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.
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
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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. 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. 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
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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
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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. 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. 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
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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
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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. 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. 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. 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. 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
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. 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. 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. 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. 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. 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. 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. 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. 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. Gold Early Warning Case study: downside outlier clustering
www.fna.fi
Kimmo Soramaki kimmo@fna.fi
Alan Laubsch alan@fna.fi
25 25
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. 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. 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. The Network for an Oil shock
www.fna.fi
Kimmo Soramaki kimmo@fna.fi
Alan Laubsch alan@fna.fi
29 29
30. The Network for Multiple Shocks
www.fna.fi
Kimmo Soramaki kimmo@fna.fi
Alan Laubsch alan@fna.fi
30 30
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. 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. 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. 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. 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. 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. 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
Hinweis der Redaktion
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.
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.