Emerging Stress Scenarios

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Slides from a PRMIA Webinar broadcast on 9 October 2013 by Alan Laubsch and me.

Description from PRMIA Website:
This webinar will apply advanced network visualization techniques to detect emerging systemic stress scenarios.

We will start with an introduction of the Adaptive Stress Testing framework, which harnesses network intelligence in the stress testing process. We'll show how Adaptive Stress Testing can be used to design credible scenarios and monitor emerging risks.

We review historical case studies, and then discuss potential emerging threats in the current market environment by using network visualization.

Published in: Economy & Finance, Business
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  • Here’s the agenda for the next hour:First we introduce a framework for Adaptive Stress Testing. The idea around Adaptive Stress testing is maximize learning feedback, very much like the trial and error evolutionary process of adaptation to new environments.Attention to Early Warning Signals is a crucial component to making Adaptive Stress Testing work in practice. Due to dramatic phase transition properties in complex systems, early warning and early response is essential for adapting successfully to changes in the environment. We take an interdisciplinary perspective, and look at lessons ranging from earthquake monitoring to epidemiology, as well as looking at some of the significant early warning signals we detected prior to the GFC and the current European sovereign crisis.
  • Early WarningYou can take a range of perspectives on early warning, ranging from imminently short term (e.g., jumps in equity implied volatility before corporate events) to very long term (e.g., macro-economic imbalances). It makes sense for us to address the whole time spectrum.We can approach this from a long term and short term perspective:Long Term: top down analysis based on diagnosing structural risks (especially bubbles) and scenario analysisBottom Up: based on specific portfolio vulnerabilities, driven by short term market factorsIn both cases, we identify key variables to monitor (the stakeout) and focus on any unusual movements.
  • FinancialsDaily upside excessions1.89%Daily downside excessions1.32%Weekly upside excessions1.32%Weekly downside excessions1.79%
  • Here’s the agenda for the next hour:First we introduce a framework for Adaptive Stress Testing. The idea around Adaptive Stress testing is maximize learning feedback, very much like the trial and error evolutionary process of adaptation to new environments.Attention to Early Warning Signals is a crucial component to making Adaptive Stress Testing work in practice. Due to dramatic phase transition properties in complex systems, early warning and early response is essential for adapting successfully to changes in the environment. We take an interdisciplinary perspective, and look at lessons ranging from earthquake monitoring to epidemiology, as well as looking at some of the significant early warning signals we detected prior to the GFC and the current European sovereign crisis.
  • Here’s the agenda for the next hour:First we introduce a framework for Adaptive Stress Testing. The idea around Adaptive Stress testing is maximize learning feedback, very much like the trial and error evolutionary process of adaptation to new environments.Attention to Early Warning Signals is a crucial component to making Adaptive Stress Testing work in practice. Due to dramatic phase transition properties in complex systems, early warning and early response is essential for adapting successfully to changes in the environment. We take an interdisciplinary perspective, and look at lessons ranging from earthquake monitoring to epidemiology, as well as looking at some of the significant early warning signals we detected prior to the GFC and the current European sovereign crisis.
  • Here’s the agenda for the next hour:First we introduce a framework for Adaptive Stress Testing. The idea around Adaptive Stress testing is maximize learning feedback, very much like the trial and error evolutionary process of adaptation to new environments.Attention to Early Warning Signals is a crucial component to making Adaptive Stress Testing work in practice. Due to dramatic phase transition properties in complex systems, early warning and early response is essential for adapting successfully to changes in the environment. We take an interdisciplinary perspective, and look at lessons ranging from earthquake monitoring to epidemiology, as well as looking at some of the significant early warning signals we detected prior to the GFC and the current European sovereign crisis.
  • Here’s the agenda for the next hour:First we introduce a framework for Adaptive Stress Testing. The idea around Adaptive Stress testing is maximize learning feedback, very much like the trial and error evolutionary process of adaptation to new environments.Attention to Early Warning Signals is a crucial component to making Adaptive Stress Testing work in practice. Due to dramatic phase transition properties in complex systems, early warning and early response is essential for adapting successfully to changes in the environment. We take an interdisciplinary perspective, and look at lessons ranging from earthquake monitoring to epidemiology, as well as looking at some of the significant early warning signals we detected prior to the GFC and the current European sovereign crisis.
  • Here’s the agenda for the next hour:First we introduce a framework for Adaptive Stress Testing. The idea around Adaptive Stress testing is maximize learning feedback, very much like the trial and error evolutionary process of adaptation to new environments.Attention to Early Warning Signals is a crucial component to making Adaptive Stress Testing work in practice. Due to dramatic phase transition properties in complex systems, early warning and early response is essential for adapting successfully to changes in the environment. We take an interdisciplinary perspective, and look at lessons ranging from earthquake monitoring to epidemiology, as well as looking at some of the significant early warning signals we detected prior to the GFC and the current European sovereign crisis.
  • Emerging Stress Scenarios

    1. 1. Emerging Stress Scenarios Wednesday, Oct. 9, 2013 at 12 pm U.S. Eastern Time Alan Laubsch Head of FNA Labs Financial Network Analytics • Kimmo Soramäki Founder and CEO Financial Network Analytics Audio: Use your microphone and speakers (VoIP) or call in using your telephone. • Direct your questions to Staff via the Questions or Chat pane. • To access this webinar audio via the internet, select “Mic & Speakers” under your Audio pane. • Check that the audio on your computer is on and the volume is turned up. • For technical assistance contact the Citrix webinar utility customer number: 1-888-259-8414 This material is the intellectual property of the presenter and shall not be reproduced or used without the express written permission .
    2. 2. Emerging Stress Scenarios Introducing HeavyTails™ Network Analytics Oct 9, 2013 PRMIA Webinar www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 2 2
    3. 3. Agenda 1. Adaptive Stress Testing • Signal or Noise? 2. HeavyTails™ Network Analytics 3. Network Stress Testing 4. Summary and Conclusions www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 3 3
    4. 4. Adaptive Stress Testing Framework I. Macro: identify structural risks (potential risks) • Stress Library based on Thought Leaders (Innovators) • Awareness of systemic cycles, in particular credit and asset bubbles • Financial or economic imbalances (e.g., capital flows, consumption vs. saving) • Examples: Shiller – (a) tech bubble (2000) and (b) housing bubble (2005) II. Micro: monitor potential precipitating events (visible risks) • Focus on short term market movements, especially outliers and regime shifts • Early Warning: identify amplification mechanisms and critical (tipping) points • Examples: vol spike in (a) tech stocks and (b) US mortgage securities & financials www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 4 4
    5. 5. Social Adoption of Disruptive Innovation Two key perspectives for stress testing 1. Macro: Stress Scenario Library from Innovators 2. Micro: Market signals from Early Adopters Source: Wikipedia; see Geoffrey Moore’s “Crossing the Chasm” (1999) www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 5 5
    6. 6. US Financials Case Study Financial Meltdown (“Roubini”) scenario escalates from ’07 and peaks March ’09 and then declines… inverse Financial Recovery scenario emerges Chart: U.S. Financials “death star pulse” 20.0% 15.0% 10.0% March 6 Market bottom Daily 99% VaR Backtest (.94 decay, Student t) Feb 27 „07 outlier 5.0% 0.0% -5.0% -10.0% -15.0% June 1 Market peaks -20.0% Source: Alan Laubsch, “Equities as Collateral In U.S. Securities Lending Transactions”, The RMA Executive Committee on Securities Lending & RiskMetrics, March 2011 www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 6 6
    7. 7. Agenda 1. Adaptive Stress Testing • Signal or Noise? 2. HeavyTails™ Network Analytics 3. Network Stress Testing 4. Summary and Conclusions www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 7 7
    8. 8. Two theories for crises Black Swan Dragon King (Taleb 2001, 2007) (Sornette 2009) vs. www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 8 8
    9. 9. Phase transitions can result from amplifying feedback Super-exponential instability and change characterizes phase transitions Source: Sornette et al., Endogenous versus Exogenous Origins of Crises (2008) See: http://www.er.ethz.ch/presentations/Endo_Exo_Oxford_17Jan08.pdf www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 9 9
    10. 10. Subprime CDO foreshocks: Tremors in Dec 2006 & Feb 2007 cascade into systemic meltdown Absolute spread moves were small, but rate of change was super-exponential. Parallels to failure and rupture process in material science (pressure to break point) bp's '2006-1 AAA' Absolute Spread Levels June 20 '07, ML tries to liquidate Bear Subprime CDO's 450 400 Major ratings agencies initiate reviews and/or downgrades week of July 9 '07 Feb 23 '07, first major outlier, 350% vol increase in 1 day, 12sd move 350 300 250 The first tremor (vol up 300% Dec 12-21) 200 150 100 50 www.fna.fi Kimmo Soramaki kimmo@fna.fi 6/19/2008 5/19/2008 4/19/2008 3/19/2008 2/19/2008 1/19/2008 12/19/2007 11/19/2007 10/19/2007 9/19/2007 8/19/2007 7/19/2007 6/19/2007 5/19/2007 4/19/2007 3/19/2007 2/19/2007 1/19/2007 12/19/2006 11/19/2006 10/19/2006 9/19/2006 8/19/2006 7/19/2006 6/19/2006 5/19/2006 4/19/2006 3/19/2006 2/19/2006 1/19/2006 0 Alan Laubsch alan@fna.fi 10 10
    11. 11. Subprime CDO VaR outlier analysis reveals the risk signals RM 2006 99% VaR bands vs 2006-1 AAA spread changes 120.0% One major outlier, a 12 sd move on Feb 23 '07, the day after the $10.5bn HSBC loss announcement 100.0% 80.0% GS exits 60.0% subprime Major ratings agencies initiate reviews and/or downgrades week of July 9 '07 Spread Change 40.0% 20.0% 0.0% -20.0% -40.0% 300%+ increase in vol from Dec 12 to 21 '06 Backtesting summary: 2.4% upside excessions 0.81% downside excessions -60.0% -80.0% 357% vol spike on Feb 23 '07 6/19/2008 5/19/2008 4/19/2008 3/19/2008 2/19/2008 1/19/2008 12/19/2007 11/19/2007 10/19/2007 9/19/2007 8/19/2007 7/19/2007 6/19/2007 5/19/2007 4/19/2007 3/19/2007 2/19/2007 1/19/2007 12/19/2006 11/19/2006 10/19/2006 9/19/2006 8/19/2006 7/19/2006 -100.0% Date Source: Alan Laubsch “Subprime Risk Management Lessons”, RiskMetrics www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 11 11
    12. 12. Polling question Does your organization use market based early warning signals? 1. YES 2. NO www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 12 12
    13. 13. Agenda 1. Adaptive Stress Testing • Signal or Noise? 2. HeavyTails™ Network Analytics 3. Network Stress Testing 4. Summary and Conclusions www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 13 13
    14. 14. The Data … Pairwise correlations of daily returns on 35 global assets (ETFs), incl. Equity indices FX Commodities Debt Derivatives www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 14 14
    15. 15. The ETFs www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 15 15
    16. 16. Significant Correlations Common method to visualize large correlation matrices is via heat maps Keep statistically significant correlations with 95% confidence level All correlations (last 100 days) Statistically significant correlations (last 100 days) Carry out 'Multiple comparison' correction -> Expected error rate <5% www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 16 16
    17. 17. About Color Perception A and B are the same shade of gray Right? www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 17 17
    18. 18. About Color Perception A and B are the same shade of gray www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 18 18
    19. 19. Correlation Network Problem: Heatmaps can be misleading due to human color perception Lets build some network approaches for visualizing correlations www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 19 19
    20. 20. Correlation Network Nodes are assets Links are correlations: Red = negative Black = positive Absence of link marks that asset is not significantly correlated www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 20 20
    21. 21. Hierarchical Structure in Financial Markets Minimum Spanning Tree Rosario Mantegna (1999): "Obtain the taxonomy of a portfolio of stocks traded in a financial market by using the information of time series of stock prices only“ We use the Minimum Spanning Tree (MST) of the network to filter signal from noise. www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 21 21
    22. 22. Re-positioning the Assets We lay out the assets by their hierarchical structure using Minimum Spanning Tree of the asset network. Shorter links indicate higher correlations. Longer links indicate lower correlations. www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 22 22
    23. 23. Mapping Returns and Outliers Network layout allows for the display of multiple dimensions of the same data set on a single map: Node color indicates latest daily return - Green = positive - Red = negative Node size indicates magnitude of return Bright green and red indicate an outlier return www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 23 23
    24. 24. Polling Question 2 Which scenarios are of greatest concern to your institution? 1. Eurozone crisis redux 2. Emerging markets hard landing (China, India, SEA) 3. US precipitated liquidity or credit shock – default, tapering 4. Geopolitical instability (Syria, Iran, …) 5. Other www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 24 24
    25. 25. Gold Early Warning Case study: downside outlier clustering www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 25 25
    26. 26. Stress Scenarios (Demo using www.heavytails.com) DEMO HERE www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 26 26
    27. 27. Agenda 1. Adaptive Stress Testing • Signal or Noise? 2. HeavyTails™ Network Analytics 3. Network Stress Testing 4. Summary and Conclusions www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 27 27
    28. 28. Systemic risk Not clearly defined We understand as: "The risk that a system composed of many interacting parts fails due to a shock to some of its parts" Not: - complex systems approach Domino effects, cascading failures, financial interlinkages, … -> i.e. a process in the financial network 28 www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 28 28
    29. 29. The Network for an Oil shock www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 29 29
    30. 30. The Network for Multiple Shocks www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 30 30
    31. 31. Poll Question 3 How do you take into account dependencies in your stress scenarios? 1. Qualitative approach: subjective assessment of repercussions 2. Quantitative approach: using correlation structure 3. Blend: combination of qualitative and quantitative (art and science) www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 31 31
    32. 32. Agenda 1. Adaptive Stress Testing • Signal or Noise? 2. HeavyTails™ Network Analytics 3. Network Stress Testing 4. Summary and Conclusions www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 32 32
    33. 33. Sense and Respond to emerging risks 1. Detect signals amidst noise - algorithms, visualization, and human intelligence to 2. Model a credible sequence of shocks from key nodes into the rest of the network 3. Keep your eyes open to the periphery, where disruptive innovation arises Anticipate Most of the focus at most companies is on what’s directly ahead. The leaders lack “peripheral vision.” This can leave your company vulnerable to rivals who detect and act on ambiguous signals Source: “6 Habits of True Strategic Thinkers,” Paul Schoemaker, March 20 2012 www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 33 33
    34. 34. Conclusions • Early detection and adaptation is crucial for managing systemic risks • HeavyTails™ amplifies market intelligence and helps prioritize focus • Spark Network Intelligence “The future is already here. It’s just not evenly distributed yet.” William Gibson www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 34 34
    35. 35. Thank You! Please join the PRMIA “Emerging Stress Themes” community on LinkedIn Email us for discounts on the PRMIA Adaptive Stress Testing online course and community Free beta trial version of HeavyTails™ for PRMIA members at www.heavytails.com www.fna.fi Kimmo Soramaki kimmo@fna.fi Alan Laubsch alan@fna.fi 35 35
    36. 36. Questions for the Presenters? Send them via the Question Pane in the webinar utility panel on the right hand side of your screen 36
    37. 37. Thank you for attending this PRMIA Webinar! Please go to PRMIA’s website at www.prmia.org. Click on Webinars under the Training tab to find more upcoming thought leadership webinars. Also, click on the Membership tab for information on joining PRMIA as a sustaining member. 37

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