHLPM contains a treasure trove of information on each of the modelling firms who provide detailed submissions in a pre-set format. These submissions include specifics on the methodology utilised in their models and the output from their models for specified portfolios.
In addition to the three vendor modellers (RMS, AIR, EqeCat), there is also details on two other models approved by FCHLPM, namely Applied Research Associates (ARA) and the Florida Public Hurricane Loss Model (FPHLM)developed by the Florida International University.
In one section of the mandated submissions, the predictions of each of the models on the number of annual landfall hurricanes for a 112 year period (1900 to 2011 is the historical reference period) are outlined. Given the issue over the wind speed classification of Super-storm Sandy as it hit land and the use of hurricane deductibles, I assume that the definition of landfall hurricanes is consistent between the FCHLPM submissions. The graph below shows the assumed frequency over 112 years of 0,1,2,3 or 4 landfall hurricanes from the five modellers.
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As one of the objectives of the FCHLPM is to ensure insurance rates are neither excessive nor inadequate, it is unsurprising that each of the models closely matches known history. It does however demonstrate that the models are, in effect, limited by that known history (100 odd years in terms of climatic experiences is limited by any stretch!). One item to note is that most of the models have a higher frequency for 1 landfall hurricane and a lower frequency for 2 landfall hurricanes when compared with the 100 year odd history. Another item of note is that only EqeCat and FPHLM have any frequency for 4 landfall hurricanes in any one year over the reference period.
Each of the modellers are also required to detail their loss exceedance estimates for two assumed risk portfolios. The first portfolio is set by FCHLPM and is limited to 3 construction types, geocodes by ZIP code centroil (always be wary of anti-selection dangers in relying on centroil data, particularly in large counties or zones with a mixture of coastal and inland exposure), and specific policy conditions. The second portfolio is the 2007 Florida Hurricane Catastrophe Fund aggregate personal and commercial residential exposure data. The graphs below show the results for the different models with the dotted lines representing the 95th percentile margin of error around the average of all 5 model outputs.
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As would be expected, uncertainty over losses increase as the return periods increase. The tail of outputs from catastrophe models clearly need to be treated will care and tails need to be fatten up to take into account uncertainty. Relying solely on a single point from a single model is just asking for trouble.
Posted in Insurance Models
Tagged 95th percentile margin of error, AIR, annual landfall hurricanes, Applied Research Associates, Eqecat, evaluate computer models, fat tail, Florida Commission on Hurricane Loss Projection Methodology, Florida Public Hurricane Loss Model, ILS fund, loss exceedance estimates, natural catastrophes, PMLs, probable maximum losses, residential property insurance, return periods, RMS, setting rates, sources of uncertainty, uncertainty, vendor models