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AGENTS IN THE GLOBAL NETWORK: SELF-ORGANIZATION AND INSTABILITIES Kings College, Financial Mathematics London January 19, 2010 5:30pm Luciano Pietronero.

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Presentation on theme: "AGENTS IN THE GLOBAL NETWORK: SELF-ORGANIZATION AND INSTABILITIES Kings College, Financial Mathematics London January 19, 2010 5:30pm Luciano Pietronero."— Presentation transcript:

1 AGENTS IN THE GLOBAL NETWORK: SELF-ORGANIZATION AND INSTABILITIES Kings College, Financial Mathematics London January 19, 2010 5:30pm Luciano Pietronero Collaborators: Valentina Alfi, Matthieu Cristelli and Andrea Zaccaria Institute of Complex Systems, CNR, Rome Italy University of Rome Sapienza and Centro Fermi, Rome (WEB page:

2 After the subprime crisis there have been many conjectures for the possibile origin of this instability. Most suggestions focus on concepts like collective behavior, contagion, network domino effect, coherent portfolios, lack of trust, liquidity crisis, leverage effect and, in general psycological components in the traders behavior. Standard risk analysis is usually linear analysis within a cause-effect relation. Possibly new insight to the risk problem could profit could be inspired by complex systems theory. Different perspective in which the interaction between agents (direct or in direct) is explicitely considered together with the idea that the system may become globally unstable in the sense of self-organized criticality. The analysis is therefore shifted from the linear cause-effect relation to the study of the possibile (nonlinear) intrinsic instabilities. We discuss some steps towards a systematic analysis of these ideas based on agent models and order book models together with the statistical analysis of experimental data. The final objective of these studies would be to define the characteristic properties of each of the above concepts from the models and then to identify their role and importance in the real financial markets. To achieve this goal it is essential to increase the number and quality of the Stylized Facts which are identified from the massive data available Summary

3 Collegium Budapest, October 8 - 10, 2009 Financial risk, market complexity and regulations. (I. Kondor et al.) M. Summer (National Bank of Austria) Model of network, not much evidence for domino effect, insolvency not the key point, leverage important - debt overhang J. Langsam (Morgan Stanley USA) Proposition of US Institute of Finance ( Large psicological component, Lack of network overview Role of direct links vs general Trust Systemic risk definition (metric); data collection; service agency J. Kiraly, A. Farkas (National Bank and Hungarian Authority) Contagion was not through toxic; Leverage problem

4 M. Gordy (Federal Reserve USA) Traditional approach, not much Complexity, big discussion Procyclicality: Nonlinear amplification feedback Not topology but the fact that many large institutions behaved the same G. Barone-Adesi (Institute of Finance, Lugano CH) Strongly correlated portfolios is risky P. Hartmann (European Central Bank, FKF.) Truly systemic financial crisis; Risk suppression; Regulation European systemic risk board Powerful feedback and amplification, nonlinearity 6% of US market has led to a global collapse M. Tisset (Banque de France) Resileince, Liquidity


6 Official reports on the Crisis: Mid 2008: Danish Central Bank Worst scenario: subprime continues, US recession, increase of 2.5% of interbank interest. Basic stability of the bank system !!! Feb. 25, 2009: de Larosiere EEC Report Financial crisis - real economy - No more Trust Risk mispriced, excessive leverage Regulations on individuals but not on macro systemic risk - Contagion - Correlations NB: SAME STARTING INFORMATION BUT COMPLETELY DIFFERENT CONCLUSIONS (A FEW MONTHS LATER) PROBLEMS WITH CAUSE-EFFECT RELATION

7 Classic theory of economics: (New Scientist editorial, 2008) Situation of equilibrium with agents (quasi) rational and informed Important price changes correspond to new information which arrives on the market This information modifies the ratio between offer and demand and then also the price Relation cause - effect

8 Problems with the classic theory: Great cathastrofic events like the 87 crash, the Inernet bubble of 2000 and the recent case of the Subprimes do not seem to have any relation with specific events or new information Also the Stylized Facts at smaller scales cannot be really explained within the standard model Breaking of the cause-effect relation: then what is the real origin of large price changes?

9 Physics, Complexity, Socio-Economics : Physics: try to discover the laws of nature Economics: are there laws to be discovered? evolutive elements, adaptivity, the whole society is involved Complexity: new vision and possible point of contact Simplicity vs Realism (reproducing vs understanding)

10 NEW perspective: The market seems to evolve spontaneously towards states with intrinsic instability which then collapse or explode (endogenous) triggered by minor perturbations Importance of social interactions (herding) effects especially in situations of uncertainity with respect to the fundamentals of economics (fear, panic, euphoria) Breaking of the cause-effect relation and of the traditional economic principles Relation to Critical phenomena and SOC in physics(?) Feedback, amplification, nonlinearity

11 MODELS AND BASIC PROBLEMS INTERDISCIPLINARY APPLICATIONS Ising * (1911) Scaling, Criticality (64 - 70) and RG Group (>72) Percolation* (70-80) Glasses Spin Glasses* etc.(>74) Deterministic Chaos* (78) Fractal Geometry (80-90) Polymers and Soft Matter Dynamical Systems and Turbulence Fractal Growth Physical Models: DLA/DBM* (82-84) Selforganized Criticality Sandpile* (87) Granular Systems (90) Minority Game (97) Rare Events Complex Networks (>2000) Condensed Matter problems Phase Transitions Magnetic Systems Bio-inspired Problems Astrophysics Geophysics Information Theory Optimization Economics and Finance Social Sciences (Random Walk, Bachelier 1900) Agent Based Models (very many) Apply old Models or develop New Models? Universality?

12 In nature trees are alike but not identical. Similarity and common basic structure but no strict universality. Exponents can therefore depend on specific situations: richness to be explored. Universality?

13 OUR PERSPECTIVE Workable ABM, clear math and properties New elements: N variable, Stylized Facts due to Finite Size Effects, Self-organization Approximate scaling, no strict universality: effective exponents depend on situation Liquidity crises: Order Book Model for finite liquidity ABM in the Global Network, Leverage Coherence, correlated portfolios, similar behavior; risky

14 Stylized Facts (Very few; Universal?): Arbitrage -- Random Walk (B&S) Fat tails, Volatility Clustering etc. AND ALSO Non stationarity Self-organization, Liquidity Global Network

15 ABM model with moving average-based strategies (V. Alfi, L.P., A. Zaccaria EPL 2008) N players: N F fundamentalists N C chartists At each time step, each agent can change its strategy with probabilities Price formation (price change related to excess demand; liquidity problem) (Lux Marchesi 2000; Other suggestions can be easily implemented; i.e. W. Shaw 2009)

16 Switching between F and C strategies FC fluctuations Origin of the Finite size effects Kirman 1993; Alfarano&Lux 2006

17 N=50 N=500 N=5000 Nc Intermittency OK (Stylized Facts) Too low fluctuations Too fast fluctuations NB: For N diverging fluctuations are suppressed. Therefore Stylized Facts correspond to finite size effects

18 Asymmetric case: Basically Fundamentalists with bubbles due to Chartists (assumption of asymptotic stability: not quite realistic in these times) If the transition probabilities are symmetric the equilibrium distribution is bimodal or unimodal depending on the parameters With asymmetric transition probabilities the scenario is richer

19 TENDENCY TO FUNDAMENTALISM: INSTITUTIONAL INVESTORS IN QUIET TIMES relative number of chartists For large value of N chartists are suppressed bimodal region

20 N dependence of price fluctuations: Switching effect between Fundamentalists and Chartists N =50 N 5000 N=500 Intermittent behavior: OK (Lux, Stauffer 2000) Changes of opinion are too fast Too stable and dominated by fundamentalists Puzzle: Interesting fluctuations appear at finite N and disappear for infinite N; unlike Critical Phenomena in Physics

21 N=1 M=10 b=5·10-4 K=0.05 B=1 g=0.1 s=1 NB: even a single agent can show some intermittency (Pf = 0) Bursts of price fluctuations appear spontaneously and are clearly due to Chartists dynamics (Possibility of analytical studies)

22 N=100 M=10 b=1·10-3 K=0.002 B=1 g=0.1 s=1 N=100 NB: All the parametrs are now in full control BUT fine tuning is always necessary

23 Autocorrelation functions of returns and square returns NB: SF arise from Finite Size Effects Probability density function of price-returns

24 What is really N or N*? In general the number of agent N is fixed in the Agent Models This idea originates probably from Stat Phys but it is rather unrealistic for trading Nonstationarity, route to Self-organization NB: Strongly correlated portfolios lead to an effective reduction of N* and therefore towards less stable situations. Comments by M. Gordy, G. Barone-Adesi, M. Marsili M. Buchanan (hedge funds with similar portfolio) Important: Estimate N* from real market data In the model the N agents may influence by the herding rules, but if they a priori behave the same this decreases N (network, leaders, gurus, media, panic etc.)

25 ACTION ~ CONSTANT In this case N* increases N C and N F detect interesting signals and are stimulated to take an action In this case little action is stimulated N* drops

26 ACTION INCREASE OF N* This resembles the GARCH phenomenology but At a microscopic level Following our concept: II

27 Therefore there is a multiplicative nature of correlations which leads to a persistence in the value of (high or low). CONCEPTUAL FRAMEWORK FOR FAT TAILS AND VOLATILITY CLUSTERING (NONSTATIONARITY) MICROSCOPIC AGENT-LIKE INTERPRETATION OF ARCH-GARCH PHENOMENOLOGY

28 Why no arbitrage ? Any action (N*) increases but price trend is much more complex Therefore: much more information is crucial for the sign of the price return

29 Towards Self-organization Asymmetric case: Basically Fundamentalists with bubbles due to Chartists (not quite realistic in these times) If the transition probabilities are symmetric the equilibrium distribution is bimodal or unimodal depending on the parameters With asymmetric transition probabilities the scenario is richer

30 (red N=50; black N=500; green N=5000) ABM results for the Self-organized state Real data from NYSE stock NB: largest peaks Correspond to the Intermediate case. It takes some stability in the C state to develop a bubble

31 (red N=50; black N=500; green N=5000) NB: Black (N=500) is the only case leading to stylized facts

32 Basic criterion for Self-Organization: Agents decide whether trading (or not) depending on the price movements they observe (Competition with other investments in the Global Network) Stable prices: Less trading Large action (price movements): More trading (Euphoria, bubbles, panic, crashes) Caution: some agents may prefer a stable market and be scared by fluctuations. This would require an analysis of different time scales and, in any case, these agents certainly do not produce the Stylized Facts

33 On the basis of the calculated volatility each agent has a probability to enter/leave the market if the volatility is above/under a certain threshold Each agent calculate the price-volatility on the previous T steps

34 Self-Organization in action: Different starting N (50, 500, 3000) evolve and finally converge to the Quasi-critical state (N=500) which corresponds to the Stylized Facts N* N1N1 N2N2

35 Linear dynamics; N = 500; Heterogeneity with respect to their time horizon Volatility clustering is decreased because the behavior is less coherent Apparent power law behavior but no fundamental critical phenomenon


37 Multiplicative dynamics: Extreme sensitivity to parameter region. Slightly different parameters lead to very different Fat Tails

38 Comparison between linear and multiplicative dynamics Fat Tails are usually larger for the Multiplicative case

39 Power laws and universality? Herding: naturally leads to population switching (i.e. F vs C) For a given N and a single time horizon this leads to a characteristic time scale. Distribution of trading horizons leads to many time scales Nonstationarity: key element for the Self-organization traders may decide NOT to play or to play variable amounts of shares (volume) Switching situation: Finite Size Effects Strong deviations from gaussian behavior but not necessarily critical with universal power laws. NB. Different opinions about data analysis: HE Stanley; R. Cont; J. Kertesz; D.Sornette; C. Tsallis …

40 Fat Tail effective exponents as a function of model properties: More Chartists lead to larger Tails Nonuniversality leads to a richer interpretation of data

41 Real data: difficult to define a single exponent Alternative for non gaussianity: Kurthosis

42 FC Kurthosis vs Market Sentiment: Chartists or Fundamentalists Stabilization vs destabilization not optimist vs pessimist

43 ABM real General Motors Lehman Recent Crisis Pf=??? Subprime ABM ideal

44 ABM + Environment BASIC ANSATZ Fundamentalists dominate in the long run (?) But in a complete model this may require evolution and adaptation for all possible instabilities

45 Fundamentalists are discouraged In a global crysis N* is strongly suppressed Different Situation: if External fluctuations affect all properties but mostly the estimate of the Fundamental Price

46 Back to Liquidity problem Liquidity: seems more important than volume or news for price changes Microscopic model for the order book & finite liquidity (crisis). This should be included in a realistic ABM

47 Order Book & ABM Order book model In a typical Agent-Based Model (ABM) the price evolution is a coarse-grained clearing/adjustment mechanism that does not take into account the liquidity of the market real markets Therefore we need to investigate the microscopic mechanisms for price formation in order to find β(g)

48 Order Book in a nutshell (Microscopic version of Farmers zero intelligence model) p ΔpΔp ask bid best bid b(t) best ask a(t) limit order market order spread s(t) limit order bid può cadere tra [-,a(t)] limit order ask può cadere tra [b(t),+] V p ΔqΔq ask bid best bid b(t) best ask a(t) incoming limit order incoming market order spread s(t) a buy limit order can be placed in the interval [-,a(t)] a sell limit order can be placed in the interval [b(t),+] p(t)

49 Order Book regimes We can identify two different regimes in the order book dynamics Very liquid market Small price variations Behavior similar to a continuous system Illiquid market: discreteness Large price variations The discreteness of the system is crucial

50 Price Impact Function p

51 Liquid market p ΔpΔp bid best bid b(t) best ask a(t) market order spread s(t) V p ask bid b(t)a(t) market order p(t) a(t+ 1 )p(t+ 1 ) p(t+ 1 )-p(t) = fraction of a tick

52 Illiquid market p ΔpΔp bid best bid b(t) best ask a(t) market order spread s(t) V p ask bid b(t) a(t) market order p(t) p(t+ 1 ) a(t+ 1 ) p(t+ 1 )-p(t) = several ticks

53 Price Impact Function The Price Impact Function (PIF) can be considered as the response function of a stock, that is... Markets are not in a linear response regime If an agent submit a virtual market order of volume ω at time t, what will be the average price change at time t+τ? The Price Impact Function of real market is a concave function with respect to the order volume (Lillo, Farmer, Mantegna 2003) (J.P. Bouchaud et al 2004)

54 Price Impact Surface We want to study the role played by liquidity/granularity in price response but the normal PIF is calculated averaging on order book configurations with different liquidity/granularity where g is a measure of liquidity/granularity We define the Price Impact Surface (PIS) which is instead a function of volume and liquidity/granularity

55 Price Impact Surface - 1 is concave as one measured in real order book Model result for τ=400 and k=4

56 Equity Market Impact Function: Different results ??? R. Almgren et al + F. Abergel (Paris): exponent = 0.6 Lillo, Farmer, Mantegna: exp = 0.2 - 0.4 J.P. Bouchaud et al: log behavior Present Model: 0.6 seems to be natural but prefactor is also important More Liquid >

57 If we rescale the PIS with the average impact function that is proportional to we observe a quasi-collapse in a unique curve (in particular for small values of the order volume) Therefore the PIS can be approximately factorized as: Price Impact Surface - 2 Relation to ABM: Small N leads to more Sparse orders and more granularity. N dependent price formation

58 Stylized Facts of Order Book

59 Summary from ABM: Globalization - Social interactions - Network: New opportunities and New Risks. Feedback, amplification, nonlinearity Fat tails and Stylized Facts arise from finite size effect Nonuniversality leads to richer analysis tool General sentiment vs direct links, effective N* Network oriented approach - New indicators for a systemic risk approach - Microscopic data and network of mutual exposure. Contagion - correlations. Trust: definition in math terms and more attention to peoples behavior, herding etc. More scientific oriented tests - less ideology

60 Key Concepts: TO IDENTIFY FROM REAL DATA Market sentiment, stabilizing vs destabilizing The effective independent agents N* in a market Analysis of Herding, Contagion, Correlations Liquidity analysis of order book Network oriented approach - Direct interaction vs global Trust. Coherence problem, similar behavior





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