This document discusses managing risks from "black swan" events, which are rare events with severe consequences that are often rationalized with hindsight. The author argues that a probabilistic or statistical approach is inappropriate for black swan risk management. Instead, organizations should use scenario-based modeling to simulate assumption failures, identify model sanity checks, and prepare reactive measures. The key is having a general consensus on risk themes and practices for different categories of model breakdowns, rather than rigid procedures, so people understand risks mentally.
4. * Original Question “How do we manage risk due
to Black Swan events? Isn’t that an event with
very small probability and very large impact?”
* The Correct Question - “Probability and
Statistics might not be the best approach. Lets
rethink Black Swan events and create a Risk
Management Framework around it.”
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6. * Black Swan Event - The black swan theory or theory of black
swan events is a metaphor that describes an event that is a
surprise (to the observer), has a major effect, and after the
fact is often inappropriately rationalized with the benefit of
hindsight.
* The World often seems to careen from crisis to crisis, with
protestors regularly spilling onto the streets over the latest
outrage or scandal, countries seemingly always on the boil.
But when things settle, as they inevitably do, little seems to
change. Blanket Regulations and Policy’s are made to address
Black Swan Events. Public anger usually cools to a simmer.
And the World moves on. But at What Cost???...
* Substitute: World w/
Country/Organization/Division/Group/Team
*
7. * Quantitative – Mathematical Models, Finance etc.
* Qualitative – Management and Strategy
* Whether Quantitative or Qualitative the approach to
Managing Risk of Black Swan events remains the same
* Vary the Assumptions and Parameters by large amounts e.g.
Lets say +/- 1000% and see the response of the
Model/Strategy
* Break the Model Assumptions one by one and see the
response of the Model/Strategy
* Which brings us to what is a Black Swan event? It is basically
a low probability event in which your core assumptions and
models go for a toss. And such risks should be addressed the
same way, by analysing the model when the assumptions
don't hold anymore.
*
8. * The obvious question to ask is what happens if something outside the Model changes
drastically?
* Three things can happen
* There is no impact on the Model and your Assumptions and Parameters don’t change
much. Which means your Model is still valid. Why even worry about this Scenario?
* There is a huge impact on your models and one or two assumptions and parameters go for
a toss. But that is exactly what we are analyzing in the earlier slide. We should be
covered.
* We have considered Independent Assumption failures earlier. What if the outside world
has a complex significant effect on the models/strategy? (which should be very close to
what should be expected in reality)
* You need to take a Scenario based Analysis Approach of the Models
* Ref my Deck: Strategic Scenarios
* Ref my Deck: The Evil of Our Worst Assumptions
* But honestly there is very little you can do besides this
* But this should cover 99.9999% of the cases if done properly
* Rule: The new mantra for Risk Management in the new age is Scenario Based Model
Simulation With Assumption Failures.
*
9. * A more proactive approach is to identify model assumption sanity checks and set alerts
in realtime decision models to monitor and report possible failure of assumptions.
* The other aspect would be to pre-prepare reactive measures to model assumption
breakdowns learnt from simulations, because in a Black Swan event the time window
for response is really really short.
* The next aspect is general consensus about the thought process w.r.t. different
categories of black swan model breakdowns so as to limit chaos in the event of a real
event. We are not talking about prescriptive reaction and thought process
documentation but rather a general consensus about risk themes, principles, values
and practices w.r.t. different categories and thresholds of model failures.
* The biggest gotcha's are: When the knowledge is hidden in models and nobody
understands them and uses them blindly or if everyone is using rules of thumbs instead
of models built on facts and assertions.
* These two factors are the reason that openly invites the highest Black Swan Disaster
Impact. It hits the hardest when you have no clue about whats happening. And then
“Everyone goes on to state in hindsight how easy it would have been to prevent Black
Swans”
*
10. * I have been talking to Statisticians
* They tend to approach Black Swans with the perspective of
* Probability & Statistics
* Long Tails of Event Distributions, or
* Considering Distributions other than the Normal Distribution
* 100 years of Historical Data
* Or a combination of above
* Such a view of Black Swans is wanting in many ways according to me
* When the events happen they happen with a probability of 1 (at that instant because of virtue of the
event) with full impact
* You simply cannot take a weighed average risk management approach
* What would a canceling of risk in a mathematical risk model even mean when the event occures?
Answer that
* Going back to the rule of: when the amount is small you can take a relative approach when its
comparable to your existence/capital/survival you have to take an absolute approach
* So basically a Probabilistic or Statistical approach to Black Swan Risk Management is completely
inappropriate
*
11. * Statistics gives a black box view of Black Swans
* It cannot lead to Realtime Diagnostics and Prevention
* It cannot lead to Sanity Checks and Failure Detection in Realtime
* It doesn’t lead to an Effective Response Strategy Building to a large
extent
* Because you really don’t know whats changed within the Black Box
and how
* Compared to that the Framework I have described is a more intrinsic
approach.
* Also the Biggest Lesson is - "Its All in the mind"
* Risk Management themes should register with everyone mentally while
the exact procedures can be lookedup and refered to later. In reality
the reverse happens. Everyone is told the exact procedures but have
no clue about the Models, Assumptions, Parameters and the reasons of
why and what can go wrong.
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