Interconnectedness amongst financial institutions has been implicated as a significant contributing factor to the 2008-09 crisis, where shocks were amplified to becoming systemic events. We study two types of networks: correlation network based on publicly traded bank returns, and a physical network based on interbank lending transactions and discuss various analytic approaches for studying their connectivity patterns over time. Some key findings include: (i) both networks behave similarly pin the period preceding the 2008-09 crisis, (ii) during the crisis the correlation network shows an increase in interconnectedness while the physical network highlights a marked decrease in interconnectedness. Moreover, these networks respond differently to monetary and macroeconomic shocks. Physical networks forecast liquidity problems, while correlation networks forecast financial crises.
In an increasingly global and interconnected economy, determining risk profiles is very critical. Read our whitepaper on the latest in network analysis theories to analyze systemic risk profiles not only in finance, but in any industry where there is a strong reliance on a network of people and goods.
More than half of senior retail, commercial and investment bankers say they lack sufficient data to support robust risk management. This report, sponsored by SAP, looks at how banks are using Big Data to improve risk management and compliance performance. Find out more and watch video: http://bit.ly/RComp1
Interconnectedness amongst financial institutions has been implicated as a significant contributing factor to the 2008-09 crisis, where shocks were amplified to becoming systemic events. We study two types of networks: correlation network based on publicly traded bank returns, and a physical network based on interbank lending transactions and discuss various analytic approaches for studying their connectivity patterns over time. Some key findings include: (i) both networks behave similarly pin the period preceding the 2008-09 crisis, (ii) during the crisis the correlation network shows an increase in interconnectedness while the physical network highlights a marked decrease in interconnectedness. Moreover, these networks respond differently to monetary and macroeconomic shocks. Physical networks forecast liquidity problems, while correlation networks forecast financial crises.
In an increasingly global and interconnected economy, determining risk profiles is very critical. Read our whitepaper on the latest in network analysis theories to analyze systemic risk profiles not only in finance, but in any industry where there is a strong reliance on a network of people and goods.
More than half of senior retail, commercial and investment bankers say they lack sufficient data to support robust risk management. This report, sponsored by SAP, looks at how banks are using Big Data to improve risk management and compliance performance. Find out more and watch video: http://bit.ly/RComp1
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based ...SYRTO Project
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based indicators for systemic risk of Euro area sovereigns and for Euro area financial firms” by Lucas et al. - Carsten Detken.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Applications of Network Theory in Finance and ProductionKimmo Soramaki
In recent years, network theory has proved useful in applications ranging from cancer research to the social graph. Applications of network theory are becoming ever more present also in economics and finance, with network analysis providing answers to questions where traditional analysis methods are weak, and leading to improved models across wide types of risks. This presentation discusses three real-world applications of network theory: identifying pivotal countries and payment corridors from the global network of payment flows, using industry level value chains for casualty risk modeling, and using asset correlation networks for detecting emerging and systemic risks.
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based ...SYRTO Project
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based indicators for systemic risk of Euro area sovereigns and for Euro area financial firms” by Lucas et al. - Carsten Detken.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Applications of Network Theory in Finance and ProductionKimmo Soramaki
In recent years, network theory has proved useful in applications ranging from cancer research to the social graph. Applications of network theory are becoming ever more present also in economics and finance, with network analysis providing answers to questions where traditional analysis methods are weak, and leading to improved models across wide types of risks. This presentation discusses three real-world applications of network theory: identifying pivotal countries and payment corridors from the global network of payment flows, using industry level value chains for casualty risk modeling, and using asset correlation networks for detecting emerging and systemic risks.
Financial Network Analysis - Talk at Oslo University 25 March 2011Kimmo Soramaki
Kimmo will introduce research in financial network analysis. He will talk about recent research on networks across various disciplines and discuss how network analysis can be used to gain a better understanding of the financial system and enhance its stability. He will also present a new open source tool ( www.financialnetworkanalyzer.com ) that can help policymakers and researchers in the area.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
Data Center for systemic risk - Michele Costola. July, 2 2014SYRTO Project
Data Center for systemic risk - Michele Costola
SYRTO Code Workshop
Syrto Workshop on Systemic Risk Policy ISSUES Bundesbank-ECB-ESRB
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Project 4 Threat Analysis and ExploitationTranscript (backgroun.docxstilliegeorgiana
Project 4: Threat Analysis and Exploitation
Transcript (background):
You are part of a collaborative team that was created to address cyber threats and exploitation of US financial systems critical infrastructure. Your team has been assembled by the White House Cyber National security staff to provide situational awareness about a current network breach and cyber attack against several financial service institutions. Your team consists of four roles, a representative from the financial services sector who has discovered the network breach and the cyber attacks. These attacks include distributed denial of service attacks, DDOS, web defacements, sensitive data exfiltration, and other attack vectors typical of this nation state actor. A representative from law enforcement who has provided additional evidence of network attacks found using network defense tools. A representative from the intelligence agency who has identified the nation state actor from numerous public and government provided threat intelligence reports. This representative will provide threat intelligence on the tools, techniques, and procedures of this nation state actor. A representative from the Department of Homeland Security who will provide the risk, response, and recovery actions taken as a result of this cyber threat. Your team will have to provide education and security awareness to the financial services sector about the threats, vulnerabilities, risks, and risk mitigation and remediation procedures to be implemented to maintain a robust security posture. Finally, your team will take the lessons learned from this cyber incident and share that knowledge with the rest of the cyber threat analysis community. At the end of the response to this cyber incident, your team will provide two deliverables, a situational analysis report, or SAR, to the White House Cyber National security staff and an After Action Report and lesson learned to the cyber threat analyst community.
Step 2: Assessing Suspicious Activity
Your team is assembled and you have a plan. It's time to get to work. You have a suite of tools at your disposal from your work in Project 1, Project 2, and Project 3, which can be used together to create a full common operating picture of the cyber threats and vulnerabilities that are facing the US critical infrastructure.
To be completed by all team members: Leverage the network security skills of using port scans, network scanning tools, and analyzing Wireshark files, to assess any suspicious network activity and network vulnerabilities.
Step 3: The Financial Sector
To be completed by the Financial Services Representative: Provide a description of the impact the threat would have on the financial services sector. These impact statements can include the loss of control of the systems, the loss of data integrity or confidentiality, exfiltration of data, or something else. Also provide impact assessments as a result of this security incident to the financial ...
Financial Networks and Financial StabilityKimmo Soramaki
The recent global financial crisis has illustrated the role of financial linkages as a channel for the propagation of shocks. It also brought to the fore the concept that institutions may be “too interconnected to fail”, in addition to the traditional concept of being “too big to fail”.
Identifying Key Factors Driving Platform Ecosystem to Collapseijtsrd
Many researchers in many fields have experienced tipping point to their complex systems as in financial markets, in ecological system , but no one has experienced in platforms ecosystem systems. One of the biggest issues those platforms ecosystem can face is the collapse. The risk of approaching to the tipping point is unknown. Complex dynamical system ranging from ecosystem to economy can collapse anytime, but predicting the point where the collapse can occur is difficult. We built a mathematical model S C model which incorporating the dynamics, interactions and mutualistic network for platform ecosystem. We use this model to predict the key factors driving platforms ecosystem to collapse. To get our predictions we used an approximation method to get rid from complexity without losing much generality and still explain the same dynamics. To achieve our results we used matlab software and solved the reduced model. The inevitable factors that lead to collapse are suggesting to be used as early indicators of dramatic changes. We developed a system dynamics model of platform group of suppliers and consumers that includes growth, churn, competition, alliance, negative interaction and mutual interaction between users. We use this model to simulate various development paths by varying different factors, which affect the platform’s ecosystem model. Our simulation results show that ds, dc, Bij, ij, and h are the key factors driving the platform ecosystem to collapse. Lamia Loudahi "Identifying Key-Factors Driving Platform Ecosystem to Collapse" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51825.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/51825/identifying-keyfactors-driving-platform-ecosystem-to-collapse/lamia-loudahi
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
Introduction to Indian Financial System ()Avanish Goel
The financial system of a country is an important tool for economic development of the country, as it helps in creation of wealth by linking savings with investments.
It facilitates the flow of funds form the households (savers) to business firms (investors) to aid in wealth creation and development of both the parties
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the telegram contact of my personal pi merchant to trade with
@Pi_vendor_247
what is the best method to sell pi coins in 2024DOT TECH
The best way to sell your pi coins safely is trading with an exchange..but since pi is not launched in any exchange, and second option is through a VERIFIED pi merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and pioneers and resell them to Investors looking forward to hold massive amounts before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade pi coins with.
@Pi_vendor_247
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
how can i use my minded pi coins I need some funds.DOT TECH
If you are interested in selling your pi coins, i have a verified pi merchant, who buys pi coins and resell them to exchanges looking forward to hold till mainnet launch.
Because the core team has announced that pi network will not be doing any pre-sale. The only way exchanges like huobi, bitmart and hotbit can get pi is by buying from miners.
Now a merchant stands in between these exchanges and the miners. As a link to make transactions smooth. Because right now in the enclosed mainnet you can't sell pi coins your self. You need the help of a merchant,
i will leave the telegram contact of my personal pi merchant below. 👇 I and my friends has traded more than 3000pi coins with him successfully.
@Pi_vendor_247
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
US Economic Outlook - Being Decided - M Capital Group August 2021.pdfpchutichetpong
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
1. OECD – ECLAC WORKSHOP
New tools and methods for policy making
19 May 2014
Session 1:
Multi-Agent Financial Network Models
And Global Macro-net Models:
New Tools for Macro-Prudential Policy
Sheri MARKOSE (scher@essex.ac.uk)
Economics Dept. University of Essex,
The software used in network modelling was developed by Sheri Markose with
Simone Giansante and Ali Rais Shaghaghi
2. Roadmap 1 : Why MAFNs/Macro-Nets for Macro-
prudential policy ?
• Two methodological problems of financial contagion and systemic
risk : (i)Paradox of Volatility and the pitfalls of market price data
based systemic risk measures hence structural bilateral data based
networks modeling needed (ii) Non- trivial Negative Externalities
problem → the need for holistic visualization
• Some Applications: Systemic Risk From Global Financial
Derivatives Modelled Using Network Analysis of Contagion and
Its Mitigation With Super-Spreader Tax
• Global Macro-nets integrated with Real Side Sectoral Flow of
Funds to assess extent of economic imbalance from
financialization and also from global flows
• Some insights from Indian Financial System and Bilateral Data
based Network Modeling: Pioneering first full bilateral digital
map of financial system (Brazil and Mexico also mandating
bilateral data)
3. Macro-prudential: Systemic Risk
Management With Networks
• Holistic Visualization to overcome fallacy of composition type errors
• ICT data base driven multi-agent financial networks: glorified data
visualizations → Andrew Haldane/ Mark Buchanan Star Trek Vision
• Causal Connections v Statistical Analysis
• Minimum three elements are needed bilateral financial data
:Contracts, Counterparties, Maturity Buckets
• Integration and automation of financial data bases in a MAFN
framework aims to transform the data from a document or record
view of the world to an object-centric view (see Balakrishnan et. al.
2010), where multiple facts about the same real-world financial
entity are accessed to give a composite visualization of their
interactions with other such entities in a scalable way.
4. Roadmap 2: Systemic Risk Analytics
• Stability of Networks and Eigen-Pair Analysis:
Markose et. al. (2012)
• 3 main questions of macro-prudential regulation :
(i)Is financial system more or less stable?
(ii)Who contributes to Systemic Risk ?
(iii)How to internalize costs of systemic risk of
‘super-spreaders’ using Pigou tax based on
eigenvector centrality: Management of moral
hazard, Bail in vs Bail out : How to Stabilize system
using EIG Algorithm ?
• Generalization to multi-layer networks
● Conclusions
5. Mark ( (i )Market Data based Statistical Models of Systemic
Risk : A Case of Steadfast Refusal to Face Facts ( á la
Goodhart, 2009) ? Absence of Early Warning Signals
Major drawback of market price based systemic risk measures: they
suffer from paradox of volatility(Borio and Drehman ,2009) or paradox
of financial stability issues first addressed by Hyman Minsky (1982).
Market based statistical proxies for systemic risk (eg Segoviano and
Goodhart (2009) banking stability index, Contingent Claim Analysis
Distance to Distress Index of Castern and Kavounis (2011)) at best
contemporaneous with the crisis in markets, at worst they spike
after crisis. Laura Kodres et al IMF WP /2013/115 now call market
based systemic risk indices Coincident and Near Coincident
Systemic Risk Measures: conceded absence of early warning
capabilities.
As credit growth boosts asset prices, CDS spreads and VIX indices
which are inversely related to asset prices are at their lowest precisely
before the crash when asset prices peak → Also procyclicality of
leverage Adrian and Shin (2010, 2011).
6. Banking Stability Index (Segoviano, Goodhart 09/04) v
Market VIX and V-FTSE Indexes : Sadly market data based
indices spike contemporaneously with crisis ; devoid of requisite info for
Early Warning System
7. “Paradox of stability” : Stock Index and Volatility Index Paradox of
Volatility (Borio and Drehman(2009); Minsky (1982)) Volatility low
during boom and at local minimum before market tanks : hence misled
regulators “great moderation”
8. IMF WP /2013/115 Market Data Based Systemic Risk Measures:
Coincident or Near Coincident Devoid of Early Warning Few Weeks at
Best
Arsov et. al. (2013) design IMF Systemic Financial Stress (SFS, black above)
index which records the extreme negative returns at 5 percentile of the (left) tail
for the joint distribution of returns of a selected sample of large US and Eurozone
FIs (Ibid Figure 4 ) Backtesting of popular systemic risk metrics (Red, above)
9. (ii) Fallacy of Composition In the Generation of
Systemic Risk/Negative Externalities: Holistic
Visualization Needed
Systemic risk refers to the larger threats to the financial system as a whole that
arise from domino effects of the failed entity on others.
At the level of the individual user micro-prudential schemes appear
plausible but at the macro-level may lead to systemically unsustainable
outcomes.
Example 1 : Risk sharing in advanced economies uses O-T-C derivatives.
Success of risk sharing at a system level depends on who is providing
insurance and structural interconnections involved in the provision of
guarantees.
Only 5% of world OTC derivatives is for hedging purposes
Credit Risk Transfer in Basel 2 gave capital reductions from 8% to 1.6% capital
charge if banks got CDS guarantees from ‘AAA’ providers
10. Structure of Global Financial Derivatives Market:Modern
Risk Management based on Fragile Topology that Mitigates
Social Usefulness (2009,Q4 204 participants): Green(Interest Rate),
Blue (Forex), Maroon ( Equity); Red (CDS); Yellow (Commodity); Circle 16
Broker Dealers in all markets (Source Markose IMF W, 2012)
11. Granular Banks and Non Bank Financial Intermediaries (Dec 2012)- Note that
insurance companies (H codes) mutual funds(G codes) and not banks (A-D
codes) are net liquidity providers: Fact can be missed in banks only models !
12. A Financial Intermediary is member of multiple financial
markets (multi-layer networks) How to calculate its
centrality across the different networks it is present ?Joint
Eigen-vector centrality
Multi-Layer Network with
common nodes in some
layers ; m markets
Single network
13. Castren and Racan (ECB 2012 WP) Phenomenal Global Macro-net
Model With National Sectoral Flow of Funds To Track Global Financial
Contagion! Only Problem- the Castren-Racan Systemic Risk Analytics
Fail to have Early Warning Capabilities
The circle in the center represents banking systems that are exposed to the cross
border liabilities of sectors (household, non bank corporate, public etc) within
countries. The latter with sectoral flow of funds are given in the outer circle
14. Castren-Racan (2012) Loss Multiplier Systemic Risk Measure(blue
lines) vs. Markose et al (2012, 2013) Maximum Eigenvalue of Matrix of
Net Liabilities Relative to Tier 1 Capital (green line)
Castren-Racan loss multiplier (blue lines), unfortunately, peaks well after crisis has
started and asset side of FIs is considerably weakened. Markose (2012,2013) direct
measure of maximum eigenvalue, (green line)of matrix of liabilities of countries relat
Tier1 capital of the exposed national banking systems, will capture growing instabili
network relative to distribution of capital buffers well ahead of actual crisis.
15. Global Macro-net plagued by within country
sectoral imbalances and global imbalances
only 8% of total lending for real investment
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%Other Financial Institutions
Secured Household (banks)
Secured Household (BSocs)
Unsecured Household
LBO targets
Commercial Real Estate
'core' PNFC
16. Network Stability and Systemic Risk Measure:
Why Does Network Structure Matter to Stability ?
lmax = 𝑁𝐶 s < 1 Formula for network stability
• My work influenced by Robert May (1972, 1974)
• Stability of a network system based on the
maximum eigenvalue lmax of an appropriate
dynamical system
• May gave a closed form solution for lmax in terms
of 3 network parameters , C : Connectivity , number
of nodes N and s Std Deviation of Node Strength :
lmax = 𝑁𝐶 s All 3 network statistics cannot
grow and the network remain stable. Eg a highly
asymmetric network with high s such as core periphery,
its connectivity has to be very low for it to be stable
17. From Epidemiology : Failure of i at q+1 determined
by the criteria that losses exceed a predetermined
buffer ratio, r, of Tier 1 capital
𝑢 iq+1 = (1 - r) uiq +
(𝑥 𝑗𝑖−𝑥 𝑖𝑗)
𝐶 𝑖0
+
𝑢𝑗𝑞
1
𝑗 (2)
(i)First term i’s own survival probability given by the
capital Ciq it has remaining at q relative to initial capital
Ci0 , r is common cure rate and (1 - r) is rate of not
surviving in the worst case scenario .
(ii)The sum of ‘infection rates’= sum of net liabilities of
its j failed counterparties relative to its own capital is
given by the term
(𝑥 𝑗𝑖
−𝑥 𝑖𝑗
)
𝐶 𝑖0
+
𝑗
18. Stability of the dynamical network
system : Eigen Pair (λmax , v)
In matrix algebra dynamics of bank failures given by:
Ut +1 = [´ + (1- r)I] Ut = Q Ut (3)
I is identity matrix and r is the % buffer
The system stability of (2) will be evaluated on the basis
of the power iteration of the matrix Q=[(1-r)I+Θ´]. From
(3), Uq takes the form:
Uq= Qq U0
Stability Condition lmax(´) < r
After q iterations
λmax is maximum
eigenvalue of Θ
19. Solvency Contagion and Stability of Matrix Θ’
: Netted impact of i on j relative to j’s capital
)2(
0...
)(
....
)(
.0........
)(
...0....
)(
.........0..
)(
........
)(
00
0.....0.
)()(
0
1
11
1
11
33
3
3223
3
3113
2
2112
jt
jNNj
t
NN
Nt
NiiN
t
ii
Nt
NN
t
tt
C
xx
C
xx
C
xx
C
xx
C
xx
C
xx
C
xx
C
xx
20. Financial network models to date have yielded mixed
results : None about propagators of 2007 crisis (C:
Core; P Periphery (see Fricke and Lux (2012))
21. Some Networks: A graphical representation of random
graph (left) and small world graph with hubs, Markose
et. al. 2004
22. Contagion when JP Morgan Demises in Clustered CDS Network 2008
Q4 ( Left 4 banks fail in first step and crisis contained) v
In Random Graph (Right 22 banks fail !! Over many steps)
Innoculate some key players v Innoculate all ( Data Q4 08)
23. Eigenvector Centrality (EVC)
Centrality: a measure of the relative importance of a node
within a network
Eigenvector centrality
Based on the idea that the centrality vi of a node should be proportional to
the sum of the centralities of the neighbors
l is maximum
eigenvalue of Θ
A variant is used in the Page Ranking algorithm used by Google
The vector v, containing centrality values of all nodes is obtained by solving the
eigenvalue equation Θ 𝒗 𝟏 = λmax 𝒗 𝟏.
λmax is a real positive number and the eigenvector 𝒗 𝟏 associated with the largest
eigenvalue has non-negative components by the Perron-Frobenius theorem (see
Meyer (2000))
Right Eigenvector Centrality : Systemic Risk Index Tax using Right EVC
Left Eigenvector centrality Leads to vulnerability Index
24. Mitigation of Systemic Risk Impact of
Network Central Banks: How to
stabilize ?
To date the problem of how to have banks internalize
their systemic risk costs to others (and tax payer) from
failure has not been adequately solved
In particular, penalty for being too interconnected has
not been dealt with from direct bilateral network data
Tax according to right eigenvector centrality
25. There are 5 ways in which stability of the
financial network can be achieved
(i)Constrain the bilateral exposure of financial
intermediaries (Ad hoc constraints do not work) Serafin
Martinez implemented these in Mexico
(ii) Ad hocly increase the threshold rho in (11),
(iii) Change the topology of the network
(iv) Directly deduct a eigenvector centrality based
prefunded buffer in matrix
Si
# = 𝜃𝑗𝑖
#
𝑗 =
1
𝐶 𝑖
( (𝑥𝑗𝑖𝑗 − 𝑥𝑖𝑗)+ − vi 𝐶𝑖) .
(i) & (ii) do not price in negative externalities and systemic
risk of failure of highly network central nodes. Network
topologies emerge endogenously and are hard to manipulate
26. How to stabilize: Superspreader tax
quantified : tax using Eigen Vector Centrality
of each bank vi or vi ^2 to reduce max
eigenvalue of matrix to 6%
28. • Too interconnected to fail addressed only if systemic risk
from individual banks can be rectified with a price or tax
reflecting the negative externalities of their connectivity
• Lessons to be learnt : Disease Transmission in scale
free networks (May and Lloyd (1998), Barthelemy et. al :
With higher probabilities that a node is connected to
highly connected nodes means disease spread follows a
hierarchical order. Knowledge of financial
interconnectivity essential for targeted interventions
• Highly connected nodes become infected first and
epidemic dying out fast and often contained in first two
tiers
• Innoculate a few rather than whole population;
Strengthen hub; Reduce variance of node strength in
maximum eigenvalue formula
Conclusion : Regulators and Systemic Risk Researchers must face up
to limits of data mining market price data for early warning signals and
instead mandate structural bilateral financial data and digitally map the
macro-financial network system
29. • Changes in eigenvector centrality of FIs can give
early warning of instability causing banks
• EVC basis of Bail in Escrow fund/Capital
Surcharge
• These banks with high Eigen vector centrality
will, like Northern Rock, be winning bank of the
year awards ; however potentially destabilizing
from macro-prudential perspective
Capital for CCPs to secure system stability can
use same calculations
• Beware gross aggregation and netting across
product classes for which there is no multi-lateral
clearing ; more systemic risk and hence more,
capital/collateral needed for stabilization
• Single network v multi-layer networks
Other Concluding Remarks
30. References:
(1) Markose, S.M (2013) "Systemic Risk Analytics: A Data
Driven Multi-Agent Financial Network (MAFN) Approach",
Journal of Banking Regulation , Vol.14, 3/4, p.285-305, Special
Issue on Regulatory Data and Systemic Risk Analytics.
(2) Markose, S. (2012) “Systemic Risk From Global Financial
Derivatives: A Network Analysis of Contagion and Its Mitigation With
Super-Spreader Tax”, November, IMF Working Paper No. 12/282,
(3) Markose, S., S. Giansante, and A. Shaghaghi, (2012), “Too
Interconnected To Fail Financial Network of U.S. CDS Market:
Topological Fragility and Systemic Risk”, Journal of Economic
Behavior and Organization, Volume 83, Issue 3, August 2012, P627-
646
http://www.sciencedirect.com/science/article/pii/S0167268112001254
(3)Multi-Agent Financial Network (MAFN) Model of US Collateralized
Debt Obligations (CDO): Regulatory Capital Arbitrage, Negative CDS
Carry Trade and Systemic Risk Analysis, Sheri M. Markose, Bewaji
Oluwasegun and Simone Giansante, Chapter in Simulation in
Computational Finance and Economics: Tools and Emerging
Applications Editor(s): Alexandrova-Kabadjova B., S. Martinez-
Jaramillo, A. L. Garcia-Almanza, E. Tsang, IGI Global, August 2012.
http://www.acefinmod.com/CDS1.html