Table Of ContentStudies in Systems, Decision and Control 219
Anton Gerunov
Risk Analysis
for the Digital
Age
Studies in Systems, Decision and Control
Volume 219
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Anton Gerunov
Risk Analysis for the Digital
Age
Anton Gerunov
Faculty of Economics and Business
Administration
Sofia University “St. Kliment Ohridski”
Sofia, Bulgaria
ISSN 2198-4182 ISSN 2198-4190 (electronic)
Studies in Systems, Decision and Control
ISBN 978-3-031-18099-6 ISBN 978-3-031-18100-9 (eBook)
https://doi.org/10.1007/978-3-031-18100-9
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Introduction
The new digital economy is the reality we all live in. The last decades have seen the
exponential growth of advanced technologies that have led to a significant rise in the
automation and digitalization of all business processes. So goes the trivial observa-
tion any pundit can quickly make. This is, however, merely the tip of the iceberg.
The enabling factors of this transformation, the driving undercurrents, are those
fundamental developments that change not merely the surface but the underlying
landscape of economic and social activity. We are observing a new type of indus-
trialization which is driven by information assets such as datasets, advanced digital
infrastructures, extensive codebases, and trained Artificial Intelligence models. From
an economic standpoint, this leads to a decreased marginal cost of production,
decreasing share of labor in the final product, increased personalization of prod-
ucts and services, and the concomitant increase in the complexity and uncertainty of
business activity.
What is more, qualitatively new phenomena are now taking center stage in the
global economy such as social networking, smart devices, and an explosion of data
that power scaringly accurate machine learning models for classification and predic-
tion. These novel and markedly digital phenomena are hardly limited by phys-
ical boundaries and cannot be expected to behave the way known ones do. The
digital world is an exponential winner-takes-all type of environment—something
that can be observed in the way engagement, impact, and revenues behave. It is
also a detailed world—sophisticated technology enables microtransactions, micro-
payments, and granular behavior monitoring and influence. Finally, it is a rela-
tively democratic world—barring pertinent limitations of skill—it takes very little
to become a full-fledged participant, producer, or even a threat.
All this creates innumerable opportunities, but also dramatically expands the risks
individuals, organizations, and nations face. The risk universe is not only vastly larger
but more complex to comprehend and analyze with traditional tools. This book aims
to bridge this gap by providing initial ideas on how to approach novel risks and
carry over traditional tools and approaches to the new digital reality. The discussions
presented here are merely starting points in a long journey and it is expected that they
will bring questions and controversy, but also attract healthy criticism. The end goal
v
vi Introduction
is clear—start the discussion that culminates in a rigorous and reliable methodology
for understanding and managing risk in a world that increasingly blurs the boundaries
between real and virtual.
Chapter 1 of the book starts the journey. It begins by outlining the fundamentals of
risk and setting the stage with appropriate definitions. The risk curve is presented and
also an initial pass at modeling risk by leveraging qualitative evaluations and prioriti-
zation within the framework of a risk matrix. Those intuitions are then formalized by
taking recourse to classical utility functions and formally deriving risk preferences
as defined in the Arrow-Pratt measure. This measure gives an idea of how to measure
risk with the risk premium—the reward one received from bearing uncertainty. The
chapter then looks at empirical data which is used to estimate the risk premia in 26
European countries over a period of 15 years, ending in 2020. This simple metric
may not be enough to capture a complex process that changes its dynamics over time.
This is why we suggest to move away from simple estimates and instead measure the
risk premia for specific types of periods (such as risk during boom and risk during
recession). The proposed way to do this in practice is by using a Markov switching
model that estimates the optimum number of states and their relevant parameters.
Regime-variant risk premia for those 26 European stock markets are calculated and
discussed.
Chapter 2 introduces the reader to more standard risk metrics. It takes the usual
route of treating the risky variable as a random one that can be described by its
statistical distribution and outlines a few popular distributions that are in use in risk
analysis—the Normal and Cauchy ones, the Student T, the Exponential, and the
Lognormal one. Investigating those, one is compelled to reach the conclusion that
pertinent risks lie in the tail of the distribution and may be modeled by calculating the
Value at Risk (VaR) and the Expected Tail Loss (ETL). From a practical standpoint,
there are many ways to do this, taking into account different periods and relying
on alternative assumptions. The chapter presents actual data on 25 European stock
markets for a period of 16 years and calculates their respective VaRs and ETLs under
different assumptions. We show that the quantitative difference in estimates may
be very large under different assumptions, reaching 42% for VaR and over 100%
for ETL. We further show that judging by the forecast error, the best-performing
estimation approach is to use non-parametric estimates. At any rate, regardless of
assumptions, both VaR and ETL fail to account for the catastrophic rise in risk in
2020 engendered by a global health pandemic.
Chapter 3 switches its focus to digital assets. It defines information assets and
digital financial assets and presents an overview of the major types in both classes.
Risk estimation for information assets may have to include a formal valuation, and
some approaches to do this are proposed. On the other hand, digital financial assets
may be traded in a market, but their behavior varies significantly from those of
traditional financial assets, particularly in the early stages of their introduction. The
chapter presents a detailed study of a major new emerging digital asset class—the
cryptocurrencies—by reviewing a set of 20 coins with large market capitalization.
Traditional risk metrics are calculated but they are shown to be missing a part of the
picture. We thus propose enhancing the classical mean-variance optimization with a
Introduction vii
new major risk typical of cryptos—liquidity risk. Essentially, this means including a
measure of trade volume in the risk optimization decision, thus moving it away from
the familiar two-dimensional optimization space and into three-dimensional one. An
example of this new concept is presented, underlining that it serves as an extension
to traditional theory.
Chapter 4 takes recourse to a markedly digital theme—the rise of networking. The
emergence of the network society and network economy, powered by information
technology, has also transformed risk. Uncertainty is now inherent in the network
structure of interactions and risk management may benefit from taking a more detailed
look at it. This chapter briefly presents the fundamentals of network theory and some
relevant applications in Social Network Analysis. Key network parameters such as
centrality and clustering are presented, and crucial network processes such as diffu-
sion are conceptualized. To motivate the discussion, we present both a theoretical
model of cascading network failures that results in a financial meltdown, as well as
a Ponzi-scheme network. Understanding risk as a network phenomenon also neces-
sitates the uptake of new tools. We propose the Bayesian Belief Network as a useful
tool for modeling networks where connections between nodes are unknown. The
chapter uses statistical data to recreate the network structure of non-diversifiable
macroeconomic risk that affects the digital sectors of the European economy. Risk
betas are calculated and interpreted.
Chapter 5 takes on the Holy Grail of risk management—analyzing and
predicting rare and potentially catastrophic risks (so-called “black swans”). The main
approaches to do that are presented, covering expert judgment, statistical methods,
prediction markets, and simulations. The chapter reviews the potential benefits but
also the challenges in using all of them. The key challenge invariantly remains that
the analyst has either very limited or practically no data on those rare risks. We
propose to surmount this by leveraging a rigorous and well-thought Monte Carlo
simulation with extensive sensitivity analysis. Such a simulation is created to model
very rare risks that affect the sector of information processing services in Europe
as a proxy of the overall digital economy. The Monte Carlo simulation is run over
10,000 iterations under four alternative scenarios, and the results are commented on
in depth. The main conclusion is that digital phenomena seem to be better modeled
by non-normal distributions that provide for longer tails such as the exponential and
the lognormal ones.
Chapter 6 investigates how individuals perceive risk and how their behavior
changes in a digital environment. It begins with a plain vanilla presentation of main
theories on individual risk perceptions starting off with classical utility theory, going
through some of its many modifications (and their respective utility functions and risk
measures), and into the realm of behavioral economics. The chapter then presents
recent advances in neuroeconomics that show how the structure, size, and composi-
tion of the brain affect risk perceptions. Crucially, it proceeds with how the online
environment mediates human perception, engendering whole new sets of heuristics
and biases online users fall prey to: reputation, endorsement, consistency, expectancy
violation, and pervasive intent heuristics. An original economic experiment is then
viii Introduction
Fig. A Overall Structure of the Book and Chapter Connections.
presented that investigates how subjects perceive risk and make decisions in a simu-
lated online social network as opposed to a control group with no network access.
The resulting decisions tend to be less optimal in terms of outcomes whenever a
proto-network is present. Furthermore, we investigate the ability to forecast indi-
vidual decisions using 55 leading machine learning algorithms and find out that the
presence of a social network decreases predictability.
These chapters can be read either sequentially or ordered at the reader’s pleasure.
Still, in some cases preceding chapters give more background and context to the
following ones. This is illustrated in the following Figure A, whereby arrows indicate
useful background from one chapter to the next. Such routes through the book may
also be useful for undergraduate or graduate courses where the instructor may wish
to use only parts of the book.
In addition to the problems and cases presented in this book, the avid reader will
surely think of many more that are relevant to analyzing risk in a digital environment.
We hope that these can also be tackled with the methods and extensions proposed here.
The chapters of the book have likewise relied on the extensive and sometimes multi-
disciplinary research conducted in the fields of risk management, decision science,
behavioral economics, finance, digital strategy, and network science. This work also
has a number of original contributions that may be of interest to academics and
practitioners:
•
Calculates long-term risk premia for 26 European markets over a period of one
and a half decades.
•
Presents regime-variant risk premia for 26 European markets, and proposed
using a Markov switching model for estimating regime-variant risk for digital
phenomena.
•
Compares and contrasts VaR and ETL estimates under different assumptions,
showing that non-parametric calculations exhibit the best performance.
•
Proposes a novel approach for analyzing cryptocurrency risks that includes
standardized volume traded as a proxy for liquidity risk.
•
Demonstrates the application of the new cryptocurrency risk analysis approach
by moving from the traditional 2D mean-variance optimization space towards a
3D space that includes standardized trade volume.
Introduction ix
•
Creates a rigorous network-driven risk model for the digital sectors of the Euro-
pean economy—Codes J62 (Computer programming, consultancy, and related
activities) and J63 (Information service activities).
•
Estimates risk betas as contributions to conditional density in the Bayesian
networks for the digital sectors in the European economy.
•
Estimates and compares four Monte Carlo models for studying information
service activities revenue, thus underlining the necessity of using non-normal
distributions when modeling risk in the digital economy.
•
Conducted (with co-authors) and analyzes an economic experiment showing
the significant effects of social networking on individual risk perceptions and
decision-making.
•
Tests 55 alternative machine learning algorithms for predicting individual choice
and outlines the best-performing ones.
•
Using the results from the decision prediction exercise, we show that exposure to
the social network makes the decision more erratic and less prone to prediction.
The journey towards understanding digital risk is a long but exciting one, and its
importance will only grow as the speed of digital transformation accelerates. This
book is probably a little step in this march, but the author hopes that reading it will
be as enjoyable as writing it.
