Showing posts with label actuary. Show all posts
Showing posts with label actuary. Show all posts

Oct 23, 2022

Why VaR fails and actuaries can do better

Perhaps the most important challenge of an actuary is to develop and train the capability to explain complex matters in a simple way. One of the best examples of practicing this 'complexity reduction ability' has been given by David Einhorn, president of Greenlight Capital. In a nutshell David explains with a simple example why VaR models fail. Take a look at the next excerpt of David's interesting article in Point-Counterpoint.

Why Var fails
A risk manager’s job is to worry about whether the bank is putting itself at risk in unusual times - or, in statistical terms, in the tails of the distribution. Yet, VaR ignores what happens in the tails. It specifically cuts them off. A 99% VaR calculation does not evaluate what happens in the last1%. This, in my view, makes VaR relatively useless as a risk management tool and potentially catastrophic when its use creates a false sense of security among senior managers and watchdogs.
VaR is like an airbag that works all the time, except when you have a car accident
By ignoring the tails, VaR creates an incentive to take excessive but remote risks.
Example
Consider an investment in a coin flip. If you bet $100 on tails at even money, your VaR to a 99% threshold is $100, as you will lose that amount 50% of the time, which obviously is within the threshold. In this case, the VaR will equal the maximum loss.

Compare that to a bet where you offer 127 to 1 odds on $100 that heads won’t come up seven times in a row. You will win more than 99.2% of the time, which exceeds the 99% threshold. As a result, your 99% VaR is zero, even though you are exposed to a possible $12,700 loss.

In other words, an investment bank wouldn’t have to put up any capital to make this bet. The math whizzers will say it is more complicated than that, but this is the idea. Now we understand why investment banks held enormous portfolios of “super-senior triple A-rated” whatever. These securities had very small returns. However, the risk models said they had trivial VaR, because the possibility of credit loss was calculated to be beyond the VaR threshold. This meant that holding them required only a trivial amount of capital, and a small return over a trivial capital can generate an almost infinite revenue-to-equity ratio. VaR-driven risk management encouraged accepting a lot of bets that amounted to accepting the risk that heads wouldn’t come up seven times in a row. In the current crisis, it has turned out that the unlucky outcome was far more likely than the backtested models predicted. What is worse, the various supposedly remote risks that required trivial capital are highly correlated; you don’t just lose on one bad bet in this environment, you lose on many of them for the same reason. This is why in recent periods the investment banks had quarterly write-downs that were many times the firm-wide modelled VaR.

The Real Risk Issues
What. besides the 'art of simple communication', can we - actuaries - learn from David Einhorn? What David essentially tries to tell us, is that we should focus on the real Risk Management issues that are in the x% tail and not on the other (100-x)%. Of course, we're inclined to agree with David. But are we actuaries truly focusing on the 'right' risks in the tail? I'm afraid the answer to this question is most often: No! Let's look at a simple example that illustrates the way we are (biased) focusing on the wrong side of the VaR curve.

Example Longevity
For years (decades) now, longevity risk has been structurally underestimated. Yes, undoubtedly we have learned some of our lessons. Today's longevity calculations are not (anymore) just based on simple straight-on mortality observations of the past. Nevertheless, in our search to grasp, analyze and explain the continuous life span increase, we've got caught in an interesting but dangerous habit of examining more and more interesting details that might explain the variance of future developments in mor(t)ality rates. As 'smart' longevity actuaries and experts, we consider a lot of sophisticated additional elements in our projections or calculations. Just a small inventory of actuarial longevity refinement:
  • Difference in mortality rates: Gender, Marital or Social status, Income or Health related mortality rates
  • Size: Standard deviation, Group-, Portfolio-size
  • Selection effects, Enhanced annuities
  • Extrapolation: Generation tables, longitudinal effects, Autocorrelation, 'Heat Maps'
X-Tails In our increasing enthusiasm to capture the longevity monster, we got engrossed in our work. As experienced actuaries we know the devil is always in the De-Tails, however the question is: In which details? We all know perfectly well that probably the most essential triggers for longevity risk in the future, can not be found in our data. These triggers depend on the effect of new developments like :

It's clear that investigating and modeling the soft risk indicators of extreme longevity is no longer a luxury, as also an exploding increase in lifespan of 10-20% in the coming decades seems not unlikely. By stretching our actuarial research to the medical arena, we would be able to develop new (more) future- and shock-proof longevity models and stress tests. Regrettably, we don't like to skate on thin ice..... Ostrich Management If we - actuaries - would take longevity and our profession as 'Risk Manager' more seriously, we would warn the world about the global estimated (financial) impact of these medical developments on Pension- and Health topics. We would advise on which measures to take, in order to absorb and manage this future risk. Instead of taking appropriate actions, we hide in the dark, maintaining our belief in Fairy-Tails. As unworldly savants, we joyfully keep our eyes on the research of relative small variances in longevity, while neglecting the serious mega risks ahead of us. This way of Ostrich Management is a worrying threat to the actuarial profession. As we are aware of these kinds of (medical) future risks, not including or disclaiming them in our models and advice, could even have a major liability impact. In order to be able to prevent serious global loss, society expects actuaries to estimate and advise on risk, instead of explaining afterward what, why and how things went wrong, what we 'have learned' and what we 'could or should' have done. This way of denying reality reminds me of an amusing Jewish story of the Lost Key...

The lost Key
One early morning, just before dawn, as the folks were on their way to the synagogue for the Shaharit (early morning prayer) they notice Herscheleh under the lamp post, circling the post and scanning the ground. “Herschel” said the rabbi “What on earth are you doing here this time of the morning?” “I lost my key” replied Herscheleh “Where did you lose it?” inquired the rabbi “There” said Herscheleh, pointing into the darkness away from the light of the lamp post. “So why are looking for your key in here if you lost it there”? persisted the puzzled rabbi. “Because the light is here Rabbi, not there” replied Herschel with a smug.





Let's conclude with a quote, that - just as this blog- probably didn't help either:

Risk is not always apparent,
but its invisibility is no longer an excuse for ignoring it.

-- Bankers Trust on risk management, 1995 --


Interesting additional links:


Jun 13, 2015

Professional Empathy of an Actuary

The most important hard skill in any profession is a soft skill called Empathy.
Without empathy, any project or business goal is doomed to fail.

Just a small humorous illustration to get the picture....

Adding Rabbits

One day, the math teacher asks six year old Johnny, "If you have 200 rabbits and you add another 100 rabbits, how many rabbits would you have?"

Johnny thinks for a moment and then answers: "I think the answer is 337 Sir".

"No, Johnny. That's the wrong answer. Try again.

Johnny takes another five seconds and answers: "Still probably 337 Sir" "

Now the math teacher slightly loses his temper and baffles: "No Johnny, wrong again. You know nothing about mathematics!".

Immediately Johnny answers: "And you know nothing about rabbits Sir"

Actuarial Skills
A recent (May 2015) Investopedia article sums up the five skills every actuary needs
  1. Analytical Problem Solving Skills 
  2. Math and Numeracy Skills 
  3. Computer Skills 
  4. Knowledge of Business and Finance 
  5. Communication and Interpersonal Skills
Although in a classical sense this powerful summary of an actuary's professional competences is perfectly in line wit more detailed descriptions as given by several actuarial associations, something essential is missing.

To be successful as an an actuary you'll need to develop what is called

Professional Empathy


What is Professional Empathy?
Professional empathy is the ability to see the world through the eyes of other professionals.

As actuaries we're trained to view and resolve our projects, challenges and issues in a primarily quantitative manner with the help of actuarial techniques, models and formulas.

Through study and experience we can develop "Professional Empathy" that allows us to look through the eyes of other professionals or clients and feel and understand what their view and perceptions are.
In doing so, we're able to optimize our advice, support or project performance.

Understanding and response
Professional empathy implies two elements: understanding and response.

Understanding
To understand other professionals and clients, we need to develop the ability to be sensitive to the needs and feelings of others. To do so, it helps to develop technical skills in other professional fields, read other than just actuarial literature and join other than pure actuarial conferences.

As graduated actuaries we already developed a broad multi-professional basis that includes professional areas as: mathematics, statistics, finance, insurance, asset management, pricing, administration, business analytics, ict, organization, marketing, etc. Therefore, if we continue to develop this multi-professional ability, we - as actuaries - are are ideally suited to organize combined professional expertise (innovation) projects.

Besides the traditional actuarial areas as insurance, pensions, statistics, and actuarial techniques we'll have to focus and develop skills in the surrounding areas of the actuarial work field:



In order to understand we need to develop the ability to read verbal, paralinguistic and non verbal cues of professionals in other professional fields.

Response
Secondly, our response and attitude to other professional should be in such a way that other professionals recognize that we understand them, appreciate that we speak their (professional) language and invite us to share their professional issues or doubts with us.

Interpersonal communication skills 
This way of responding requires to bring out a professional attitude that's based on interpersonal communication skills like:
  1. showing interest, respect and appreciation
  2. active listening
  3. showing understanding, being accessible, 
  4. having a flexible attitude 
  5. operate steady on basis of ethical principals.

Intrapersonal skills
It also forces us actuaries to continuously work on our intrapersonal skills like: 
  1. Knowing what drives, angers, motivates, frustrates, inspires you
  2. Knowing your own strengths and limitations
  3. The ability to stay calm and balanced in stressful situations
  4. Self confidence

How to start with Professional Empathy?
You can start with developing your professional Empathy as follows:
  1. simply pick out one of the communication issues as mentioned above (e.g. 'active listening')
  2. After a conversation with a trusted professional you work with, simply ask for honest feedback by asking for example: "I try to develop a more active listening style. May I ask you: Do you think I really listened to your arguments. If so, in what way? If not, 'why' and 'when' not? What would you have expected of me?

If it seems difficult for you to ask these questions, congratulations! You now know for sure this approach is applicable to you.

If you think it makes no sense to ask these questions once in a while. Just keep doing what you always did until the final reality check!

SOA Self Assessment
The Society of Actuaries has developed a Competency Framework Self-Assessment Tool. The self-assessment asks you to rate a series of statements about the skills that actuaries should have to be valued for their professionalism, technical expertise and business acumen.

It's a 45 minute test that gives first impression of you improvement areas. However, interpersonal en intrapersonal skills are only limited measured.........

Sep 26, 2013

Actuarial Cookery in the Boardroom

Suppose your friend gave you the recipe for a delicious 'Paleo Tomato Soup'.

Does that recipe also guarantees you a delicious meal ?

Undoubtedly you answered this question with a clear "no".

Why?

As we all know, it is the 'touch of the chef' that determines the quality and final taste of the meal. The recipe is the score and the chef the performer of the culinary piece of music, that will end up on your plate.

Although the above example probably sounds logical to us, the actuarial cooking practice appears different. Let's take a look at the next example.

An Excellent ALM Advice
What about a 'plate of five' asset mix advice that's on the board's breakfast table, as the ultimate outcome of your excellent ALM analysis...

Does this ' computer recipe' actually guarantees a sound decision about an adequate investment policy?

Actually, the answer to this question can hardly be other than 'NO'.

Your advice is a static advice in a dynamic world and - on top of - the final question remains whether the asset manager is able to 'spice up' your recipe.

The actuary as Risk-Director
Key question is whether we as a profession - keeping ourselves inadvertent in the role of  'technical experts' - merely feel responsible for delivering the recipe for a cold asset mix salad on basis of 'expected values' ​​and variances.

Or ... that we actuaries are willing to act as 'risk-director' in the interactive process of creating a dynamic investment policy that's based on a nonlinear constructed healthy and varied based asset mix over time. Albeit..., without taking the driver's seat in the advice process, but with the obligation to report the eventual existence of any GMCs ('Genetically-Modified Cickens') in the asset-mix.

Economic Risk Management or ALM?
In the thorough process of adopting a dynamic investment policy, financial boards more and more take decisions based on the study of different future economic scenarios.

This development challenges actuaries to invest more in the development of "Economic Risk Management" (ECRM) models instead of traditional ALM modeling. In ECRM 'asset class data' (as in ALM) and economic data (GDP, inflation, consumer confidence, etc) are mixed in an integral set of data, that's analysed and - with future expectations, 'stress-test conditions' or of 'believes' -  (nonlinear) translated and optimized in a dynamic asset mix.

This economic risk approach requires new nonlinear economic-asset models that urge for a close cooperation between economists and actuaries, resulting in an serious interactive board discussion (board members and economical & actuarial experts) of the ECRM models.

This approach is not limited to the well-known three or four so-called 'muddle through scenarios', but covers the outcome and impact of a large number of more precise formulated possible economic scenarios on the asset mix and the investment strategy.

Scenarios that help determine the overall risk appetite and result in a major impact on the composition of the strategic asset mix.

New Q&A's
In other words, new scenarios that give answers to questions like:

As with the current ALM approach, the focus should not be only on the quantitative outcome of the ECRM model, but more on the discussion and wider perception of how economic risk affects the optimal asset mix and the dynamic asset policy, allowing boards to take more informed and underpinned investment policy decisions.

In this approach, ALM and ECRM are helpful but not dominating decision support tools in the creation of the final investment policy and not an unintended consultant's dictate that's implicitly adopted ("take note") by the board and then subsequently implemented.

How to Check the Quality of your ALM or ECRM Advice?
Fortunately, it is easy to check whether your ECRM or ALM advice is actually a good quality decision document or just a bite-sized chunk.

If your advice offered only 'one option' or was adopted without a serious debate or any amendments, then -  to put it euphemistically - your advice is 'ready for improvement'.

Actuaries: Backroom to the Boardroom
Finally, it all comes down together whether we as actuaries want to profile ourselves as 'recipe writers' or pick up the 'risk-director role' as an 'actuarial chef'. If you choose the latter, please stand up and help to bring out actuaries from the Backroom to the Boardroom. Success!

Jun 13, 2011

Actuary Garfield

There's not a lot of 'Actuary Humor' on the Internet. Here's one...

Actuary Garfield explains how actuaries think...


Great and lots of humor, those Garfield cartoon strips, (especially those about actuaries....).

Original Sources:
- Garfield Snow
- Garfield Snowman

May 25, 2011

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

May 10, 2011

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 (Rt) 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 (Rt) more on basis of a 'Multi Economic Approach'  (MEA) in a form that looks more or less something like: Rt = 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

Apr 18, 2011

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



on facebook.


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

Related links:
- Gapminder World

Mar 27, 2011

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)