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Systemic Risk caused by
Synchronization
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and
Policy Interventions
Jorgen Vitting Andersen, CNRS, Centre d´Economie de la Sorbonne, Université
Paris 1 Panthéon-Sorbonne
Collaborators:
L. Bellenzier, P. Dellaportas, S. Galam, A Nowak, P. de Peretti, M.
Roszczynska, G. Rotundo, R. Savona, S. Stefani, and I. Vrontos
SYRTO Project Final Conference, Paris – February 19, 2016
“Socio-Finance: price formation
is a sociological phenomenon.
“Will consider human decision making to happen in
two different ways
i) direct communication between people
or
ii) indirect communication between people through
a medium, say price index (Finance)
-1st level: communication between individuals
- 2nd level: communication between groups of
individuals
Dynamics of indirect communication
of individuals through a price/index
“
Indirect decision making through a medium:
people trading a financial market index
The “symphony” of the market
(complexity theory applied to financial markets)
Synchronization of markets: “price-quakes”
Integrate-and-fire (IAF) oscillators: fireflies
Integrate-and-fire oscillators
Systemic risk caused by synchronization
Systemic risk caused by synchronization
Phase transitions, symmetry break and
financial moods
The sand pile model
Systemic risk caused by synchronization
Systemic risk caused by synchronization
Systemic risk caused by synchronization
Understanding Excessive Risk Taking Seen in
Experiments on Financial Markets
• Jørgen Vitting Andersen, CNRS,
Centre d’Economie de la Sorbonne,
University of Paris 1.
• Research in progress, collaborators:
Yifang Liu, Philippe de Peretti, Maxim
Frolov, Roberto Savona, Hayette Gatfaoui,
Rania Kaffel
Individual versus collective risk taking
• Individual risks: Men are known to be more risk loving
compared to women. For real market traders see e.g. :
“Endogenous steroids and financial risk on a London
trading floor”, J. M. Coates and J. Herbert, PNAS, V.105,
16, 6167-72 (2008); “A note on trader Sharpe Ratios”, J.
M. Coates and L. Page, PLoSONE, V.4, 11, e8036 (2009)
• Collective risks: how does a group of traders with
heterogeneous risk profiles influence the formation of
market risks?
Setup of experiments
• Before each experiments individual risk profiles of participants were
obtained from lotteries (C. A. Holt, S. Laury, The American Economic
Review, V. 92, 1644 (2002)
Here’s what happens when a group of men
trade …
Next we let a group of women trade …
Looks similar to the group of men …
Then another group of women … initially this
group of women seem to trade differently…
And indeed in this experiment the group of
women creates a speculative state…
Their speculation however was « dwarfed »
compared to state created by yet another
group of men…
Claim: we need a fluctuation based framework in
order to be able to understand behavior seen in
experiments.
The $-Game
The $-Game
Results: simulations of the $G
Important: Symmetry break
Taking the «temperature» of the market: predicting big
price «swings »
• Internal state of water? Insert a thermometer into the
liquid.
• Internal state of market? «slave » an agent based model to
the price evolution.
• Market in a “hot” or “cold” state.
Systemic risk caused by synchronization
ACTION
0000 0

0000

0001 0
0100 1

1010
 
0101 1
0101
0110 1
 
1011

0111 1
Systemic risk caused by synchronization
Systemic risk caused by synchronization
Dynamics of direct communication
between individuals
Systemic risk caused by synchronization
Changing the « bullishness » in a population
via communication in subgroups
During the day communication takes place in
random subgroups
During the day communication takes place in
random subgroups
During the day communication takes place in
random subgroups
Systemic risk caused by synchronization
Systemic risk caused by synchronization
Quantitative description of model
Groupe of size k
Groupes of different sizes
Link between communication and its impact
on the markets
• r(t) the return of the market, RB(t)=[B(t)-
B(t-1)]/B(t), and η(t) Gaussian distributed
with zero mean and std. dev.:
Link between transition probabilities
and the market performance
Main results: reproducing stylized facts
Main results: maximum likelihood analysis
Main results: maximum likelihood analysis
Systemic risk caused by synchronization
This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement n° 320270
www.syrtoproject.eu
This document reflects only the author’s views.
The European Union is not liable for any use that may be made of the information contained therein.

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Systemic risk caused by synchronization

  • 1. Systemic Risk caused by Synchronization SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions Jorgen Vitting Andersen, CNRS, Centre d´Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne Collaborators: L. Bellenzier, P. Dellaportas, S. Galam, A Nowak, P. de Peretti, M. Roszczynska, G. Rotundo, R. Savona, S. Stefani, and I. Vrontos SYRTO Project Final Conference, Paris – February 19, 2016
  • 2. “Socio-Finance: price formation is a sociological phenomenon.
  • 3. “Will consider human decision making to happen in two different ways i) direct communication between people or ii) indirect communication between people through a medium, say price index (Finance) -1st level: communication between individuals - 2nd level: communication between groups of individuals
  • 4. Dynamics of indirect communication of individuals through a price/index “
  • 5. Indirect decision making through a medium: people trading a financial market index
  • 6. The “symphony” of the market (complexity theory applied to financial markets)
  • 7. Synchronization of markets: “price-quakes”
  • 12. Phase transitions, symmetry break and financial moods The sand pile model
  • 16. Understanding Excessive Risk Taking Seen in Experiments on Financial Markets • Jørgen Vitting Andersen, CNRS, Centre d’Economie de la Sorbonne, University of Paris 1. • Research in progress, collaborators: Yifang Liu, Philippe de Peretti, Maxim Frolov, Roberto Savona, Hayette Gatfaoui, Rania Kaffel
  • 17. Individual versus collective risk taking • Individual risks: Men are known to be more risk loving compared to women. For real market traders see e.g. : “Endogenous steroids and financial risk on a London trading floor”, J. M. Coates and J. Herbert, PNAS, V.105, 16, 6167-72 (2008); “A note on trader Sharpe Ratios”, J. M. Coates and L. Page, PLoSONE, V.4, 11, e8036 (2009) • Collective risks: how does a group of traders with heterogeneous risk profiles influence the formation of market risks?
  • 18. Setup of experiments • Before each experiments individual risk profiles of participants were obtained from lotteries (C. A. Holt, S. Laury, The American Economic Review, V. 92, 1644 (2002)
  • 19. Here’s what happens when a group of men trade …
  • 20. Next we let a group of women trade …
  • 21. Looks similar to the group of men …
  • 22. Then another group of women … initially this group of women seem to trade differently…
  • 23. And indeed in this experiment the group of women creates a speculative state…
  • 24. Their speculation however was « dwarfed » compared to state created by yet another group of men…
  • 25. Claim: we need a fluctuation based framework in order to be able to understand behavior seen in experiments.
  • 28. Results: simulations of the $G Important: Symmetry break
  • 29. Taking the «temperature» of the market: predicting big price «swings » • Internal state of water? Insert a thermometer into the liquid. • Internal state of market? «slave » an agent based model to the price evolution. • Market in a “hot” or “cold” state.
  • 31. ACTION 0000 0  0000  0001 0 0100 1  1010   0101 1 0101 0110 1   1011  0111 1
  • 34. Dynamics of direct communication between individuals
  • 36. Changing the « bullishness » in a population via communication in subgroups
  • 37. During the day communication takes place in random subgroups
  • 38. During the day communication takes place in random subgroups
  • 39. During the day communication takes place in random subgroups
  • 45. Link between communication and its impact on the markets • r(t) the return of the market, RB(t)=[B(t)- B(t-1)]/B(t), and η(t) Gaussian distributed with zero mean and std. dev.:
  • 46. Link between transition probabilities and the market performance
  • 47. Main results: reproducing stylized facts
  • 48. Main results: maximum likelihood analysis
  • 49. Main results: maximum likelihood analysis
  • 51. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 320270 www.syrtoproject.eu This document reflects only the author’s views. The European Union is not liable for any use that may be made of the information contained therein.