Summary

Adaptive stress testing is a blend of art and science which continually integrates qualitative macro and quantitative micro perspectives. The first challenge in stress testing is to conceive of a wide range of credible potential threats before they materialize. Let’s tap into the marketplace of ideas for scenarios, and harness the ability of visionaries to perceive risk in potential form (think Albert Einstein). After constructing Stress Indices to reflect scenarios, we monitor outliers, which are precursors to regime shifts. The StressGrades methodology amplifies market- based risk signals, which highlights cascading risk (e. g., super-exponential increases in PStress). StressGrades are also useful in identifying assets with exceptionally low volatility, which should be stressed for hidden risks (e. g., high DStress & low StessQ). Let’s never forget that volatility only represents visible risk and that risk managers must be contrarian and uncover risks that are invisible to most. Low volatility is a temporary respite which allows us to search for hidden risks and rebalance to build more resilient portfolios and institutions. By being intelligently contrarian, we can mitigate systemic risks and transform future crises into opportunity.

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Conclusions: Spark Network Intelligence

Evolutionary adaptation is a learning process: we sense changes in the environment and respond with learning experiments. Failures are not only inevitable, but essential to learning. As Tim Harford elegantly observes in Adapt: Why Success Always Starts With Failure (2011): “the art of success is to fail productively.” But to be able to learn from failure, we must be able to survive and keep playing. The obvious priority for risk managers is to ensure that their organization can withstand credible stresses. And yet paradoxically, many risk strategies that are designed to reduce individual risk (e. g., portfolio insurance, stop-loss limits, and liquidity hoarding in crisis situations) increase coupling, and often even precipitate crises. In A Demon Of Our Own Design (2008) Richard Bookstaber shows that many crises were precipitated by flawed safety mechanisms. When faced with complexity, tightly coupled systems eventually break down.

To manage systemic risks, we must look beyond individual nodes and understand the non-linear processes driving ecosystems. In “Rethinking Cap­italism” Nick Hanauer and Eric Liu implore us to transcend “Machinebrain” linear thinking:

In the Gardenbrain story, markets are not perfectly efficient, but they are effective if managed well. Humans are not perfectly rational, calculating and selfish; they are emotional, approximating and reciprocal. And outcomes are not just as they should be; rather, they reflect the kinds of compounding and feedback loops—virtuous circles or death spirals—that distort all complex systems. (Hanauer and Liu 2012)

Industrial capitalism has fuelled economic growth and expanded wealth worldwide. But it also comes with new liabilities (externalities), many of which are in hidden form. We face serious disruptive threats across all our global ecosystems.16 As Otto Scharmer writes in “Leading from the Emerging Future: From Ego-System to Eco-System Economies” (2013), individually oriented approaches are unsustainable:

What’s dying is an old civilization and a mindset of maximize “me”—maximum material consumption, bigger is better, and special-interest-group driven decision­making that has led us into a state of organized irresponsibility, collectively creating results that nobody wants.

Throughout history, humans have faced a basic choice when meeting challenges: conflict or cooperation. Conflict, while unavoidable at times, is negative sum. Cooperation yields far better results, and indeed is the

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16Major potential risk fault lines include rising economic inequality and environmental degradation due to pollution, overuse of resources, and loss of biodiversity.

 

foundation for sustainable growth and innovation (Johnson 2010). As inventor Dean Kamen puts it: “if you have an idea and I have an idea and we exchange them, then we both have two ideas. It’s nonzero (Diamandis and Kotler 2012).”

We see the benefits of cooperation throughout natural systems. Evolu­tionary leaps occur when individual “holons” (Koestler 1967) cooperate, for example as in the emergence of multi-celled organism, or hive insects like ants and bees. Thriving ecosystems are characterized by “cooperative relationships, self-regulating feedback cycles, and dense interconnectedness” (Benyus 2002).

The specter of disruptive global risks calls for mass collaboration plat­forms to better share information and coordinate responses. Nate Silver (2012) makes a case for predictive markets for economic data. Dan Tapscott shows many practical examples of effective mass collaboration platforms in Macro Wikinomics (2012), such as the mobile and Google Maps based platform that helped coordinate the Haiti earthquake relief efforts. Why not build sharing platforms for financial risk management, and specifically around stress testing? Indeed, network visualization platforms such as FNA might serve as the shared Google Maps of financial cartography, to help us better understand and communicate about the dynamic financial landscape.’

A new sharing economy has emerged. Social networks have connected us in online communities, and every like, tweet, and update has the potential to increase collective intelligence. Each of us has the potential to contribute in uniquely. Evolution, after all, is not an abstract force. We each embody evolutionary intelligence, and are all co-creators in a world where a “flap of a butterfly’s wings in Brazil [could] set off a tornado in Texas” (Lorenz 1972). Imagine a neuron within a vast network of neurons, each sensing and responding to an ever changing world. Let’s spark an evolutionary leap in intelligence by participating in collaboration platforms to share information about the risks that affect us all. It’s not technology that’s holding us back. The challenge is mindset, and a transition from an ego-centered to an eco­centric perspective of risk.

 

Acknowledgments I’d like to express gratitude to the many minds who have inspired this work. Firstly, to Sergey Ivliev of Prognoz for organizing the unique gathering of Perm Winterschool, and encouraging this paper.

Thank you to my colleagues at FNA. Kimmo Soramaki opened my eyes to financial cartogra­phy. Sam Cook generated our case study network graphs. And Eugene Nevdov provided valuable feedback.

Deep gratitude to the RiskMetrics family. Ethan Berman nurtured the open and creative culture that brought the best out in us. Our credo: “Change the world. Have fun. Make money. In that order.” Allan Malz’s crisis early warning research was seminal. I’ve referenced Chris Finger’s research throughout, and am proud that we have finally realized our idea of a global outlier based systemic risk monitor with FNA HeavyTails. It was great to work with Pete Benson on

 

riskcommons. org and to produce the first generation of StressGrades analytics. Gilles Zumbach’s RiskMetrics 2006 time series research were invaluable. It was an honor to work with Knut Kjaer on next generation risk management, which evolved into the Adaptive Stress Testing framework. It was always a joy to brainstorm with my RiskMetrics labs partner Ron Papanek. Marty Nemeth was also a great sounding board, overflowing with ideas. Alvin Lee was my first mentor at JPMorgan and has always supported new ideas and a path of growth and adventure. And it was great to work with Ken Parker, Tom Stockdale, and the NextThought. com team to produce our online Adaptive Stress Testing course.

Thank you to PRMIA for much support. Lori Ramos-Marilla offered constant encouragement and enabled the opportunity to present the work at several conferences. Alex Voicu has been a creative force in enabling this research. He established a bridge to the global risk community by organizing many excellent workshops and producing the Adaptive Stress Testing online course at PRMIA University.

I deeply appreciate the insightful conversations with Anne Lalsing of Citibank, who inspired the StressGrades methodology and has provided so much thoughtful feedback.

Thank you to my Winhall Consulting partner David Shimko for encouraging early warning research, an area he had pioneered many years ago at JPMorgan.

I am grateful to philosopher Ken Wilber who inspired Integral Risk Management, and to the Boulder Integral community (especially Jeff Salzman and Nomali Perera).

Thank you to the editors at Springer for their detailed attention and patience.

And finally, I hope that Didier Sornette’s foundational Dragon King research will empower the global community to be more proactive in managing systemic risks before irreversible tipping points are crossed.

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