Post on 03-Feb-2022
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Economic NetworksCoping with Crises in Complex Socio-Economic Systems
G. Caldarelli1
1IMT Alti Studi Lucca, ItalyInst. of Complex Systems CNR, Dep. Physics Univ. "Sapienza" Rome, Italy
MOCAP ConferencePostdam October 5th 2012
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Economic Network and Stability
Do Networksincrease/decreasestability?Consequently, can wecreate/remove them?How networks affecttraditional financialagents activity?
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Financial Networks and Systemic Risk
European Central Bank’s view1 on monitoring systemic stability:
1 develop new concepts and models2 take into account networks3 build integrated information systems4 look at new systemic risk indicators
1ECB Issing Report 2009Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Scientific activity
http://www.focproject.net
Forecasting
What is feasible:Estimating systemic riskSimulating plausible future scenarios
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Financial Networks and Systemic Risk
Systemic risk is the probability that a large fraction of financialinstitutions go in distress at the same time.
Systemic crisis1 have large costs for the real economy and the society,2 can emerge endogenously, without an external shock.
Financial institutionsform a global networkof financial ties (e.g.lending, or equity)Derivatives createadditional andundisclosed ties
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Economic Networks
Networks appear at all the levels of Economic SystemsOwnershipTradeLendings
Finally the social network of investors can help in identifyingtrends
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Web of Property
If property is diffused, bankruptcy is limited to a single companyand few subcompanies.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
World Property Net
An analysis of 43,000 companies worldwide showed that arelatively small group of banks own a disproportionally large setof them
1 Barclays plc2 Capital Group Companies Inc3 FMR Corporation4 AXA5 State Street Corporation6 JP Morgan Chase & Co.7 Legal and General Group plc8 ....
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Control
From public data we can checkwho owns whom (and thenumber of shares)Out of 30 Millions of companieswe can find 43,000Trans-national Companies (TNC)
Take home message
147 topholders in the core of the TNC network, control nearly40% of the TNC value.
S. Vitali, J. B. Glattefelder, S. Battiston, PLOS One, 6(10): e25995 (2011)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
WTW Data
Analysis is done on various years2
M.-A. Serrano, M. Boguñà and A. Vespignani, J. Ec. Int. & Contr. 2, 111-124 (2007)
2http://www.intracen.org/menus/countries.htm and http://www.tswoam.co.uk/world
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
WTW Modelling
GDPThe GDP determines the property of the network
A fitness model based on GDP reproduces the dataxi =
wi∑Nj=1 wj
f (xi , xj) =δxixj
1 + δxixj
where wi is the GDP of country i and δ > 0 is the only freeparameter of the model
G. Caldarelli, A. Capocci, P. De Los Rios and M.-A, Muñoz, PRL 89, 258702 (2002)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
The product taxonomy
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
The country product matrix
The information is a rectangular matrix Mcp(Mij = 0,1)In year 2000 we have 129 countries and 779 products.
M =
AlbaniaArgentina
...Zimbabwe
M11 M12 . . . M1Np
M21 M22 . . . M2Np...
.... . .
...MNc1 MNc2 . . . MNcNp
0111 0112 . . . 9710
0111 = Growing of Cereals, but rice0112 = Growing of Rice. . .
9710 =Service-Producing Activities of Private Households
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
The two “projected matrices”
TransposeIf M is Nc × Np then
C := MMT is Nc × Nc
P := MT M is Np × Np
Cij = [MMT ]ij = # products produced by both countries i , jPij = [MT M]ij = # countries producing both products i , j
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
A simple topological analysis
One simple analysis is to define a dissimilarity xij
Define the sets Si and Sjof i,j neighborsCompute the number ofelements N(S) in thesesets
Dissimilarity
One used formula is xij = 1− 2 N[S(i)∩S(j)]N[S(i)]+N[S(j)]
or equivalently
xij = 1− 2Cij
Cii + Cjj
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Network of competition
We can cluster Countries according to their products
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Network of sectors
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Network of sectors
PARTS AND ACCESSORIES OF MOTOR VEHICLES
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Trade Networks
Trade aggregated or not, is anetworkWe can learn from the structureof trade webWe can use networks to visualizehidden information
Take home message
Networks quantify, importance & centrality of countries andtheir competition
A. Tacchella et al., http://arxiv.org/abs/1108.2590 (2011)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Networks out of regulations
European Central Bank must avoid liquidity crises
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
ECB policy
ECB
Bank 1
Bank 2
Bank 3
The requirement to depositcash the 2% of all depositand debts into the nationalbanks generates tradingbetween banks.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Dataset
e-MIDThe data set analysed is the electronic broker market forinterbank deposit e-MID reference dataset. This data iscomposed by 586007 overnight transactions (i.e. payments ofloans done in 24 hours) concluded from January 1st ,1999 toDecember 31st ,2002. The network is composed by a set of Nbanks. The average number of active banks is 〈N〉 = 140connected by an average number of links L = 200. In case ofmultiple transactions among banks i and j we count just onelink.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
The structure of the market
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionWorld Trade Web Banks
Intraday Bank Networks
Large banks borrow franticallySmall banks mostly lendThe system is intrinsically fragile
Take home messageIn a complex systems reasonable requests produceunexpected results
G. De Masi, G. Iori, G. Caldarelli PRE 74 066112 (2007)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Querylogs
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AMZN Volumes
Date
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Vol
ume
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Trading VolumesQuery Volumes
Take home message
Queries are correlated with market trends
I. Bordino et al. http://arxiv.org/abs/1110.4784
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Dataset
In the period May 1st2010 - April 30th2011:we took market volumes of the 100 stocks in NASDAQ100;we looked in the querylog for:
the tickers (apple –> aapl)the name of the company (and in case cleaned the results);
we aggregate the data at the level of day (Googletrendsaggregates at the time width of week).
CorrelationWe measured a correlation between the two time series
For both datasets we focus on n = 250 working days during theabove period.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Pearson Correlation Coefficient
The time-lagged Pearson cross correlation r(δ) coefficientbetween two time series Xt and Yt is:
r(δ) =∑n
t=1(Xt − X )(Yt+δ − Y )√∑nt=1(Xt − X )2
√∑nt=1(Yt+δ − Y )2
(1)
X , Y are the sample average of the two time seriesδ is a time lag.To reduce short-term fluctuations, for each time series, we alsoaverage the signal at a given time t with the w − 1 previouspoints. We use windows of width w = 1,2,3,5.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Pearson Correlation Coefficient
Time evolution of query-logs normalised volumes for the ticker"NVDA" compared with the trading-volume of the "NVIDIACorporation". plain lines (positive time lag) are always larger thandashed lines (negative timelag). Therefore suggesting that searchvolumes anticipate trading
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Results
W CCF-5 -4 -3 -2 -1 0 1 2 3 4 5
1 0.03431 0.08416 0.09454 0.11437 0.19947 0.35302 0.28022 0.13923 0.08929 0.07361 0.057832 0.06015 0.09980 0.13057 0.17726 0.29583 0.40793 0.36126 0.22151 0.13363 0.10112 0.087733 0.07526 0.11693 0.16489 0.25045 0.35252 0.42378 0.39275 0.29334 0.19062 0.13354 0.108475 0.11506 0.17759 0.25169 0.32711 0.39177 0.43181 0.41197 0.35715 0.29148 0.22416 0.16403
Table : Average Cross-correlation time series for nasdaq-100companies (query: Ticker)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Granger Causality test
DefinitionWe want to determine whether a time series X (t) is useful inforecasting another time series Y (t).
X (t) Granger-causes Y (t) if Y (t) can be better predicted usingboth the histories of X (t) and Y (t) rather than using only thehistory of Y (t).The test can be assessed by regressing Y (t) on its owntime-lagged values and on those of X (t).An F-test is then used to examine if the null hypothesis thatY (t) is not Granger-caused by X (t) can be rejected.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Results
Dataset lag (days) Direction %p < 5% %p < 1% Avg reduction in RSSQ (100 tickers) 1 Q → T 48% 32% 5.2%Q (100 tickers) 1 T → Q 15% 5% 1.5%U (100 tickers) 1 U → T 48% 36% 5.4%U (100 tickers) 1 T → U 16% 4% 1.74%Q (100 tickers) 2 Q → T 52% 40% 6.9%Q (100 tickers) 2 T → Q 22% 8% 2.31%U (100 tickers) 2 U → T 53% 41% 7.6%U (100 tickers) 2 T → U 25% 8% 2.8%Q (82 tickers) 1 Q → T 53.66% 36.59% 5.71%Q (82 tickers) 1 T → Q 14.6% 6.1% 1.48%U (82 tickers) 1 U → T 53.66% 41.46% 6.04%U (82 tickers) 1 T → U 17.07% 4.88% 1.84%Q (82 tickers) 2 Q → T 57.32% 45.12% 7.66%Q (82 tickers) 2 T → Q 23.17% 7.32% 2.39%U (82 tickers) 2 U → T 57.32% 46.34% 8.35%U (82 tickers) 2 T → U 22.99% 8.54% 2.97%
Table : Granger Causality Test
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Financial and Economic Networks and Systemic Risk
RobustnessAs in infrastructural networks, economic and financial systemsform an interconnected web with unexpected fragilities
If you consider a financial network you would like to knowwhich ties connect the various financial institutions;what is the global effect of bankruptcy;how network theory can help in controlling and possiblyrecovering.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Importance for policy makers
Network theory allows to sort out relevant information. Inparticular we can use a centrality measure to assess locallythe global impact that a default might have.
Centrality measure:more central moresystemicallyimportantit’s not just a rankingmeasures total losscaused to the system
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Take-Home Message
DebRank is a novel indicator to identifySIFI (Systemically Important Financial Institutions)groups of SIFI
DebtRank overcomes some limitations instandard stress-test techniques incl. default-cascade algostandard network mesures (e.g. betweenness, centralityetc.)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application Conclusion
Take-Home Message
DebRank is a novel indicator to identifySIFI (Systemically Important Financial Institutions)groups of SIFI
DebtRank overcomes some limitations instandard stress-test techniques incl. default-cascade algostandard network mesures (e.g. betweenness, centralityetc.)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionCentrality Dynamics
Which is the best choice?
Vertex Status (too big to fail)
Degree (too connected to fail)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionCentrality Dynamics
Terminology
Aij= amount invested by i in the funding of jAi= total amount invested by i defined as Ai =
∑l Ail
Ei = capital of i . When Ei < γ the institution defaultsγ= positive thresholdWij= impact of i on j defined as Wij = min{1,Aij/Ej}vi= relative economic value of i defined as vi = Ai/
∑l Al
di= debt of iφi=fragility of i defined as φ(i) = di/Ei
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionCentrality Dynamics
DebtRank Centrality
We starting from feedback centrality adapted to financialdistressA node is more important if it impacts on many valuableand important nodes
ci =∑
j
Wijvj +∑
j
Wijcj → c = (I −W )−1Wv
Centrality measures total outgoing flow, includingreverberationStrategy: keep real impact and tame the reverberation inthe cycles by excluding walks already visited
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionCentrality Dynamics
Debt Rank Dynamics
continuous variable hi ∈ [0, 1].3 possible states, undistressed, distressed, inactive:si ∈ {U, D, I}.Sf : set of nodes in distress at time 1. hi(1) = ψ ∀i ∈ Sf ;hi(1) = 0∀i /∈ Sf , and si(1) = D ,∀i ∈ Sf ; si(1) = U ∀i /∈ Sf .
hi(t) =min{
1,hi(t − 1) +∑
j
Wjihj(t − 1)}, where j | sj(t − 1) = D
si(t) ={
D if hi(t) > 0 & si(t − 1) 6= IU otherwise,
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionCentrality Dynamics
DebtRank value
DefinitionThe DebtRank of a set S is defined as
R =∑j∈S
hj(T )vj −∑j∈S
hj(1)vj
If S is the network and the initial condition is ’only one node indistress’ we can assess the effect of a single default on thewhole system.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionCentrality Dynamics
How to compute
43
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7 8
h=1
h=0
h=0
h=0
h=0 h=0
h=0 h=0
43
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65
7 8
h=1
h=0.5
h=0.5
h=0.5
h=0 h=0
h=0 h=0
43
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65
7 8
h=1
h=0.5
h=0.5
h=0.5
h=0.25 h=0.25
h=0 h=0
43
1
2
65
7 8
h=1
h=0.5+
0.125
h=0.5
h=0.5
h=0.25+
0.125
h=0.25
h=0.125 h=0.125
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
A Specific Case of Study: the FED
The Federal Reserve System (FED) is the central banksystem USA.FED serves as lender of last resort not excludingemergency lending facilities as, for example, the discountwindow.FED during the credit crisis of 2007, put in place severalemergency programs to assist individual institutions forinstance the support to the AIG, and the help extended toJP Morgan in acquiring Bear Sterns.The supreme court of the United States asked to releaseexposure data to the public in batches on Dec. 1, 2010,March 31 and July 6 of 2011.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
FED Data
The data is especially relevant to systemic risk because1 it covers specifically the emergency loans, i.e., those loans
for which FED is the lender of last resort.2 it allows to obtain insights into the financial fragility of many
key players in the US, as well as in the global financialsystem.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
The network of exposure
The original FED dataset consists of 30 thousands pdf pages.Each entry reports the name of the borrowing institution, thecredit channel used for financing, the origination date and thematurity date of each loan.
Bloomberg data
Bloomberg analysed the FED dataset releasing a set of excelfiles on Dec 23, 2011. Data consists of a set of 407 dailytimeseries’ of outstanding debt and market capitalizations.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
The time evolution
17/03/08 Bearn Sterns acquired by JP Morgan Chase (240 M US $)
18/09/08 Lehman Brothers default after payback of 38.5 M US$ to FED
16/09/08 Citigroup announced losses for 2.8 G US$
07/03/09 All institutions collectively got at the minimum of market capitalization
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
The star-like network
As always happens in this kind of data only partialinformation is availableWe got only the ties between FED and Institutions
Reconstructing informationWe took banks’ investment in each others equity share as aproxy of all exposures
(Orbis 2007 Database of 43060 TransNational Companies)Vitali S, Glattfelder JB, Battiston S, The Network of Global Corporate Control. PLoS ONE 6 e25995 (2011)
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
The plots
We take about 20 amongst the largest financial institutionswith debt larger than 5 Millions USDThe nodes are positioned within a circle of radius R=1,centred in 0.The distance of each node from the center is proportionalto DebtRankAlso the angle (∈ [0,10π]) grows in the spiral withDebtRank
Scatter plot of DebtRank versus asset size,fraction (in percentage) of the total of the asset size in thenetwork.The size of each bubble is proportional to the outstandingdebt of the institutionwhile the color reflects its fragility
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
periods 1-2
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
periods 3-4
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionData Figures
periods 5-6
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Systemic Risk - Global Perspectives
Perfect storm, unforeseen.Big banks are to big-to-fail. We have to rescue the financialsystem.Liquidity drainage: call the plumber and squeezelow-income tax payers
Surely, an issue isNetwork architecture, resilience, liquidity flowI.e. a “plumbing” issue
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Systemic Risk - Global Perspectives
Perfect storm, unforeseen.Big banks are to big-to-fail. We have to rescue the financialsystem.Liquidity drainage: call the plumber and squeezelow-income tax payers
Surely, an issue isNetwork architecture, resilience, liquidity flowI.e. a “plumbing” issue
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Systemic Risk - Global Perspectives
Perfect storm, unforeseen.Big banks are to big-to-fail. We have to rescue the financialsystem.Liquidity drainage: call the plumber and squeezelow-income tax payers
Surely, an issue isNetwork architecture, resilience, liquidity flowI.e. a “plumbing” issue
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Systemic Risk - Global Perspectives
Perfect storm, unforeseen.Big banks are to big-to-fail. We have to rescue the financialsystem.Liquidity drainage: call the plumber and squeezelow-income tax payers
Surely, an issue isNetwork architecture, resilience, liquidity flowI.e. a “plumbing” issue
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Systemic Risk - Global Perspectives
Perfect storm, unforeseen.Big banks are to big-to-fail. We have to rescue the financialsystem.Liquidity drainage: call the plumber and squeezelow-income tax payers
Surely, an issue isNetwork architecture, resilience, liquidity flowI.e. a “plumbing” issue
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Systemic Risk - Global Perspectives
Perfect storm, unforeseen.Big banks are to big-to-fail. We have to rescue the financialsystem.Liquidity drainage: call the plumber and squeezelow-income tax payers
Surely, an issue isNetwork architecture, resilience, liquidity flowI.e. a “plumbing” issue
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Indicators of Systemic Importance
Standard stress-test approach based on default cascades.Issues:
only default mattersalmost never systemic defaults
Standard network approachmeasures of centrality do not have an economic meaning
eigenvector centralitybetweenness, degree, closeness, etc.
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
Conclusion
The economy is composed by a series of networks
Individuals determining markettrendsCompanies trading andinteractingBanks providing liquidityCountries organizing trade andproduction
Take home message
All the above features can be explained by network of networks
Guido Caldarelli FOC project and Economic Networks
Introduction Ownership Trade and lending Queries DebtRank DebtRank Application ConclusionImportance FOC project
The FOC Consortium
CNR-IMT (RM-LU, I) A. Gabrielli, F. Pammolli, L. PietroneroU. Marche (Ancona, I) L. Bargigli, D. Delli Gatti, M. Gallegati
ETH (Zürich, CH) S. Battiston, M. Puliga, F. SchweitzerCITY (London UK) G. Iori, S. JafareySaïd BS (Oxford UK), F. Reed-Tsochas, A. GerigYahoo! (Barcelona) A. Gionis, I. Bordino, A. Ukkonen, I. WeberECB (Frankfurt, EU) M. Grande, O. Castrén
From September 2011JSI (Ljubljana, SLO) I. Mozetic, M. GrcarRBI (Zagreb, CRO) T. Smuc, V. ZlaticELTE (Budapest, H) I. Kondor
From March 2012BU (Boston, USA) I. Vodenska, H. Gene Stanley
KY (Kyoto, J) Y. Fujiwara, H. Aoyama,
Guido Caldarelli FOC project and Economic Networks