When you're - like me - a born professional optimist, but nevertheless sometimes worry about the unavoidable misery in the world, you ask yourself this question:


Think about this question and try to answer it, before reading any further..



The answer to this question is very simple:


The moral of this anecdote is that when you're fully aware of all the risks and their possible impact, chances are high you'll not be able to take any well-argued decision at all, as any decision will eventually fail when your objective is to rule out all possible risks.


On the other hand, if you're not risk-conscious at all regarding a decision to be taken, most probably you'll take the wrong decision.

'Mathematical Confident'
So this leaves us with the inevitable conclusion that in our eager to take risk-based decisions, a reasoned decision is nothing more than the somehow optimized outcome of a weighted sum of a limited number of subjective perceived risks. 'Perceived' and 'Weighted', thanks to the fact that we're unaware of certain risks, or 'filter', 'manipulate' or 'model' risks in such a way that we can be 'mathematical confident'. In other words, we've become victims of the "My calculator tells me I'm right! - Effect".

Risk Consciousness Fallacy
This way of taking risk based decisions has the 'advantage' that practice will prove it's never quite right. Implying you can gradually 'adjust' and 'improve' or 'optimize' your decision model endlessly.
Endlessly, up to the point where you've included so much new or adjusted risk sources and possible impacts, that the degrees in freedom of being able to take a 'confident' decision have become zero.


Risk & Investment Management Crisis
After a number of crises - in particular the 2008 systemic crisis - we've come to the point that we realize:

• There are much more types of risk than we thought there would be
• Most type of risks are nonlinear instead of linear
• New risks are constantly 'born'
• We'll not ever be able to identify or significantly control every possible kind of risk
• Our current (outdated) investment model can't capture nonlinear risk
• Most (investment) risks depend heavily on political measures and policy
• Investment risks are more artificial and political based and driven, than statistical
• Market Values are 'manipulable' and therefore 'artificial'
• Risk free rates are volatile, unsure and decreasing
• Traditional mathematical calculated 'confidence levels' fall short (model risk)
• As Confidence Levels rise, Confidence Intervals and Value at Risk increase

Fallacy
One of the most basic implicit fallacies in investment modeling, is that mathematical confidence levels based on historical data are seen as 'trusted' confidence levels regarding future projections. Key point is that a confidence level (itself) is a conditional (Bayesian) probability .

Let's illustrate this in short.
A calculated model confidence level (CL) is only valid under the 'condition' that the 'Risk Structure' (e.g. mean, standard deviation, moments, etc.) of our analysed historical data set (H) that is used for modeling, is also valid in the future (F). This implies that our traditional confidence level is in fact a conditional probability : P(confidence level = x% | F=H ).

Example

• The (increasing) Basel III confidence level is set at P( x ∈ VaR-Confidence-Interval | F=H) = 99.9% in accordance with a one year default level of 0.1% (= 1-99,9%).
• Now please estimate roughly the probability P(F=H), that the risk structure of the historical (asset classes and obligations) data set (H) that is used for Basel III calculations, will also be 100% valid in the near future (F).
• Let's assume you rate this probability based on the enormous economic shifts in our economy (optimistic and independent) at P(F=H)=95% for the next year.
• The actual unconditional confidence level now becomes P( x ∈ VaR-Confidence-Interval) = P( x ∈ VaR-Confidence-Interval | F=H) × P(F=H) = 99.9% × 95% = 94.905%

Although a lot of remarks could be made whether the above method is scientifically 100% correct, one thing is sure: traditional risk methods in combination with sky high confidence levels fall short in times of economic shifts (currency wars, economic stagnation, etc). Or in other words:

Unconditional Financial Institutions Confidence Levels will be in line with our own poor economic forecast confidence levels. 



A detailed Societe Generale (SG) report tells us that not only economic forecasts like GDP growth, but also stocks can not be forecasted by analysts.


Over the period 2000-2006 the US average 24-month forecast error is 93% (12-month: 47%). With an average 24-month forecast error of 95% (12-month: 43%), Europe doesn't do any better. Forecasts with this kind of scale of error are totally worthless.

Confidence Level Crisis
Just focusing on sky high risk confidence levels of 99.9% or more is prohibiting financial institutions to take risks that are fundamental to their existence. 'Taking Risk' is part of the core business of a financial institution. Elimination of risk will therefore kill financial institutions on the long run. One way or the other, we have to deal with this Confidence Level Crisis.

The way out
The way for financial institutions to get out of this risk paradox is to recognize, identify and examine nonlinear and systemic risks and to structure not only capital, but also assets and obligations in such a (dynamic) way that they are financial and economic 'crisis proof'. All this without being blinded by a 'one point' theoretical Confidence Level..

Actuaries, econometricians and economists can help by developing nonlinear interactive asset models that demonstrate how (much) returns and risks and strategies are interrelated in a dynamic economic environment of continuing crises.

This way boards, management and investment advisory committees are supported in their continuous decision process to add value to all stakeholders and across all assets, obligations and capital.

Calculating small default probabilities in the order of the Planck Constant (6.626 069 57 x 10^-34 J.s) are useless. Only creating strategies that prevent defaults, make sense.

Let's get more confident! ;-)

Sources/Links
- SG-Report: Mind Matters (Forecasting fails)
- Are Men Overconfident Users?

Where are we heading with Europe? Let's examine some main topics that illustrate Europe's future direction.

EU Budget Development
The European budget comprises roughly 130,000 Million Euros yearly. The ongoing (2013) budget process in terms of 'net contributions' (= Benefits -Contribution) per country is not transparent. Only the figures until 2011 are available.

Let's take a look a the net contribution development from the start of Europe (2000) until the last available data in 2011. Countries that (net) receive money are 'placed above the x-axis, countries that (net) have to pay money are placed below the x-axis.


Clear is that the amount of money that is transferred from the North&West European countries to the South&East European countries is growing fast from around 15,000 Million Euro in 2000 to around 34,000 Million Euro in 2011.

In a stable Europe the net contributions should decline and vary. However, the opposite is the case!

Who Pays, Who Receives?
It's interesting to look at which countries in Europe are financing other countries.

Take a look at the 2011 transfer of money between all European countries. To create a clear overview, the numbers are rounded in Millions. As a consequence some rows or columns  might not exactly sum up to their total.


To illustrate this table:

• In 2011 Poland received 10,975 Million Euro, of which The Netherlands payed 711 Million Euro of the total of 2,214 Million Euro The Netherlands (net) payed
• Germany payed a net total of 9,003 Million Euros, of which 1,217 Million euro was payed to Greece

It really gets frightening when we analyse the total amount of money transferred between 2000 and 2011:


This table tells us inter alia:

• Over the period 2000-2011 a total amount of 253,119 Million Euro was transferred from North&West to South&East Europe. The end of this imbalanced flow of money is not yest in sight. The future looks dark, due to the current crises in Greece and Cyprus, as well as the worrying situation in Spain and Portugal. 
• Spain has already received 63,563 Million Euro of which 22,115 Million Euro was payed by Germany.
• Germany pay 88,067 Million Euro to support other structural failing European countries, and will continue to do so...... 

Declining Support
On top of the bad financial perspective as presented above, PEW Research recently published, the next survey results:


Only in Germany a small majority still 'believes' in Europe... Other European countries have given up on Europe. Overall, the support for an 'Integral Europe' is declining...

Let's end with the trust European countries have in each other. I leave the trivial conclusions up to the intelligent readers of this blog.. Please don't laugh, while examining the outcomes.


Conclusion
Europe..., how long will it last....???????

 Sources/Links
- Spreadsheets with data used in this blog
- EU Budget 2011
- Folketinget: Net EU Contributions
- PEW Research: The New Sick Man of Europe: the European Union

• Every AIFM needs to have an adequate documented risk management policy, covering all possible risks faced by the AIFs
• Every AIFM has to set quantitative and qualitative risk limits for each AIF for all possible kind of risks 
• An AIFM's Risk Measurement Procedure should include requirements for: backtesting, stress-testing, scenario analyses and the rules should describe remedial action plans when limits are breached. 

• undefined systemic risks are often beneath the analyse surface
• (re)hedged risks could be part of a fatal unknown or unapparent self-reference hedge cycle
• in-depth 'fund of funds' management is time consuming and presumes that risk profiles of sub-funds are available, when in practice they are often not

• It's scientifically proven that women are better drivers, have just as much car accidents as men, but cause less damage. That's a fact and that's why car insurance for women is cheaper than for men. 
• As from December 21, 2012, European insurers are not allowed to 'discriminate' anymore by gender, implying equal car insurance premiums for men and women. 
• If insurers calculate this premium as the weighted average of their portfolio, women are obliged to pay (much) more premium than before and also more than actually and actuarially necessary regarding their gender group. 
• Therefore women are de facto discriminated, although the genuine intention was NOT to discriminate!
• Not only women, but also insurers are discriminated as they now will be faced with anti-selection: Relatively more men will choose an insurance cover, as car insurance premiums for men have become less than the premium corresponding with the expected damage for their gender group. Insurers will therefore face a loss on car insurance. 
• Based on solvency legislation, the insurer will (next) be 'forced' to increase the average weighted premium. This - in turn - is at odds with the measures envisaged by the European Court. 
• A similar kind of reasoning applies also for unisex rates for pension and life insurance.
• The upcoming (2014) US health care law will also prohibit “gender rating”. However, gaps persist in most states. There seem to be no signs of insurers that have taken steps to reduce them.

1. He answered: "Go to the top floor, tie a long piece of string to the barometer, let it down 'till it touches the ground and measure the length of the string".
   
   The examiner wasn't satisfied, so they decided to interview the guy: "Can you give us another method, one which demonstrates your knowledge of Physics ?"
    
2.  "Sure, go to the top floor, drop the barometer off, and measure how long before it hits the ground……"
   
   "Not, quite what we wanted, care to try again ?"
    
3. "Make a pendulum of the barometer, measure its period at the bottom, then measure its period at the top……"
   
   "..another try ?…."
    
4. "Measure the length of the barometer, then mount it vertically on the ground on a sunny day and measure its shadow, measure the shadow of the skyscraper….."
   
   "….and again ?…."
    
5. "walk up the stairs and use the barometer as a ruler to measure the height of the walls in the stairwells."
   
   "…One more try ?"
    
6. "Find where the janitor lives, knock on his door and say
   'Please, Mr Janitor, if I give you this nice Barometer, will you tell me the height of this building ?"

Warning!
Keep in mind that not every method leads to satisfactory results.
An uncertainty analysis of determining a building height using a barometer, developed by Israel Urieli, shows that this method is not accurate at all!

So the surprising news is that the first two alternative methods mentioned above are more accurate than the method you learn at high school.