Datashop Alchemy

WHITEPAPER

Next Generation FinancialRisk MonitoringAssessing the systemic risk of amulti-trillion dollar financial economy

“ Yes, there is the occasional corporate fraudor misaligned incentives, but those are aberrations rather than a systemic problem. So I think this view, that you couldn’t have alarge systemic problem, this was the problem.”

Raghuram Rajan | Former Chief Economist and Director of Research at IMF who

predicted the 2008 U.S. recession in 2005. He is a Distinguished Professor at

Chicago Booth, now Governor of the Reserve Bank of India, and author of the

Financial Time Book of the Year Fault Lines: How hidden fractures still threaten

the world economy.

Datashop | Next Generation Financial Risk Monitoring

Acknowledgements

Innovaccer would like to acknowledge and thank Dr. Sanjiv Das for collaborating and supporting

analytical modules of Datashop Alchemy. Dr. Das, a William and Janice Terry Professor of

Finance at Santa Clara University’s Leavey School of Business and an expert in risk and

networks, presented a new measure of systemic risk that accounts for both the interconnectedness

of financial institutions and their relative levels of financial solvency. This work is at the heart

of Datashop Alchemy framework to evaluate systemic risk and fragility scores of the

interconnected financial networks.

Innovaccer also extends thanks and appreciation to CAFRAL (Research Wing of RBI) and

especially to Dr. NR Prabhala, Chief Mentor and Head of Research at CAFRAL and Associate

Professor at the Robert H. Smith School of Business at University of Maryland, for their

continuous support and feedback during the development of Datashop Alchemy.

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Introduction

The 2008 global financial crisis raised profound questions regarding the stability of the

global financial system. It proved that in an interconnected world, what was initially a liquidity

crisis can very quickly turn into a solvency crisis for financial institutions, a balance of payment

crisis for countries, and a full-blown confidence crisis for the entire world.

Finance and economic researchers emerged from these crises to address global stability

using a unified approach of interconnectedness to capture what is denoted as systemic risk. There

is no widely accepted, comprehensive definition of systemic risk. In a general sense, it refers to

the risk of failure of an entire system, be it financial, manufacturing, retail, or otherwise.

This white paper explores measures of interconnectedness in a financial system and

discusses approaches to regulating its movements. We also showcase a novel framework for

network-based systemic risk measurement and management. Innovaccer’s Datashop Technology

through its Big Data framework of unified data management, analysis, and dashboarding modules

enables organizations to deploy systemic risk frameworks within weeks and assess the

interconnected risk on a daily basis.

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What is Systemic Risk?

There is no widely accepted, comprehensive definition of systemic risk. In a general sense,

it refers to the risk of failure of an entire system, be it financial, manufacturing, retail, or

otherwise. A G-10 report on Financial Sector Consolidation (2001) defines Systemic Risk as

follows:

“Systemic financial risk is the risk that an event will trigger a loss of

economic value or confidence in, and attendant increases in uncertainty

about, a substantial portion of the financial system that is serious enough to

quite probably have significant adverse effects on the real economy.

Systemic risk events can be sudden and unexpected, or the likelihood of

their occurrence can build up through time in the absence of appropriate

policy responses. The adverse real economic effects from systemic problems

are generally seen as arising from disruptions to the payment system, to

credit flows, and from the destruction of asset values.”

Systemic risk represents the potential for interconnected entities to collapse, affecting every

market, trade, or economy they touch, thereby becoming a global crisis in any industry. The G-

20 Brisbane summit report (2014) (from KPMG) also shows systemic risk as one of the four core

areas of financial regulatory reform and summit priorities.

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Why are actors in an economy interconnected?

Firms in an economy are connected because of common economic factors. These factors

may be (a) economy-wide, (b) industry-wide, and (c) within industry, small group specific. A

factor analysis of firm returns will reveal how many common factors exist that support the

interconnectedness of firms. In credit risk modeling, the presence of common factors has been

empirically established by Longstaff and Rajan (2008).1 The existence of such factors has been

well-established for publicly traded firms and is in fact used widely in the asset management

industry, as seen in the Fama-French four-factor model.2

In financial systems, entities are perforce connected as speculation and

risk-sharing requires trading and this connects agents to each other.

As the number of products and risks grows, the intricacy of connections grows as well.

As risks grow, more trading is needed, and network connections grow rapidly. The network

grows as an antidote to itself! More sharing of risks naturally creates more systemic risk.

The historical evolution of social systems (which includes financial networks) has been

characterized by increasing connectedness, arising from the Law of Preferential Attachment,

originally developed in Barabasi and Albert (1999).3 In this theory, influential nodes in

a network grow in influence, as new nodes that enter a network attach to these nodes,

leading to a concentration of influence. In the context of financial networks, large trading

intermediaries become increasingly central and important in the network, fostering rapid

transmission of economic contagion when a crisis occurs.

1 Longstaff, Francis A. and Arvind Rajan. “An Empirical Analysis of the Pricing of Collateralized Debt Obligations.”Journal of Finance 63, 2 (April 2008): 529-63.2 See: https://en.wikipedia.org/wiki/Carhart_four-factor_model. The data for this model is widely available at:http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.3 Barabási, A.-L.; R. Albert (1999). “Emergence of scaling in random networks”. Science 286 (5439): 509–512.

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http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/










How is Systemic Risk Evaluated?

Post-2008 research on financial stability is centered around the evaluation of

interconnectedness of actors in an economy. Acemoglu et al. (2013) argued that more

interconnected networks do not mean a more stable economy, in fact they show a negative

impact on financial stability in certain circumstances.4

Hansen (2012) presents various approaches to measuring systemic risk such as

tail measures, contingent claims analysis, network models, and dynamic, stochastic macroeconomic

models.5 In a recent development by Billio et al. (2012), econometric measures of connectedness

based on component analysis and Granger causality networks promises to capture the intricate web

of pairwise statistical relations among individual firms in the finance and insurance industries.6

Das (2014)7 developed a new measure of systemic risk that accounts for

both, the interconnectedness of financial institutions and also their relative

levels of financial solvency.

Interconnectedness in the Das model may be developed in a myriad of ways, from using

interbank lending networks8 to using Granger causality methods developed in Billio et al.

(2012). The financial health of each bank may be derived from a credit scoring model and

is signified with a credit rating level, where higher values denote worse credit. Using these

inputs (the network matrix and credit vector), the metric produces a single composite

number for systemic risk that can be tracked over time. In the Das (2014) measure, an

increase in connectedness or a worsening in credit ratings results in an increase in systemic

risk.

4 Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. “Systemic risk and stability in financialnetworks.” American Economic Review 105(2), (2015): 564-608.5 Hansen, Lars Peter. Challenges in identifying and measuring systemic risk. No. w18505. National Bureau ofEconomic Research, 2012.6 Billio, Monica, et al. “Econometric measures of connectedness and systemic risk in the finance and insurancesectors.” Journal of Financial Economics 104(3), (2012): 535-559.7 Das, Sanjiv (2014). “Matrix Math: Network-Based Measures of Systemic Risk”, Working paper, Santa ClaraUiversity.8 See Douglas Burdick, Sanjiv R. Das, Mauricio A. Hernandez, Howard Ho, Georgia Koutrika, Rajasekar Krishnamurthy, Lucian Popa, Ioana Stanoi, Shivakumar Vaithyanathan, (2012). “Extracting, Linking and Integrating Data from Public Sources: A Financial Case Study,” IEEE Data Engineering Bulletin, 3(3),60-67.

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The Das (2014) metric also offers some interesting analyses as a byproduct. First, the single

value for the financial system can be broken down into values for each bank’s contribution

to total risk, so as to determine the relative share of each bank in system-wide risk. Second,

we may also compute the increase in system-wide risk if any one bank drops in credit

quality. Third, we are able to undertake what-if scenario analyses to assess the impact of

increasing connectedness in the system. Fourth, we can determine best ways in which to

reduce systemic risk by better risk management at banks or through a reconfiguration of the

network. Several other analyses are also possible, supported by high-quality visualizations

of the financial system.

Evaluating systemic risk builds a hedge of protection around a financial system by providing

a vantage point to see the big picture of interconnectedness, individual banks from many

angles, and outcome predictions of specific scenarios.

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Why should your businessmonitor systemic risk?

Systemic risk is not limited to the financial sector. Parallels can be drawn to any

industry where there is a high degree of interconnectedness and need to monitor

interactions between players in the system. This can help organizations understand the

interconnectedness of themselves with other competitors and between competitors.

Central Banks

A central bank’s mandate is to regulate the financial economy of itsrespective country. Acharya

et al. (2010) suggests that systemic risk can be regulated by measuring each financial

institution’s individual contribution.9 They conclude in their paper that financial regulation be

focused on limiting systemic risk, that is, the risk of a crisis in the financial sector and its

spillover into the economy at large.

Measuring and monitoring systemic risk, interconnectedness of financial institutions and

individual institution’s contributions to the risk is useful to regulate the financial economy in

order to prevent a systemic and irreversible shock to the world’s economy.

Financial Institutions

For financial institutions, the reverse is true, i.e., an awareness of how systemic risk impacts

their firms is useful. By correlating the time series of systemic risk with individual firm’s

performance indicators, a single financial institution will be able to better ensure that it is

not overexposed to systemic risk.

Industries

In retail, the viability of your business depends not only on your customers and your competitors,

but on every entity that has a hand in getting your product from an idea in your mind to your

customers hands, from vendors to manufacturers to shipping companies. Interconnected players in

any industry pose complicated and shifting systemic risk. Being able to accurately determine your

risk profile versus those of competitors is essential for risk assessment and risk mitigation planning.

9 http://pages.stern.nyu.edu/~sternfin/vacharya/public_html/MeasuringSystemicRisk_final.pdf

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http://pages.stern.nyu.edu/~sternfin/vacharya/public_html/MeasuringSystemicRisk_final.pdf









Datashop Alchemy

Datashop Alchemy is a decision science framework that enables companies to understand

and manage their systemic risk profile. This framework automatically pulls data from multiple

data streams, computes multiple metrics including systemic risk scores, fragility scores, and

individual risk scores on a daily basis and visualizes all of this via an interactive dashboard.

Interconnectedness of Banks

Illustrative network graph of banks representing

which banks are interconnected based on their

equity and credit ratings’ Granger causality. Bubble

size represents the number of inter-connections and

a bank more central in the graph is more connected

to other banks.

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Systemic Risk and Fragility Scores

Time-Series graph of daily systemic risk and

fragility scores of a financial economy of a

country. Volatility in time-series data of

systemic risk or fragility scores gives rich

information on instability in the market.

The benefits of this framework are:

Academically proven systemic risk algorithms

The Das (2014) metric is derived from 15 years of work on systemic risk. The academic

community validated the metric, which is at the heart of Datashop Alchemy to compute

systemic risk scores. This measure is robust enough to even evaluate an increase in

systemic risk if one bank drops in credit quality.Scalability

The framework is scalable to store and manage large data-sets to easily accommodate 50+ years of

data and more than 5,000 actors. Using a NoSQL database along with a scalable architecture of R

+ Spark, systemic risk score evaluations can be fired up on multiple virtual machines

simultaneously to provide near real-time scores and metrics.

Flexibility

The modular architecture of Datashop Alchemy allows for adding functionality or data

visualizations on the web dashboard or streaming other sources of data with ease, without

touching any other modules irrelevant to the additions. Access control management

and role-based access to different views provides more power to the administrator in

disseminating the web dashboard across people in the organization.

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Easy to Setup

Data streams plug in to this framework automatically using data connectors. To configure

Datashop Alchemy, one must simply choose the data-streams activate and filter parameters of

obtaining the streams including geographic region, time frame, industry, frequency schedule of

data streaming, and frequency schedule of risk evaluations. Administrators at the organization can

then set up accounts and role-based access levels for users to start accessing the web dashboard.

Protection

Interactive

dashboards

provide key

insights and risk

scores to drive

vital and time-

sensitive

decisions that

will ultimately

protect the

entire system.

Datashop

Alchemy builds

a vantage point

for the

administrator to

understand the

big picture of

the

organization.

Architecture Diagram ofDatashop Alchemy

Datashop Alchemy is capable of streaming in any

data from stock markets, credit ratings, and inter

institution lending data using its data connectors

and execute systemic risk models on a scalable R

+ Spark architecture for providing near real-time

systemic risk measures on a web dashboard.12


Case Studies

The Datashop Alchemy framework has supported worldwide organizations, helping to

secure trillions of dollars in our interconnected, global economy. In the business of risk

management, no news is good news. To prevent the next financial crisis or economic

collapse, Datashop Alchemy quietly plugs away, providing financial leaders with vital

information that drives key decisions to strengthen and grow their organizations, keeping

risks at bay and squelching threats.

Central Bank

Innovaccer deployed the Datashop Alchemy framework at a central bank of a multi-trillion

dollar national economy to examine systemic risk scores of more than a 1,000financial

institutions using stock market and credit rating data.

The central bank uses this score on a daily basis and has preventative measures in place

determined by fluctuations calculated by Datashop Alchemy.

Oil and Gas

In the Oil and Gas industry, market players’ equity market and credit ratings are not the only

variables affecting systemic risk but also currency and energy prices. These two additional data

streams were added to the framework with a slight customization to the algorithm to incorporate

two new indices.

An Oil and Gas major player was able to examine external market risks all at

once, including competitor’s equities and credit ratings, currency, and energy price

fluctuations.

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About Innovaccer

At Innovaccer, we create products that transform the way organizations use data. Our

products and services are deployed at critical government, commercial, and non-profit

institutions around the world to solve sophisticated and world-changing problems. Simply put,

we accelerate innovation through the power of data science.

© Innovaccer Inc 2015

Innovaccer, Innovaccer Inc, and Innovaccer Datashop are trademarks of Innovaccer Inc. All other

company and product names may be trademarks with which they are associated with. Datashop

Alchemy is a proprietary technology and Intellectual Property of Innovaccer.

For feedback, requests, or other questions, feel free to reach out:

[email protected]

Innovaccer, Inc.

Stanford Financial Square,

2600 El Camino Real, Suite 415

Palo Alto, CA 94306

United States

+1 714 729 4038

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