Black Swan Risk Management - Aditya Yadav

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Black Swan Risk Management - Aditya Yadav

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Black Swan Risk Management - Aditya Yadav

  1. 1. * “Running an Agile Fortune 500 Company” Aditya Yadav, aditya.yadav@gmail.com in.linkedin.com/in/adityayadav76
  2. 2. * A Typical Global Company * Fortune 500/1000 * 200 Divisions * 40 Countries * 25000 Employees *
  3. 3. * @ Acme Inc.
  4. 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.” *
  5. 5. * And The Philosophy Behind The Answer
  6. 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. 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 dont hold anymore. *
  8. 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. 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 gotchas 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. 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. 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. *
  12. 12. Aditya!!! *

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