Country Bond Rates are decreasing. As global debt is still increasing, trust is declining. 'Counterparty Safe Cash' is what's becoming more and more important. Prepare for getting used to negative interest rates!
BTW: If you can't hold your breath, go to the end of this blog and click one of the tabs to get an updated worldwide overview of actual country bond interest rates.
What's up?
Countries with a high inflation (e.g. Brazil, India, China) or countries (e.g. Portugal, Ireland, Spain) that can't control and therefore have to finance their increasing debt at high interest rates, still show optical interesting interest rates for investors... So it seems, as these relative high interest rates are in fact 'compensation for inflation' or 'hidden default premiums'.
And of course we have countries (e.g Greece), who's interest rates show that they have in fact gone broke.
Unfortunately non of the EU countries dares to pull the plug... From a risk management perspective: Living in a nuclear financial death zone, apparently is a better option than pulling the trigger in the knowledge that not only your Greek brothers but also YOU will be 'financial dead' for sure.....
Still the Greeks get away with this non compliance strategy, let's call it:
Greek Risk Management
Last but not least we have the strong countries like Denmark and Germany with low interest rates. These countries have to carry and finance their weaker brothers short term. So it all comes down on cash and counterparty risk.
The rhetorical question in this European business case is:
Can Germany finance a Europe that fails to restructure their debts in a sustainable way?
Country Bond Interest Rates in alphabetical order
Let's examine those interest rates as reported by Bloomberg, at the end of July 2012 in alphabetical order:
It's clear that country bonds interest rates vary widely across countries.
White spots in the table imply, there's no (Bloomberg) data available.
Let's bring some order in this bond-muddle, by ranking the countries on basis of their 10Y Bond yield.
Country Bond Interest Rates sorted by '10Y' Bond Rate
From the above chart it is clearly visible that
Germany, Denmark and The Netherlands already enter the negative interest rate zone for 1 and 2 year bonds.
Greece, with a phenomenal interest rate, is is completely burned up
The Eurozone is split up in good and bad performing countries
A strong, sustainable and relatively independent country like Switzerland has 'low short term', as well as 'low long term' interest rates. This must for sure be a warning to every investor to estimate long term interest for other countries much higher on the long term. Perhaps the relatively higher long term interest rates of other countries resembles the implicit (extra) inflation expectation on the long run.
Mattress Money
As debt keeps increasing, economic growth in western countries is
limited and modest inflation continues, short term interest rates will stay low
for the near future (until the end of time inflation beast is released).
With an increasing 'cash demand' from weak performing countries, we have to learn to get used to negative interest rates in relatively more strong performing countries.
In other words, consumers and professional investors have to pay to put their money in the bank. Why not keep your money under the mattress?
For consumers this might perhaps be a risky (theft) solution to consider. Professional investors however, have to reduce counterparty risk which demands first class collateral assets.
Therefore "mattress money" is no option for professional investors and (increasing) negative interest is the price these investors will have to pay for keeping more and more cash as debt and risk keep rising.
Desperate advice ;-)
Perhaps - just like World War II was financed by War Bonds - we should
appeal to private investigators and consumer to fund the government in their
desperate war against debt.... government debt ...
Are you interested in following the actual country bonds interest rates, than bookmark this blog or the special Actuary-Info Actual Country Bond Rates Page, and come back once in a while to view the latest bond interest developments by clicking on one of the next tabs (have a few seconds patience, loading 150 (!) bond rates takes some time).
Actual Country Bond Interest Rates (Alphabetical)
Actual Country Bond Interest Rates ('10Y' Sorted)
Update 2013 Bloomberg stopped publishing a lot of bond rates. That's why several bond rates are missing. Sorry.
Financial institutions have to optimize ‘Risk – Return’ and diversify their portfolio. This (strongly interactive) presentation by CEO and Actuary Jos Berkemeijer, supports the power of Gold as the best asset class to optimize ‘Risk – Return’ in a given portfolio.
Just widen your knowledge about monetary gold by examining the next presentation given on June 19 2012 as a 'Johan de Witt Lecture' before 60 in gold interested actuaries of the Dutch Actuarial Association (Actuarieel Genootschap, AG), the professional association of actuaries and actuarial specialists in the Netherlands.
With the help of a button (""ACTuary NOW" ), Jos Berkemeijer calls for action by actuaries on several main issues .
Dutch Pension Funds are active Traders
In a 2011 research document called "Herd behavior and trading of Dutch pension funds", researchers Rubbaniy, Lelyveld and Verschoor of the Dutch Erasmus University in Rotterdam, provided evidence that repudiates the popular belief that - in specific - Dutch pension funds are long-term passive institutional traders.
De facto Dutch pension funds are active traders and trade about 8.5% of their portfolio on a monthly basis!
Conclusions
Main conclusions of Rubbaniy (et al.) are:
Significant feedback trading strategies, both momentum and contrarian
Robust herding behavior in investments of Dutch pension
funds Overall (LSV) herding level of 8.14% (significant at 1% level !!) On average if 100 PFs are active in the same security in the same month, there are 8.14 more PFs trading on the same side of the market than what would be expected
under null hypothesis of random selection of securities.
Herding asymmetry in buying and selling of securities Across
asset classes there is a higher degree of herding in less-risky assets.
Recent financial crises have a positive impact on
both turnover and herding while it negatively affects feedback trading.
Explanations
Possible explanations of these herding effects are:
Possibly outsourcing of portfolio management and small PFs imitation of large PFs’ lead to the same kind of asset allocation strategy.
Many small Dutch PFs often hire the same large and reputed asset
management firms for their portfolio management and are likely to have
same asset allocation of their portfolios.
Even if they do their own portfolio management, small Dutch PFs may mimic the investment behavior of large PFs - a
widespread belief about the small investors - and thus, add to (LSV) herding measure.
Remarks
Let's conclude with some remarks....
Dangerous Big Brother Hedge Although large PFs (investors) have some 'economics of scale'
and budget for experimenting on a small scale with (alternative) non-conventional investments, their investment strategy probably strongly differs from a small PF, as liabilities, sponsor obligations and pension benefits conditions are often are fund specific.
Therefore, following a large PF asset strategy as a small PF, is extremely dangerous and will eventually not turn out to be the 'big brother hedge' the fund was aiming at.
Unfounded First Mover Risk Key question remains if all this herding, hedging and active trading results in an outperformance above a long term sustainable asset-location strategy.
Probably not. But although investors pretend tot act on a rational basis, in reality irrational and conformist behavior take the upper-hand. Small investors often don't dare to formulate a unique fund specific asset allocation strategy because of 'first mover risk'.
Keep care and formulate yur own specif pension fund strategic asset mix!
From the day we were born, we've learned to survive in a complex world by applying linear mechanisms in life:
On a short time scale things don't change much
The future can be predicted by extrapolation of the past
Every event now, must have a cause in the past
Results are a (linear) combination of events in the past
Linear Thinking
In line with our linear culture, we - actuaries, (risk) managers, investment consultants or asset managers, etc. - have applied this way of linear thinking in our professional field:
Mean reversion: Returns continue to go back to an average value over time
Volatilities are more or less constant in time
Increasing volatility is a good predictor of an upcoming financial crisis
Standard deviation is similar to risk or volatility
If a distribution is complex, a normal distribution nevertheless will do fine
Tail risks are not really interesting or can't be modelled anyway
More detailed psychological linear thinking in the Risk area...
Peer Risk: If all other professionals (institutions) are using a certain method or investment strategy, why should I take the risk of developing a new one?
First Mover Risk: Why should I act first and carry all research investments?
Supervisory Compliant:If the regulator prescribes new regulations, I'll apply those regulations as if it is my own risk appetite.
Big Brother Hedge Risk: I base my investment strategy at a save distance on the biggest leader in the market. Might trouble arise, the Regulator first has to deal with my Big Brother.
Regulation Risk: Regulation (change) is perceived as a given fact and not viewed or managed as a kind of risk
Risk of Free Rate Risk: There must be some kind of risk free interest rate.
Thinking long term and two steps deeper, it's obvious that applying any of the above mentioned linear thinking methods will likely be the nail in the coffin of any financial institution.
Linear thinking and modelling make our daily life more simple. Unfortunately, 'too simple' to cope with financial markets reality on a long term.
Metamorphosis by Escher...
If we are lucky, (market) circumstances only change slowly and we're able to adapt the value of the variables in our linear models gradually, while keeping our traditional way of linear thinking and modeling. We act just like the famous graphic artist Escher shows us in Metamorphosis...
If we are less 'lucky' (as we are in2012), our linear models all of a sudden seem to fail. Covariances and volatility increase. Systemic risk shows up everywhere and a 'risk free rate' turns out to be an illusion. Our risk dashboard is on fire and we'll have to admit: our linear MPT models are failing.
Navigation Risk Parable
Why is it so so hard to admit that our linear models fail?
Suppose you developed a 2D linear (x,y)-navigation app in your Florida flatland office. Your app works fine for years. Than you decide to visit Black Hills & Badlands of South Dakota. Suddenly your app seems to fail in the mountains. Travel times and distances on your display suddenly seem wrong.
You realise you urgently need to develop a nonlinear 3D (x,y,z)-navigation app.... However, you don't do it.
Why not?
Well, first of all your old linear 2D app worked fine for years and on short trips the app still works (approximately) fine.
Besides, nobody of your Californian friends uses a 3D app and developing a new nonlinear app is very expensive.
Well, it's time we realise that most developments in life are in fact nonlinear.
If the stakes are high, like in the investment business, linear models will eventually lead us to a disaster.
Summarised, we might conclude:
If life is Nonlinear, so why aren't our models?
New Solutions
What alternatives do we have for our old linear model?
Although there are many nonlinear models, I'll emphasize on two interesting nonlinear based models in this blog.
I. Predicting economic market crises using measures of collective panic
Is there an adequate predictor of a market crisis?
Using new statistical analysis tools based on complexity theory, the New England Complex Systems Institute (NECSI) performed a new research on predicting market crashes.
As we know volatility is a measure of risk. So one would expect an increase of volatility also to be an adequate predictor of a financial market crash. Unfortunately this is not the case, as is shown in the recent NECSI study. While volatility increases at the beginning of a crisis, it is unreliable as an adequate indicator of a nearby market crash.
What also is not true, is that a market crash is often triggered by market panic justified, or not justified, by external (bad) news.
The NECSI research indicates that it's the internal structure of the market and not an external crises, that's primarily responsible for a market crash.
It turns out that the number of different stocks that move up (U) or down (D) together is an indicator of the mimicry ( 'collective flight'; herding) within the market. When mimicry is high, many stocks follow each other's movements.
This "co-movement" of stocks is an indicator of a nervous market that is ripe for panic. The existence of a large probability of co-movement of stocks on any given day, is a measure of systemic risk and vulnerability to self-induced panic.
So, rather than measuring volatility or correlation, the fraction of stocks that move in the same direction turns out to be a successful predictor of a market crash..
NECSI researchers showed that a dramatic increase in market mimicry occurred during the entire year before each market crash of the past 25 years, including the recent financial crisis.
II. Worst-Case Value-at-Risk of Non-Linear Portfolios
We all know that VaR lacks some desirable theoretical properties:
- Not a coherent risk measure.
- Precise knowledge of the distribution function is critical
- Non-convex function of w → VaR minimization intractable
- To optimize VaR we have to resort to VaR approximations
- Normality assumption is unrealistic → may underestimate the actual VaR.
Zymler, Kuhn & Rustem of the Department of Computing Imperial College London now developed a nonlinear alternative for VAR, called
Two variations on WCVar lead to practical applications:
Worst-Case Polyhedral VaR (WCPVaR)
A polyhedral VaR approximation for portfolios containing long positions in European options expiring at the end of the investment horizon
Worst-Case Quadratic VaR (WCQVaR)
A suitable VaR approximation for portfolios containing long and/or short positions in European and/or exotic options expiring beyond the investment horizon.
Here's an example of WCQVar's results against WCVar (plain) and the good old 'Monte Carlo Var' we mostly use in linear modeling. This graph needs no further comment.....
Using the WCQVar leads to more realistic modeling results. WCQVar-techniques can also be used for for index tracking leads to spectacular results (see pdf).
Finally
After so many years of relative successfully using linear models, it's hard to recognize that we need new models based on new nonlinear approaches.
Therefore we need 'first movers'. Who's willing to take the risk and jump into the nonlinear deep-sea?
Be confident and stay on your happy feet.. after a successful jump, 'herding theory' tells us others will follow...
Desperate attempts to catch Risk in new regulatory standards like Basel (II/III) for banks and Solvency (II) for insurers seem a dead end street....
What is happening?
That's the question we're about to answer in this blog!
Here are some observations:
Risk Weighting
All new risk valuating standards are based on Risk Weighting. Some assets (or liabilities) are assumed to be more risky than others. In practice, every asset class that has been identified as more or less 'safe', has turned out to be risky after all. E.G., government bonds where - until the 2011 crisis in Greece - assumed to be risk free. Unfortunately, nothing could be further from the truth...
Nothing in life is risk free
Tier Ratio's
Instead of simple 'Equity to Asset Ratios', Tier 1 & 2 ratios where developed. These Tier ratios only take a fraction of the total assets into account. This leads to 'Equity to Risk-Weighted Assets Ratios' that insinuate adequate, substantial and reassuring 10-15% Capital ratios, while - in fact - they're not! These kind of ratios are misleading and create a false sense of safety....
Tier Ratios lead people up the garden path
Tail Hide and Seek
As more and more risks are valued, regulated and urge for extra capital requirements, financial institutions will try to create extra return on risks that are formally not or only 'light weighted' measured. This way substantial risks are 'pushed' into the tail, fat risk tails are created and the sight on the real risks in the company becomes misty.
Overregulation decreases the effect of good risk management
Illustration: Comparison 'Deutsche Bank' - 'Bank of America'
To illustrate what is happening, let's compare a giant like "Deutsche Bank" (DB) with the number one on the banking list, the "Bank of America" (BOA).
Financial Ratios
Deutsche Bank
Bank of America
(x 1 bn $) Year:
2010
2009
2010
2009
Assets (A)
1906
1501
2265
2230
Liabilities (L)
1855
1463
2037
1999
Shareholder Equity (SE)
49
37
228
231
SE / A - Ratio
2.6%
2.4%
10.1%
10.4%
---------------------------------
Risk-Weighted Assets (RWA)
346
273
1456
1543
Assets (A)
1906
1501
2265
2230
RWA / A - Ratio
18%
18%
64%
69%
---------------------------------
Regulatory Capital (RC)
49
38
230
226
Risk-Weighted Assets (RWA)
346
273
1456
1543
Total Capital Ratio
14.1%
13.9%
15.8%
14.7%
---------------------------------
Tier 1 capital
43
34
164
160
Risk-Weighted Assets (RWA)
346
273
1456
1543
Tier 1 Capital Ratio
12.3%
12.6%
11.2%
10.4%
Although both banks have more or less the same 'Tier 1' and 'Total Capital Ratio', their individual risk profile is completely different.
In the case of DB only 18% of the assets are assumed (marked) risky, while in the case of BOA around 64% is assumed risky and taken into account for a risk weighted solvency approach.
Notice that the simple gross 'Equity to Asset' Ratio (E/A-Ratio, or in short 'EAR') of DB is only 2.6%, while the similar ratio of BOA is around 10.1%. If DB would be hit by an 5% impact loss, it would be in deep trouble.
Reflections
Our risk models have become too sophisticated and don't cover the area of 'Unkown Risk' enough. Unintentionally rand controversially, risk regulations and models make us implicitly sweep our real risks under the carpet. In principle Risks can be categorized as:
Known Risk Measured
Known Risk Unmeasured
Unknown Risk
Hidden Risk (knowingly or unknowingly)
It's time to admit that no asset or liability is completely free of risk and there's an overall substantial probability that risk - by definition - will hit eventually from an unexpected corner. To put things in perspective: In the 19th century, banks funded their assets with around 40-50% equity.
Conclusion
Including 'Unkown Risk', a simple gross E/A-Ratio (EAR) of a magnitude of 15-25% (across the total assets) would probably be the best kind of guarantee to accomplish a more sustainable financial system in the world. The new EAR could be best defined as the sum of an actuarial underpinned percentage on basis of the underlaying calculable covered risks and a TBD overall 10% 'add up' for unknown risks:
Until we've included Unkown Risk fully in our risk models, we'll stay in deep trouble.
Aftermath: 'Avatar Ratios'
To rate a company (bank), often it's not enough to look at just the traditional financial ratios. An interesting way to additionally rate a company in a more sophisticated way, is with the help of so - by me - called 'Avatar Ratios'.
Additional to financial ratios, 'Avatar Ratios' tell you more about what the intentions, (real) important issues and the 'drive' of a company and its employees are.
An 'Avatar Ratio Analysis' gives you more or less 'the embodiment' of all what drives a company. It can be constructed by making a word analysis of a crucial document or annual report of a company. In short: You simply download the annual report (or any other company characteristic document) and analyze it with a 'Word Frequency Counter' like WriteWords.
With the help of WriteWords we first create (on line) a frequency table. Next we cut out irrelevant words like 'and', 'the', etc.
Here are the results:
(1) a scrollable frequency table of all relevant words
(2) a 'Top 22 words' frequency table
In most cases - like this one - the result of simply putting the first 10 to 15 words in the top of the frequency table behind each other, is astonishing: It creates a kind of 'Identity Statement'. Here's the result for IAA's strategic plan, where even more than 20 words give a beautiful comprised identity statement:
IAA's Strategic Plan (Identity Statement comprised with WriteWords)
Actuarial strategic develop associations. Standards, priorities plans objective action. Promote profession member international. IAA association practice maintain key. Global encourage education worldwide.
Avatar Analysis: Comparison 'Deutsche Bank' - 'Bank of America'
Let's now go back to our banking case and compare 'Deutsche Bank' (DB) and the 'Bank of America' (BOA) with the help of a simple Avatar Ratio Analysis.
The Avatar Ratio Analys presents the word frequency (absolute numbers) and their relative frequency (= word frequency / total number of word in document). Here is the result:
Avatar Ratios
Deutsche Bank
Bank of America
Freq.
Perc.
Freq.
Perc.
Governance
109
0.06%
25
0.02%
Risk
1458
0.79%
852
0.53%
Control
273
0.15%
156
0.10%
Total G+R+C
1840
1.00%
1033
0.64%
------------------
Client/Customer
359
0.20%
250
0.15%
Shareholder
169
0.09%
162
0.10%
------------------
Transparent
15
0.01%
2
0.00%
------------------
Employee
153
0.08%
63
0.04%
Director
40
0.02%
23
0.01%
------------------
Profit, Income
1001
0.54%
835
0.52%
------------------
Tot. nr. of words
161579
100%
184048
100%
Although I'll leave the final conclusions up to you, here are some remarkable observations:
Total number of words
Both companies (DB and BOA) need an enormous amount of words to explain their environment (clients, shareholders, rating agencies, etc) the essentials about what's going on in their company in a modest calendar year.
To read an annual report of about 170,000 words, it would take an average reader (reading speed 200 to 250 words per minute) about 10-12 hours.
Perhaps you, as an actuary, can read faster ( test it!: speed reading test ), but even at a speed of 500 wpm it would be an enormous task (5-6 hours) to fulfill.
Governance, Risk & Control
It's clear that DB puts much more energy (+60%) in communicating about themes as Governance Risk and Control than BOA. Also is clear that DB is far more transparent in its communication than BOA. This does (of course) not imply that BOA's risk and control frame is inferior to DB's. It could even be the opposite. It just shows that (and how) BOA handles and communicates differently (less open) from DB.
Profit, Income, Shareholders + Clients and Employees
DB and BOA weight Profit, Income and shareholders on more or less the same level. Both rank client/customer above shareholders. DB gives 'clients/customers' as well as employees double the attention of BOA!
At last
Next time you report to your board, include an Avatar Analysis of your report in your presentation!
We all are more than familiar with the definition of Risk:
Risk = Probability × Impact= P×I
This way of measuring risk is a nice, simple, explainable and intuitive way of ordering risks in board or bath rooms, but unfortunately quite useless.
To demonstrate the limits of this kind of typical Risk definition, let's take a look at the next story:
The Risk of bicycling
You decided to start a 3 year math study at City University in London. From your brand new apartment in Southall, it's a 12.5 mile drive to the University at Southhampton Street. As a passionate cyclist you consider the risk of cycling through London for the next three years.
Based on your googled " DFT's Reported Road Casualties 2009" research (resulting in a cycling death rate of 36 per billion vehicle miles), you first conclude that the probability of getting killed in a cycle accident during your three year study is relatively low : 0.1% (≈ 3[years] × 365[days] × 25[miles] × (36 [Killed] ÷ 109[vehicle miles]).
Subjective probability
After this factfinding you start to realize it's YOU getting on the bike and it's YOUR 0.1% risk of DYINGin the next three years of your study....
Hmmmm...this comes closer; it makes things a little different, doesn't it?
Its looks like 'subjective probability' - on reflection - is perhaps somewhat different from 'objective probability'.
While your left and right brain are still in a dormant paradoxical state of confusion, your left (logical) brain already starts to cope with the needs of the right (emotional) half that wants you on that bike at all costs!
Russian Roulette
Now your left brain tells you not to get emotional, after all it is 'only' an additional 0.1% risk. Already your left brain starts searching for reference material to legitimate the decision you're about to take.
Aha!.... Let's compare it with 'Russian Roulette', your left brain suggests. Instead of 6 chambers we have thousand chambers with one bullet. Heeee, that makes sense, you talk to yourself.
With such a 1000 chambers Russian gun against my head I would pull the trigger without hesitating.... Or wouldn't I?..... No.., to be completely honest, 'I wouldn't risk it', my right brain tells me.
In Control
My left half concludes it must be the 'feeling' of my right side that makes me feel I'm 'in control' on my bike, but not in case of Russian Roulette. That makes sense, tells my left brain me. Of course! Problem solved! My right and left brain finally agree: It's only a small risk and it's I who can control the outcome of a healthy drive. Besides, this way the health benefits of cycling massively outweigh the risks as well, my right brain convinces me superfluous.
A final check by my right brain tells me: If I can't trust myself, who can I?
This rhetorical question is the smashing argument in stepping on the bike and to enjoy a wonderful ride through London City.
As ever...,
Aftermathematics
After returning from my accidentless bike trip, I enjoy a drink with a colleague of mine, the famous actuary Will Strike [who doesn't know him? ;-)].
After telling him my 'bike decision story' he friendly criticizes me for my non-professional approach in this private decision problem. Will tells me that I should not only have analyzed the probability (P), but also the Impact (I) of my decision. Remember the equation: Risk=P×I?
Yes of course, Will is right. How could I forget? ..., the probability of getting a deathly accident was only 0.1%.
Yet, 'when' a car hits you full, the probability of meeting St. Petrus at heaven's gate is 100% and the Impact (I) is maximal (I=1; you're dead ...)
Summarized:
Risk[death on bike;25 miles/day; 3 years] =
Probability × Impact = 0.1% × 1=0.1%
From this outcome it's clear that, even though the Impact is maximal (1=100%) , on a '0% to 100% Risk scale' this 3 year 'London-Bike Risk Project' seems negligible and by no means a risk that would urge my full attention.
I'm finally relieved... it always makes a case stronger to have a taken decision verified by another method. In this case the Risk=P×I method confirmed my decision taken on basis of my left-right brain discussion. Pff....
Afteraftermath
The next morning, after my subconscious brain washed the 'bike dishes' of the day before, I wake up with new insights. Suddenly I realize I tried to take my biking decision on the wrong variable: Probability, instead of Impact.
Actually, in both cases and without realizing, I took my decision finally on basis of the Impact and the possible 'Preventional Control' (not damage control !!!) I could exert before and during my bike trip.
I had to conclude that in cases of high Impact (I>0.9), nor my left-right brain chat, nor the 'Risk=PxI' formula lead to a sound decision, because both are too much based on probability instead of Impact. In other words:
In case of high Impact, probability is irrelevant
In case of high Impact, only control counts
From now on this 'bike conclusion' will be engraved in my memory and I will apply it in my professional work as well.
P.S. for disbelievers, the tough ones!
If you're convinced you would take the risk of firing the 1000 chamber Russian gun against your head, you probably valuate the fun of the bicycle trip higher than probability of the loss of your life or good health.
In this case, suppose someone would offer you an amount of money if you would take part in a 1000 chamber Russian roulette instead of a bicycle tour. At which amount would you settle?
Let's assume you would settle at € 10.000.000 (I wouldn't settle for less). In this case you really value your bicycle trip!!!!
As we've seen in banking business as well : extreme low probabilities and high impact situations are tricky! That's why stress tests focus on impact and not on probability.
The different faces of Risk
Another issue when looking at risk is that risk is always conditional.
'The' probability of death or 'the' mortality rate doesn't exist. Mortality depends on a number of variables, such as age (the run down state of your DNA quality), the DNA-quality you where born with and lifestyle. Secondly mortality also depends on a number of uncertain events in your life.
To demonstrate this 'Chameleon property' of probability, lets take a look at the probability of a meteor hitting good old earth.
The initial probability of an asteroid devastating the earth within a 10 year time frame is around 0.1%. A typical case of low probability and high impact. Once we've become aware of a spotted meteor in our direction, the probability suddenly changes from a general probability in a time frame to a asteroid specific probability during his actual passage of the earth.
In case of the asteroid '2011 MD' that will pass the earth at a minimal distance of 11000 km on June 27, 2011, this specific probability turns out 0.11% (remember the Russian Gun...).
With a diameter of around 8 meter, this asteroid is no big threat to our civilization.
Here's a short impression what's coming flying in on us within the next decades (Source: Nasa; asteroid>50 meter or minimum distance< 100,000km):
Apart from some 'big asteroids' in the next decade, this picture puts our minds at rest. Yet we should keep in mind that most asteroids are discovered only a few weeks before a possibe collaps...
In their document they analyze the case of the film Armageddon, where an asteroid of the size of Texas is on a direct collision course with the earth and Harry Stamper (alias Bruce Willis) saves the world by blowing it up.
Trying to fill in the Risk=P×I model in this Armageddon case is useless.
In this case, Risk is defined as:
Risk = [Probability of Impact] × [Impact of asteroid striking the earth]
Fanton and Neil conclude:
We cannot get the Probability number.
The probability number is a mix up. In general it makes no sense and it's too difficult for a risk manager to give the unconditional probability of every ‘risk’ irrespective of relevant controls, triggers and mitigants.
We cannot get the Impact number.
Impact (on what?) can't be unconditional defined without considering also the possible mitigating events.
Risk score is meaningless.
Even if we could get round the two problems above, what exactly does the resulting number mean?
It does not tell us what we really need to know.
What we really need to know is the probability, given our current state of knowledge, that there will be massive loss of life.
Instead of the Risk=P×I model, Fanton and Neil propose (Measuring risks) the use of causal models (risk maps) in which a risk is characterised by a set of uncertain events.
Each of these events has a set of outcomes and the ‘uncertainty’ associated with a risk is not a separate notion (as assumed in the classic approach).
Every event (and hence every object associated with risk) has uncertainty that is characterised by the event’s probability distribution.
Examples:
The Initial risk of meteor strike
The probability of loss of life (meaning at least 80% of the world population) is about 77%:
In terms of the difference that Bruce Willis and his crew could make there are two scenarios: (1) the meteor is blown up and (2) where it is not.
Reading off the values for the probability of “loss of life” being false we find that we jump from 8.5% (meteor not blown up) to 82% (meteor blown up). This near tenfold increase in the probability of saving the world clearly explains why it merited an attempt.
Lessons learned
Use (Bayesian) Risk Maps rather than the Probability Impact Model or Risk Heat Maps, if you want to take decisions on facts instead of your intuition.
applicable to the Institutions for Occupational Retirement Provision (IORPs) in the context of the IORP Directive.
The report analyses several interesting differences in reporting among member states.
I'll will confine myself in this blog to two remarkable results.... 1. Difference in number of Supervision employees per country
It's remarkable (and not directly explainable) to see that the UK and The Netherlands outnumber the other European countries on number of supervision employees....
2. Influence Actuarial Reporting
The survey provides a large number of reporting and monitoring issues that aim to monitor or mitigate several types of risk.
I'll provide a short report that shows the connection between some actuarial reports and types of risk.
Clearly the risk of funding is one of the most important issues with regard to actuarial reporting. Perhaps it's even a little bit overweighted......
Anyhow, check your reports with regard to the above risks, especially if your living in an oversized supervision country like the UK or The Netherlands....
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