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
In many episodes of fervent investment activity within a particular hot spot, like the current insurance M&A party, there is a point where you think “really?”. The deal by Mitsui Sumitomo to take over Amlin at 2.4 times tangible book is one such moment. A takeover of Amlin was predicted by analysts, as per this post, so that’s no surprise but the price is.
With the usual caveat on the need to be careful when comparing multiples for US, Bermuda, London and European insurers given the different accounting standards, the graph below from a December post, shows the historical tangible book value levels and the improving multiples being applied by the market to London firms such as Amlin.
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Comparable multiples from recent deals, as per the graph below, show the high multiple of the Mitsui/Amlin deal. Amlin has a 10 year average ROE around 20% but a more realistic measure is the recent 5 year average of 11%. In today’s market, the short to medium term ROE expectation is likely to be in the high single digits. Even at 10%, the 2.4 multiple looks aggressive.
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There is little doubt that the insurance M&A party will continue and that the multiples may be racy. In the London market, the remaining independent players are getting valued as such, as per the graph below tracking valuations at points in time.
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When the hangover comes, a 2.4 multiple will look even sillier than its does now at this point in the pricing cycle. In the meantime, its party like 1999 time!
Posted in Insurance Firms, Insurance Market
Tagged 99% T-VaR, 99.5% VaR, Amlin, average ROE, bermudian insurers, commercial insurance pricing, dilution of terms and conditions, European reinsurers, fervent investment activity, insurance M&A, insurance mergers, insurance pricing pressure, insurance valuation, Lancashire valuation, London based specialty insurers, london insurance market, London market, M&A premium, Mitsui Sumitomo, reinsurance pricing, reinsurance rates, ROE expectation, specialty insurance sector, tangible book value, tangible book values
In a previous post, I compared the M&A action in the reinsurance and specialty insurance space to a rush for the bowl of keys in a swingers party. Well, the ACE/Chubb deal has brought the party to a new level where anything seems possible. The only rule now seems to be a size restriction to avoid a G-SIFI label (although MetLife and certain US stakeholders are fighting to water down those proposals for insurers).
I expanded the number of insurers in my pool for an update of the tangible book multiples (see previous post from December) as per the graphic below. As always, these figures come with a health warning in that care needs to be taken when comparing US, European and UK firms due to the differing accounting treatment (for example I have kept the present value of future profits as a tangible item). I estimated the 2015 ROE based upon Q1 results and my view of the current market for the 2011 to 2015 average.
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I am not knowledgeable enough to speculate on who may be the most likely next couplings (for what its worth, regular readers will know I think Lancashire will be a target at some stage). This article outlines who Eamonn Flanagan at Shore Capital thinks is next, with Amlin being his top pick. What is clear is that the valuation of many players is primarily based upon their M&A potential rather than the underlying operating results given pricing in the market. Reinsurance pricing seems to have stabilised although I suspect policy terms & conditions remains an area of concern. On the commercial insurance side, reports from market participants like Lockton (see here) and Towers Watson (see graph below) show an ever competitive market.
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Experience has thought me that pricing is the key to future results for insurers and, although the market is much more disciplined than the late 1990s, I think many will be lucky to produce double-digit ROEs in the near term on an accident year basis (beware those dipping too much into the reserve pot!).
I am also nervous about the amount of unrealised gains which are inflating book values that may reverse when interest rates rise. For example, unrealised gains make up 8%, 13% and 18% of the Hartford, Zurich, and Swiss Re’s book value respectively as at Q1. So investing primarily to pick up an M&A premium seems like a mugs game to me in the current market.
M&A obviously brings considerable execution risk which may result in one plus one not equalling two. Accepting that the financial crisis hit the big guys like AIG and Hartford pretty hard, the graph below suggests that being too big may not be beautiful where average ROE (and by extension, market valuation) is the metric for beauty.
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In fact, the graph above suggests that the $15-$25 billion range in terms of premiums may be the sweet spot for ROE. Staying as a specialist in the $2-7 billion premium range may have worked in the past but, I suspect, will be harder to replicate in the future.
Posted in Insurance Market
Tagged 99% T-VaR, 99.5% VaR, ACE Chubb merger, Amlin, bermudian insurers, commercial insurance pricing, dilution of terms and conditions, European reinsurers, G-SIFI, Hartford, insurance M&A action, insurance pricing pressure, insurance valuation, Lancashire valuation, London based specialty insurers, london insurance market, M&A premium, MetLife, policy terms & conditions, price tangible book values, price to tangible book value, reinsurance price to tangible book value, reinsurance pricing, reinsurance rates, risk adjusted premiums, risk based capital, Shore Capital, specialty insurance sector, specialty insurer valuation, swingers party, Swiss Re, tangible book multiples, total return, unrealised book value, unrealised capital gains, VaR shock insurance, Zurich
In an opinion piece in the FT in 2008, Alan Greenspan stated that any risk model is “an abstraction from the full detail of the real world”. He talked about never being able to anticipate discontinuities in financial markets, unknown unknowns if you like. It is therefore depressing to see articles talk about the “VaR shock” that resulted in the Swissie from the decision of the Swiss National Bank (SNB) to lift the cap on its FX rate on the 15th of January (examples here from the Economist and here in the FTAlphaVille). If traders and banks are parameterising their models from periods of unrepresentative low volatility or from periods when artificial central bank caps are in place, then I worry that they are not even adequately considering known unknowns, let alone unknown unknowns. Have we learned nothing?
Of course, anybody with a brain knows (that excludes traders and bankers then!) of the weaknesses in the value-at-risk measure so beloved in modern risk management (see Nassim Taleb and Barry Schachter quotes from the mid 1990s on Quotes page). I tend to agree with David Einhorn when, in 2008, he compared the metric as being like “an airbag that works all the time, except when you have a car accident“. A piece in the New York Times by Joe Nocera from 2009 is worth a read to remind oneself of the sad topic.
This brings me to the insurance sector. European insurance regulation is moving rapidly towards risk based capital with VaR and T-VaR at its heart. Solvency II calibrates capital at 99.5% VaR whilst the Swiss Solvency Test is at 99% T-VaR (which is approximately equal to 99.5%VaR). The specialty insurance and reinsurance sector is currently going through a frenzy of deals due to pricing and over-capitalisation pressures. The recently announced Partner/AXIS deal follows hot on the heels of XL/Catlin and RenRe/Platinum merger announcements. Indeed, it’s beginning to look like the closing hours of a swinger’s party with a grab for the bowl of keys! Despite the trend being unattractive to investors, it highlights the need to take out capacity and overhead expenses for the sector.
I have posted previously on the impact of reduced pricing on risk profiles, shifting and fattening distributions. The graphic below is the result of an exercise in trying to reflect where I think the market is going for some businesses in the market today. Taking previously published distributions (as per this post), I estimated a “base” profile (I prefer them with profits and losses left to right) of a phantom specialty re/insurer. To illustrate the impact of the current market conditions, I then fattened the tail to account for the dilution of terms and conditions (effectively reducing risk adjusted premia further without having a visible impact on profits in a low loss environment). I also added risks outside of the 99.5%VaR/99%T-VaR regulatory levels whilst increasing the profit profile to reflect an increase in risk appetite to reflect pressures to maintain target profits. This resulted in a decrease in expected profit of approx. 20% and an increase in the 99.5%VaR and 99.5%T-VaR of 45% and 50% respectively. The impact on ROEs (being expected profit divided by capital at 99.5%VaR or T-VaR) shows that a headline 15% can quickly deteriorate to a 7-8% due to loosening of T&Cs and the addition of some tail risk.
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For what it is worth, T-VaR (despite its shortfalls) is my preferred metric over VaR given its relative superior measurement of tail risk and the 99.5%T-VaR is where I would prefer to analyse firms to take account of accumulating downside risks.
The above exercise reflects where I suspect the market is headed through 2015 and into 2016 (more risky profiles, lower operating ROEs). As Solvency II will come in from 2016, introducing the deeply flawed VaR metric at this stage in the market may prove to be inappropriate timing, especially if too much reliance is placed upon VaR models by investors and regulators. The “full detail of the real world” today and in the future is where the focus of such stakeholders should be, with much less emphasis on what the models, calibrated on what came before, say.
Posted in Insurance Market, Insurance Models
Tagged 1 in 10000 return period, 99% T-VaR, 99.5% VaR, adverse selection, Alan Greenspan, Barry Schachter, climate models, David Einhorn, deeply flawed VaR metric, dilution of terms and conditions, discontinuities in financial markets, economic capital models, economic modelling, European insurance regulation, exchange rates, fat tails, financial engineering, financial innovation, financial models, FTAlphaVille, game theory, imperfect art of modelling, insurance capital models, insurance pricing pressure, internal capital models, internal models, Joe Nocera, loss exceedance probability distribution, modern risk management, Nassim Taleb, New York Times, probability models, probability of default, probability of occurrence, reducing risk adjusted premiums, Return on equity, return period, risk based capital, risk model, solvency ii, Solvency II calibration, Solvency II standard formula, specialty insurance sector, Swiss National Bank, Swiss Solvency Test, Swissie, tail value at risk, Tails of VaR, the Economist, unknown unknowns, unrepresentative low volatility, value at risk, VaR and T-VaR, VaR shock