Short Term Longevity Risk

As well-born actuaries we all know the long term risks of longevity:

• Life Expectancy variance Monster
• Longevity Swaps: The Next Bubble
• Coverage Ratio Solution Space
• Exceptional Longevity Predictable

Lots of actuaries keep expending their energy on calculations of 50 years ahead mortality probabilities....  And indeed..., this is challenging....

Some research reports predict a decline in life expectation, others and more serious recent reports show a steady increase of life expectation.

Mission Impossible

Fact of actuarial life is that - although long term research is useful and educational - we are no Actuarial Magicians.

We should never suggest that we're able to value a bunch of complex and systemic risks  (liabilities, assets,mortality, costs, demographics, etc) into a reliable consistent model that predicts reality.

It's a farce!

What CAN we do?

Instead trying to compress a complex of long term risky cash flows into one representing unique value, we need to:

1. Analyze and model the short term risks
2. Develop a method (system) that enables boards of directors to manage and control their risky cash flows (profit share systems, experience rating, etc.).

Example: Short Term Longevity Risk

As a 2011 report of the National Research Council clearly shows:  The previous 50 years we've seen a 3 months yearly increase of lifespan every calendar year.

Instead of recalculating, checking and pondering this trend, let's take a look at the short term effects of this longevity increase trend.

Effect of 'one year life expectancy' increase 
First we take a look at the cost effect of the increase of 'one year of life expectancy' on a single-premium of a (deferred) life annuity (paid-up pensions)...
( Life table total population: United States, 2003 )

Depending on the discounting interest rate, a one year improvement of longevity for a 65 old person demands a 2,3% to 4,0% increase of the liabilities.

Of course the increase of the liabilities of a portfolio (of a pension fund) depends on the (liability weighted) age distrubution of the corresponding portfolio.

Here's a simple example:

This comes close to the rule of thumb as mentioned by AEGON:

10% mortality improvement adds one year to life expectancy, and one year of life expectancy adds 4% to the required value of a pension fund’s reserves

Conclusion

From the above presented visual sensitivity analysis we may conclude that for general (distributed) portfolio's a 'one year lifetime increase' will demand approximately 4-5% of the actual liabilities.

A three to four months yearly longevity-increase - as is still the actual trend - will therefore demand roughly a substantial 1,5% (yearly) of the liabilities.
This implies that in case your contribution is calculated at 4% and your average portfolio return is 7%, there's 3% left for financing longevity and indexation (=method). As 'longevity growth' in the near future will probably cost about 1,5%, there's  only 1,5% left for indexation on the long run.


Case closed


Related links:
-  Spreadsheet (xls) with data used in this blog
- Forecasting longevity of Dutch pension scheme members using postcodes
- Increasing life expectancy at pension funds (uvt;2011)
- Life Tables for the United States Social Security Area 1900-2100
- Valuing Pension Fund Liabilities on the Balance Sheet
- No limits to life expectancy?
- Broken Limits to Life Expectancy
- NRC: Explaining divergent levels of longevity (pdf;2011)
- Wolfram Alpha: Longevity U.S.
- AEGON: Longevity Rule of thumb

Google Hits on Actuary

Google can be a great help for actuaries. Especially 'Google Insights' and 'Google Trends' are two useful applications for retrieving relative Google Search Hits data from the Internet.

Google Insights Example
Let's dive a little deeper into Google Insights and start with researching the relative development of the number of hits on the word 'Actuary'.
Here is the result (period 2004-2011-May, extracted csv-file, Excel-Graph):

Explanation
The numbers on the graph reflect how many searches have been done for a particular term (e.g. 'Actuary'), relative to the total number of searches done on Google over time. They don't represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100. Each point on the graph is divided by the highest point, or 100.

Conclusion

Clear is that the search for (the word) actuary is relatively declining from 2004 to May 2011.

To keep the actuarial profession virtually alive we'll need to make more noise as actuaries on the Internet.

Step outside, spread the (acturial) word, make yourself visible in the outer world and let people wonder:  'who's that?',  'what a professional', 'what's his job?', 'Actuary?', 'I will google it!'.

So let's Twitter and Blog to get more actuarial exposure...


Actual Data

Apart from generating these kind of relative time-data, Google Insights can generate actual data anywhere on any web-application or presentation.

This way your data will always be up to date!
Moreover Google Insights is easy to handle without any code knowledge.....


Some examples....

(1) Actual relative development of the number of hits on the word 'Actuary'


(2) Top searches and rising searches on Google for the word 'Actuary'

More applications

The next example shows how you may use Google Highlights as a market crash predictor.  

It turns out that in advance of the 2008 market crash, Google searches on "Stock market crash" increased...

Make you own discoveries, highlights or trends (e.g 'Solvency II') and enjoy!


Related Links:
- Actuaries on Twitter
- Google Insights 
- S&P 500 Data 
- How Google Trends and Internet Searches Correlate with Asset Prices
- Google trends: 21 May 2011: End of the world, predicted by Harold Camping

Actuarial Proverbs: Will Europe Survive?

According to Eurostat, Europe - especially the Euro (€) 'Coin' Countries that put all their Euro eggs in one basket -  face a difficult time. In a world where money seems to grow on trees, it's hard to take the right measures to prevent Greece from a financial meltdown with unknown consequences.

Questions
Even for actuaries it's hard to understand what's happening and what makes sense or not, It's over our 'actuarial' head....

• Should 'Europe donor countries' support Greece fore more than the '110 billion Euro rescue' in 2010?
  
  
• Is Greece’s 10-year bond rate of 15% an adequate risk premium?
  
  
• Will restructuring Greece's debt solve anything, devaluate the Euro,  or pose other  incalculable risks to the overall Euro zone?

Difficult questions that are hard too answer....


Debt-Deficit Comparison
Let's take an actuarial look at the facts by comparing 2010 Government Debt with Deficit (all in % GDP):

From this chart it's clear that not only Greece is in the danger zone, but also Ireland and the US as well... Moreover, the UK is not free from worries, to put it mildly...

The blind are leading...
Another chart-conclusion might be that the blind are leading the blind'. Relative strong less-weaker countries like Germany and France,  have to carry the financial consequences of cheating and not-performing countries. Above all, we all know: one rotten apple spoils the barrel!!

In fact to save or revive 'Financial Europe' it would take some countries with no debt and a strong positive surplus (= negative deficit) instead of a deficit.

It seems neither sensible nor logical  to restructure another  country's debt if the outlook of the governments debt and deficit of the' helping country' is (slightly less) negative as well. But as we know: only fools rush in where angels fear to tread.

Trying to help other countries that fail to restructure themselves is like banging your head against a brick wall...  No risk premium on government bonds can compensate that...

Countries with a strong relative debt and a deficit should restructure their own country and financial situation at once, before asking ore receiving any outside help.

Growth: The Solution?
Some argue that debt and deficits are not so bad as long as countries are growing. Let's dive into this argument with the next chart (data source: Eurostat):

Indeed, from this 'Growth-Believe' we can now understand why (only) Greece is seen as such a major problem.

From this chart it's also clear that if Ireland and Spain are not going to grow one way or the other, they will become the next big problem. These countries have to take the bull by its horns, before it's too late.

It's throwing caution to the wind when 'debt and deficit countries' with a positive 'Real GDP Growth Rate' try to save sicker country-brothers by lending them money.

Moreover, it's lending money you don't really possess or own, it's like robbing Peter (yourself) to pay Paul....

Combining the two Eurostat charts it becomes clear that that not all 'Garlic Countries' (Mediterranean countries:Greece, Spain, Portugal, Italy) can be lumped together.

Greece is indeed the greatest risk , secondly a non-garlic country: Ireland...
Spain, Portugal and Italy are relatively at arm’s length and could perhaps keep their head above water if they take the right measures in time.

U.S.' Fiscal Gap

Finally, don't forget about the U.S., as the U.S. Real GDP Growth Rate is already declining to 2.3% in Q1 2011.

According to Boston University economist Kotlikoff, the U.S. is broke.  Kotlikoff doesn’t trust government accounting. He uses “Fiscal Gap,” not the accumulation of deficits, to define public debt. This "Fiscal Gap" is the difference between a government’s projected revenue  and its projected spending .

By this measure, the U.S. government debt is $200-trillion – 840 percent of current GDP. 

Conclusions

From all this it's clear Europe is stuck between a rock and a hard place...
Although ECB President Mr. Trichet thinks different, it looks like €-Europe has to choose between two blind goats (Irish saying):

(1) A complete Financial Europe Meltdown in case of endless financing default countries like Greece or

(2) Letting individual default countries go bankrupt, with unsure (systemic) consequences for local banks and other financial institutions that financed or invested in default countries.

How to decide? Guideline:  Of two evils, always choose the less....
As option (1) is clearly putting the cart before the horse, and surely leads to a meltdown, only option 2 is left: QUIT!

Sources and related links:
- Spreadsheet: Used Data, Tables for this blog (xls)
- US Real GDP Growth Rate
- Government Debt and Optimal Monetary and Fiscal Policy (2010)
- English proverbs and sayings (!)
- English deficit (including time table)
- Shadowstats (for the real stats!)
- The U.S. is broke?
- Eurostat: Euro area government deficit at 6.0% GDP (2011) 
- BILD: Interview with Jean-Claude Trichet, President ECB, 15 January 2011

Oversized Supervision?

In April 2011 EIOPA  published  the findings of its 2010 survey:

 REPORT ON REPORTING REQUIREMENTS TO SUPERVISORY AUTHORITIES

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....

Homo Actuarius Bayesianis

Bayesian fallacies are often the most trickiest.....

A classical example of a Bayesian fallacy is the so called "Prosecutor's fallacy" in case of DNA testing...

Multiple DNA testing (Source: Wikipedia)

A crime-scene DNA sample is compared against a database of 20,000 men.

A match is found, the corresponding man is accused and at his trial, it is testified that the probability that two DNA profiles match by chance is only 1 in 10,000.


Sounds logical, doesn't it?
Yes... 'Sounds'... As this does not mean the probability that the suspect is innocent is also 1 in 10,000. Since 20,000 men were tested, there were 20,000 opportunities to find a match by chance.

Even if none of the men in the database left the crime-scene DNA, a match by chance to an innocent is more likely than not. The chance of getting at least one match among the records is in this case:



So, this evidence alone is an uncompelling data dredging result. If the culprit was in the database then he and one or more other men would probably be matched; in either case, it would be a fallacy to ignore the number of records searched when weighing the evidence. "Cold hits" like this on DNA data-banks are now understood to require careful presentation as trial evidence.

In a similar (Dutch) case, an innocent nurse (Lucia de Berk) was at first wrongly accused (and convicted!) of murdering several of her patients.

Other Bayesian fallacies
Bayesian fallacies can come close to the actuarial profession and even be humorous, as the next two examples show:

1. Pension Fund Management
   
   
   It turns out that from all pension board members that were involved in a pension fund deficit, only 25% invested more than half in stocks.
   
   Therefore 75% of the pension fund board members with a pension fund deficit invested 50% or less in stocks.
   
   From this we may conclude that pension fund board members should have done en do better by investing more in stocks....
   
   
2. The Drunken Driver
   It turns out that of from all drivers involved in car crashes 41% were drunk and 59% sober.
   
   Therefore to limit the probability of a car crash it's better to drink...

It's often not easy to recognize the 'Bayesian Monster' in your models. If you doubt, always set up a 2 by 2 contingency table to check the conclusions....

Homo Actuarius
Let's  dive into the historical development of Asset Liability Management (ALM) to illustrate the different stages we as actuaries went through to finally cope with Bayesian stats. We do this by going (far) back to prehistoric actuarial times.
 

As we all know, the word actuary originated from the Latin word actuarius (the person who occupied this position kept the minutes at the sessions of the Senate in the Ancient Rome). This explains part of the name-giving of our species.

Going back further in time we recognize the following species of actuaries..

1. Homo Actuarius Apriorius
   This actuarial creature (we could hardly call him an actuary) establishes the probability of an hypothesis, no matter what data tell.
   
   ALM example: H₀: E(return)=4.0%. Contributions, liabilities and investments are all calculated at 4%. What the data tell is uninteresting.
   
   
2. Homo Actuarius Pragmaticus
   The more developed 'Homo Actuarius Pragamiticus' demonstrates he's only interested in the (results of the) data.
   ALM example: In my experiments I found x=4.0%, full stop.
   Therefore, let's calculate with this 4.0%.
   
   
3. Homo Actuarius Frequentistus
   In this stage, the 'Homo Actuarius Frequentistus' measures the probability of the data given a certain hypothesis.
   
   ALM example: If H₀: E(return)=4.0%, then the probability to get an observed value more different from the one I observed is given by an opportune expression. Don't ask myself if my observed value is near the true one, I can only tell you that if my observed value(s) is the true one, then the probability of observing data more extreme than mine is given by an opportune expression.
   In this stage the so called Monte Carlo Methods was developed...
   
   
4. Homo Actuarius Contemplatus
   The Homo Actuarius Contemplatus measures the probability of the data and of the hypothesis.
   
   ALM example:You decide to take over the (divided!) yearly advice of the 'Parameters Committee' to base your ALM on the maximum expected value for the return on fixed-income securities, which is at that moment  4.0%. Every year you measure the (deviation) of the real data as well and start contemplating on how the two might match...... (btw: they don't!)
   
   
5. Homo Actuarius Bayesianis
   The Homo Actuarius Bayesianis measures the probability of the hypothesis, given the data.  Was the  Frequentistus'  approach about 'modeling mechanisms' in the world, the Bayesian interpretations are more about 'modeling rational reasoning'.
   
   ALM example: Given the data of a certain period we test wetter the value of H₀: E(return)=4.0% is true : near 4.0% with a P% (P=99?) confidence level.

Knowledge: All probabilities are conditional
Knowledge is a strange  phenomenon...

When I was born I knew nothing about everything. When I grew up learned something about some thing. Now I've grown old I know everything about nothing. Joshua Maggid

The moment we become aware that ALL probabilities - even quantum probabilities - are in fact hidden conditional Bayesian probabilities, we (as actuaries) get enlightened (if you don't : don't worry, just fake it and read on)!

Simple Proof: P(A)=P(A|S), where S is the set of all possible outcomes.

From this moment on your probabilistic life will change.

To demonstrate this, examine the next simple example.

Tossing a coin

• When tossing a coin, we all know: P (heads)=0.5
• However, we implicitly assumed a 'fair coin', didn't we?
• So what we in fact stated was: P (heads|fair)=0.5
• 
  Now a small problem appears on the horizon: We all know a fair coin is hypothetical, it doesn't really exist in a real world as every 'real coin' has some physical properties and/or environmental circumstances that makes it more or less biased.
• We can not but conclude that the expression
  'P (heads|fair)=0.5'  is theoretical true, but has unfortunately no practical value.
• The only way out is to define fairness in a practical way is by stating something like:  0.4999≥P(heads|fair)≤0.5001
• Conclusion: Defining one point estimates in practice is practically  useless, always define estimate intervals (based on confidence levels).

From this beginners  example, let's move on to something more actuarial:

Estimating Interest Rates: A Multi Economic Approach

• Suppose you base your (ALM) Bond Returns (R) upon:
  μ= E(R)=4% and σ=2%
  
  
• Regardless what kind of brilliant interest- generating model (Monte Carlo or whatever) you developed, chances are your model is based upon several implicit assumptions like inflation or unemployment.
  
  The actual Return (R_t) on time (t) depends on many (correlated, mostly exogenous) variables like Inflation (I), Unemployment (U), GDP growth(G), Country (C) and last but not least  (R_[t-x]).
  
  A well defined Asset Liability Model should therefore define (R_t) more on basis of a 'Multi Economic Approach'  (MEA) in a form that looks more or less something like: R_t = F(I,U,G,σ,R_[t-1],R_[t-2],etc.)
  
  
• In discussing with the board which economic future scenarios will be most likely and can be used as strategic scenarios, we (actuaries) will be better able to advice with the help of MEA. This approach, based on new technical economic models and intensive discussions with the board, will guarantee  more realistic output and better underpinned decision taking.

Sources and related links:
I. Stats....
- Make your own car crash query
- Alcohol-Impaired Driving Fatalities (National Statistics)
- D r u n k D r i v i n g Fatalities in America (2009)
- Drunk Driving Facts (2006)

II. Humor, Cartoons, Inspiration...
- Jesse van Muylwijck Cartoons (The Judge)
- PHDCOMICS
- Interference : Evolution inspired by Mike West

III. Bayesian Math....
- New Conceptual Approach of the Interpretation of Clinical Tests (2004)
- The Bayesian logic of frequency-based conjunction fallacies (pdf,2011)
- The Bayesian Fallacy: Distinguishing Four Kinds of Beliefs (2008)
- Resource Material for Promoting the Bayesian View of Everything
- A Constructivist View of the Statistical Quantification of Evidence
- Conditional Probability and Conditional Expectation
- Getting fair results from a biased coin
- INTRODUCTION TO MATHEMATICAL FINANCE

Humor: Scrambled Actuarial Reporting

Some actuaries are convinced that adding more important details really helps. With more details and more information you are able to explain you models better and as we all know: better communication is key in actuarial science.


Here is an example of detailed information (click on the image!)

Some(times) details don't matter
Unfortunately more information and more details generally disturb efficient decision making. The next text shows that some details don't really matter.

Smoe acaruites are covcnined taht adding mroe imnrpotat deaitls rlaely hleps. Wtih more dleitas you are albe to eplaxin you mlodes bteter and as we all konw: btteer cmniutcoiaomn is key in aratiuacl sieccne.

Sirnpigrulsy tihs is not ture. Tihs txet sowhs taht smoe daeilts dno't rlaley mttear.

The arutacial aidnceue isn't rlaley istretneed in the daeilts, but in caelr ipunt (fsrit ltteer of a wrod) and oumotces (last letetr of a word). The dtilaes (letetrs) in bweteen can be mexid up in evrey rodnam oerdr you lkie. Keep in mnid tihs iponmatrt lsosen in your nxet peeiatntsorn.

Explanation
According to a study at Cambridge University, to read and understand a text well, it doesn't matter in what order the letters in a word are placed. The only condition is that the first and last letter of each word remain the same. The rest can be a total mess up. This is because the human mind does not read every letter by itself but the word as a whole.

DIY
Let's conclude with an 'example text' for the opening-slide of you next board presentation:

Daer Board mrebmes,

Agtlhouh we hvae to tkae fetdanmaunl dniecioss tdoay, it wlil not be ncseresay to udasnertnd or dcssius all knid of tcihcenal dtileas.

The relust of my avicde is pertseend in scuh a way as to esurne taht we can stcik to the mian ptinos and hneieadls.

The vrey fcat that you wree albe
to raed and udnreastnd tihs txet,
greauetans taht we wlil hvae a
sefscuucsl mtineeg.

Yuor aivdosr

Scramble your own opening-slide text for your next presentation at:

Words Scrambler

No doubt, your next report will be actuarial scrambled.... ;-)

Related sources and links
- Words Scrambler
- MRC Cognition and Brain Sciences Unit
- All My Faves

The Ten Actuarial Commandments

We all (think to) know The Ten Commandments from the holy scripts by heart, do we?

Now close your eyes to see how far you can get in quoting those simple ten guidelines in life.............

The Ten Commandments for Investors
Just like the Ten Commandments for Man, God - more specific - created The Ten Commandments for Investors. Let's compare the two, while - at the same time - you can check out your Commandment-Memory on Man as well:

Risk-Return-Supervision Development
As you may have noticed, The Ten Commandments are a mix of rules-based and principles-based principles.

Just as in our own life, it's interesting to see how we apply and implement these two different kind of rules during the evolution of a financial institution (insurance company, pension fund, bank, etc.):

In time, the ideal supervision model consists of three phases:

• Phase I: No rules
  In this phase we cannot value or the company. Chances are substantial the company is 'at risk'.
  
  
• Phase II: Rules-Based Supervision
  In phase Ia 'Rules' are mostly perceived as 'Have to's" . As a result Risk will be reduced, but Return as well. Once the board, actuaries and financial specialists are becoming aware and will see the advantages and new possibilities of managing risk. 'Have to's" will develop into 'Want to's" . The Risk-Return Ratio will increase  and even a better Return will result.
  
  
• Phase III: Principles-Based Supervision
  Just like with the implementation of Rules-based Supervision, in case of Principles-Based Supervision, the Financial Institution needs time to adept to the new situation. At first there might be a unbalance between Risk and Return. It takes time to calibrate Risk and Return again.
  
  After a while actuaries, investors and management will translate Rules-Based principles into own rules that fits the company's specific risk in an optimal way. The company will be able to take more risk and to optimize its own Risk-Return Ratio.

Take a look at your own company's development and see for yourself where you fit in on the Risk-Return-Supervision lines....

It might be possible that you have to conclude that you aren't able to increase your Risk-Return ratio in the end. In this case it's likely you've become (so called) 'Supervisory Compliant': Your risk appetite probably corresponds more or less with the supervisor's minimal risk view. Instead of redefining your own risk appetite and restructuring your products from a risk-management perspective you merely implied new regulations and supervisor guidelines. As a result your Return and Risk-Return Ratio implode....

Ten Actuarial Commandments
Having learned the possible effects of supervisory rules in practice, we may now conclude with The Ten Commandments for Actuaries.

The Ten Commandments for Actuaries

1. There's only one God, as he's omnipotent he's also an actuary.
   As you're only an actuary: be humble.....    Remember: As God wants something in Return, you'll have to take Risk!!
2. Reality can't be comprised in a model.
   Use your brains. A model is a help, not a decision machine. Don't mix up God with Risk or Chaos. Chaos for us humans (actuaries) can be defined as "Unrecognized Order" (quote). 
3. Never blame anything or anyone than yourself for an unexpected or negative outcome.
4. Be consistent, act sustainable. But change your opinion just in time, if circumstances or facts urge you to do so.
5. Alway show respect to others, even if you think different. Appreciate where you come from. Nobody is perfect, not even you.
6. As there is no 'right' model, never criticize other models, actuaries or other people. Try to give your opinion without slaughtering the other.
7. Never advice or state anything you do not really mean or cannot defend.If you're not sure or don't know, tell it or get help.
8. Always cite your sources or give credits to others that helped you.
9. Don't 'steal' the advice.
   Never include the final decision to be taken in your advice. Wrap up arguments, consequences and present scenario's so the board has to make a choice and not you.
10. Don't get carried away by results, reports or performances of others.
    Stick to your own consistent approach.

Apply supervisory rules and actuarial commandments in a conscious way...

Risk Quotes

I'll not even try to give a definition of 'Risk Management'.
More than the word Risk, Risk Management is full of traps and paradoxes.

Just to mention some.....

Risk Management is...

• not primarily about risk that can be calculated with a 99,x% confidence level
• dealing with Risks you know will come, but can't be calculated
• more about correlation in time than mean estimates and standard deviation
• more about prevention, foreseeing and managing risk than capitalisation of risk
• more about taking risks to benefit, than trying to exclude risks
• fighting risk instead of excluding risk
• making Plans B and C, in case your confidence level fails

Avoiding Risk

One of the trickiest parts of Risk Management is that we often  are trying to avoid Risk at any price.

By doing so, we introduce a new risk: It gets harder to achieve shareholder and client value.

Often returns will decline because of over-capitalisation and a risk-return unbalance.

Finally we have to compete in new risk areas we're not experienced in. 

It's all well expressed in a cartoon on cartoonstock:

'We've considered every potential risk, except the risks of avoiding all risks.'


Personally I prefer the challenging Risk Management quote of Jos Berkemeijer, that states:

 "Risk Management: the art of foreseeing hindsight."

Better than trying to define Risk or Risk Management, it is to study and get inspired by Risk Quotes.

Therefore I conclude this Blog with a link to the Blog 'Risk Quotes'

You can place a random quote like this one:

Random Risk Quote

on your website or Blog by copying the next javascript code.

<script type="text/javascript" src="http://goo.gl/WdMOK"></script>

Install Instructions

1. Copy above JavaScript code (select;CTRL-C).
2. Paste (CTRL-V) the code on your webpage or Blog
   Blogger: Go to Design Tab, Click on Add a gadget;
   Click on 'HTML/Javascript' Gadget
   Paste the above code in the gadget and Save. 

Related Links
- Risk Quotes
- Riskviews: quotes
- Best Risk Management Quotes
- Death of Risk Management

Actuary beats Chimp (or not?)

Is your joy for stats just as big as Hans Rosling's enthusiasm shows?

Just watch how Hans tells the developing story of Lifespan against Income in more than 200 years, showing around 200 countries in a 120.000 numbers flowing chart!



It's clear, we actuaries can learn from Hans with his (1) 'sticky enthusiasm' and (2) 'keeping the issue on headlines'.....


You can play for your own with the data Hans uses on Gapminder World

No doubt..., your next board presentation will be dynamic and flowing.

To conclude... If you as an actuary think you know more about the actuarial world than a chimp, please take Hans Rosling's

Beat the Chimp test

on facebook.


Let's hope for the 'best'.....

Related links:
- Gapminder World

Fun: Actuarial Dasboard Crash

Last week I gave a Risk Management training about pension funds. After illustrating several times the importance of adequate risk management dashboards, one of the attendees suddenly stated:

'No matter how impressive your dashboard, you should keep your eyes on the road!'........

Right he was! A driver  who's constantly focused on his dashboard will sooner or later end up in the bush and finally crash.

We, actuaries and risk managers all trust on our dashboards, but at the same time we should keep our eyes open to anticipate on coming events in a changing marketplace.

Sometimes it's even better to just leave the road, as the next video shows...


Police Risk management

David | Myspace Video

Anyhow, keep your eyes open, to prevent an Actuarial Dashboard Crash.....

Related Links:
- Alfa Romeo Spider Veloce: Don’t Let Dashboards Drive You Crazy

Stress test stress test

How did you interpret the title of this Blog......? 

Can you read it in more than one way? In how many ways can you read it?

Still confused?

Enough questions to start this blog.

In short: the more ways you are able to read this title, the more successful you'll probably be in defining and executing 'Stress Tests" in practice.

Let's dive a little deeper to illustrate this important 'multi interpretation talent' you need, to make stress testing a success.

Although there are far more ways (please add some interesting suggestions as comments to this blog) to interpret the title 'stress test stress test', we'll analyze in this blog two interesting  interpretations that follow from the fact that 'stress test' can be interpreted as a noun or as a verb.

1. stress test [noun] stress test [noun]

Interpreting the title as two nouns could:

• make you aware of the importance of stress tests
• emphasize the importance of repetitive execution of stress tests
• illustrate the feeling of disinterest and apathy that occur when important words are repeated to often without enough plowing depth..
  
  

2. stress test [verb] stress test [noun]

This perhaps the most interesting interpretation.
How can you really stress test your stress test?  

The way we stress test at financial institutions like banks, insurance companies and pension funds, is basically more or less as follows:

• Project historical crisis crash-data into the future. Simulate what would happen and take a look at the consequences
  
  
• Test crash scenarios on basis of the question: What would happen if.... (prices go down, S&P 500 collapses, etc., etc.)
  
  
• Take several economic scenarios. Project them on your balance sheet and see what happens.

To stress-test a stress test we have to develop a different view on stress tests.

A view based on the answer of the next leading question:

How many sides has a coin?

Let's demonstrate this new crucial view on a stress test.

A Different View on Stress Tests
Some examples:

• Inverted Stress Test
  An interesting way of stress testing is 'working the other way around': Try to define financial situations where you never want to end up in (e.g. equity< -5%, etc.) and try to imagine scenarios that could lead to this unwanted financial situation  Paul Duijsens, ALM Principal Mercer Investment Consulting, mentions this approach).
   
• Idiot Proof Stress Test
   Andrea Enria, chairman of Europe’s new banking regulator stated recently:
  
  A stress test is only as good as the scenarios you plug into it
  
  Therefore, make stress tests 'idiot proof' as much as possible.
  Once a stress test is developed, don't present it to the board directly. Organize a 'second opinion' from a professional company that's undependable and critical enough to seriously test and analyze your stress test and its assumptions.
  
  Presenting a stress test without clear statements about the limits, vulnerabilities, constraints and shortcomings (every test has shortcomings) is like playing with fire and  offering your board 'the wrong end of the stick'.
  
  So we can add another conclusion quote:
  
  A good stress test transparently presents its weaknesses
  
  If nobody can find a weak spot spot in your stress test: ask a 5 year old child to ask some simple questions.....
  
  
• Compare Stress Tests
  Please  think about :
  
  'One stress test' = 'No stress test'
  
  Comparing successively executed stress tests on assumptions, methods and outcomes is essential for a correct understanding of the impact and consequences.
  
  Not only compare tests of your own company, compare also with tests of other financial institutions. Questions like: why do we as a [pension fund]  have different assumptions and methods than an international [bank], are key to a correct understanding of your own risks.
  
  
• Surpass Regulator Constructed Stress Tests
  Regulator Constructed Stress Tests(RCS-tests) seem relatively easy to implement.
  As a risk manager or board member you don't have to think about scenarios or methods. That's all been taken care of by the Regulator. Easy, isn't it?......... Wrong!
  
  Heedlessly implementing RCS-tests is risky. First of all, the regulator's principles are partly biased. As an example, take the risk of treasury bonds on your balance sheet.  Treasury bonds are commonly seen (and valued) as save (AAA-rating). However, some countries (Greece, Spain,Ireland etc.) have already been downgraded. Which countries will follow? Does the RCS-test includes this non-hypothetical risk? No? Does your own test includes this risk?
  

• By definition regulators have to act and communicate in a responsible and 'prudent' way about government financial issues. If they wouldn't, world wide financial chaos would be the inevitable result.
  
  
  The other side of this 'prudent coin' is that the actual risks are probably larger than can be concluded from the government (treasury) interest rates and interest spreads in a particular country. Here you'll have to develop your own risk model or - if data fail - formulate  your own risk approach (get out!).
  
  Key is that, given the general level of  systemic risk, all financial institutions must be able to withstand haircuts on all their own sovereign debt holdings.
  
  A 'third side' of this coin is the fact that regulators (in time) might decrease risks on certain asset categories that are not in line with your own risk view. Stay awake to prevent from becoming 'Supervisory Stress Compliant'.........
  
  

• Unmask Derivatives
  Market valuation with respect to derivatives is tricky business and probably only valid as long as there's a 'normal' market activity. Nobody is able to value derivatives under severe market conditions as is the case in stress tests. So, depending on the size and characteristics of a stress test, don't hesitate to to unmask your derivatives by applying a large discount on the value of your derivatives.
  
  

Conclusion
Stress testing is not for dummies, but for professionals.
It turns out that the more you're able to look different, critical and 'out of the box', the more stress testing will be successful.

Making your audience aware that a coin has three sides instead of two, is probably the essence of an actuary's or risk manager's profession. 

It has become clear that analyzing assumptions, models, outcomes,constraints and shortcomings of a stress test is no superfluous luxury. So stress test your stress test!

Related links:
- KAS BANK develops stress test for UK pension funds
- Concerns over latest EU bank stress tests
- EU bank stress tests, a joke (2011) 
- Lateral Thinking:  US Economy Stress test (2011)
- World Wide Interest Spread by Country (2011) 
- Government Bonds yields 10 Year Notes 
- How many sides has a coin? 
- Worst-Case Scenario Survival Card Game 

Zero Problems Risk Management

More and more we actuaries and risk managers become aware that our risk models can't just be based on numbers and statistics exclusively.

Some examples:

Systemic risk

The recent financial crisis made it clear that a 'mono risk approach' on a sole risk-object (mortgage, fund-investment) is insufficient.

Investments and loans are embedded in a worldwide sea of connected financial instruments and reinvestments. Systemic risk has to be included in our models.

Main challenge here is that systematic risk essentially depends on macroeconomic and (mostly) irrational factors. Further, systemic risk is related to the structure and dynamics of the market. More than numbers.....

Supervisory Herding Risk

In their effort to control and support financial institutions like banks, pension funds and insurance companies, country supervisors, regulators and 'accounting standards boards', defined a meticulously set of guidance rules (Basel I/II/II, Solvency I/II, Qis-I-V, IFRS, FAS, AIFMD, FTK, FIRM, etc.,etc.)

Financial institutions not only confirmed and adopted to those new rules, but - in their rush and driven by cost and time pressure - also implicitly (and often unintentionally) declared those same imposed rules and rationales as their own business 'Risk Appetite'. This way, most financial Institutions became so called: 'Supervisory Compliant'.

Instead of  expliciting their specific company-targets and successively developing their own correspondent risk appetite and risk framework, they incorporated the supervisor's risk philosophy. 

Without a sound own (board) risk vision that would undoubtedly have included some extra safety on 'company specific risk issues', financial institutions became - like a herd - all in the same way extremely vulnerable to (less defined) external risks.

Summarized:

Overregulation increases Herding Risk

Financial institutions all measure and respond to regulated risks in the same way. Supervisory Herding Risk is born.....

Too Much Focus Risk

As a consequence of pre-subscribed risk categories and ruling by law or (accounting) standards, there's the risk of 'too much focus' on specific risks while forgetting, denying or neglecting other important risks. Remember, the devil is in the (correlating) details....

Here's a useful, but not exhaustive, checklist to keep track on your risk models...

Average Premium Risk	Diversification Risk	Matching Risk
Commodity risk	Employer Continuity Risk	Operational risk
Compliance Risk	Environmental Risk	Outsourcing Risk
Compliance Risk	Equity Risk	Oversight Risk
Concentration Risk	Herding Risk	Price Inflation Risk
Counterparty Risk	Interest rate risk	Property Derivatives Risk
Coverage Ratio Risk	IT Risk	Reinsurance Risk
Credit Risk	Legal risk	Reinvestment risk
Culture Risk	Legislative Risk	Reputation risk
Currency Risk	Liability Risk	Sex Calculation risk
Default Risk	Liquidity risk	Strategic risk
Deflation risk	Longevity Risk	System Risk
Disaster Risk	Market Risk	Systemic Risk
Discount Risk	Matching Risk	Wage Cost Inflation Risk

ALM Simplifying Risk

Univariate models are killing and even multivariate models have proven to be too vulnerable and too limited in the recent crisis. It's not just about correlation and covariance matrices. What we need is an self-explaining model. A model  that predicts or generates expected values in an economic context, depending on exogenous economic variables like inflation rate, GDP-Level, etc. and that is based on the same structured historical economic data-set.

We need 'Asset Liability Modeling New Style' and not only Stress Testing or
advanced and excellent Crash Modelling as well explained by EMB.

Geopolitical Risk

With Europe and Japan as recent examples, it's clear that risks come from everywhere around the world.

The consequence of earthquakes (Japan, Australia), a possible  country default (Ireland, Greece, Portugal, ..), political instability (Libya, Ivory Coast, ..), war threat (Vietnam,Iraq, ..), financial easing (US, Europe,...), on our economic system, prices and financial institutions seems substantial and - moreover- predictable.

More than just trying to catch and capitalize these kind of risks in our risk models, we need to develop (financial) mechanisms and products that can cope as best as possible with these kind of risks.

The Riskmap 2011, Managing Risk | Maximising Opportunity, offers a good description of the actual risks that influence our lives and risk models.

A nice example is the recent (unexpected) leading role that France took in action against Libya.  'Riskmap 2011' mentions the 'Arabic Poll 2010' that clearly shows (despite the lack of sympathy for president Sarkozy) the trust and sympathy for France. France clearly outperforms the US and president Obama  unfortunately has lost the trust of the Arabic world... Take a look at the next slide summary (or the original complete pdf):  

Arab Public Opinion Poll 2010 Summary

Arab Public Opinion Poll 20... by Jos Berkemeijer

The Arabic poll shows that the prime minister of Turkey, Erdogan, has clearly gained  the confidence and trust of the  Arabic countries. With Ergodan, Turkey - at the cross road between East and West - takes a leading role in the 'World Risk Management Process'.  Ergodan's Risk Philosophy, invented  by the Turkish Foreign Minister Ahmet Davutoglu,   is 'Zero Problems'.....

Perhaps that should be the philosophy of actuaries too...

Zero Problems

Conclusion

From now on 'Modeling Risk' is more than just a financial exercise.
It's building scenario's, mechanisms and products that can cope with this risky world.  Success as actuary or risk manager!

Related Links:

- Committee (behaviour) assessment tool
- Control Risks:Riskmap 2011
- Arabic Poll 2010
- Supervisory Compliant
- Maplecroft Risk Maps 
- EMB: How to Model a Crash (REVO)