This book presents a foray into the fascinating process of risk management, beginning from classical methods and approaches to understanding risk all the way into cutting-age thinking. Risk management by necessity must lie at the heart of governing our ever more complex digital societies. New phenomena and activities necessitate a new look at how individuals, firms, and states manage the uncertainty they must operate in. Initial chapters provide an introduction to traditional methods and show how they can be built upon to better understand the workings of the modern economy. Later chapters review digital activities and assets like cryptocurrencies showing how such emergent risks can be conceptualized better. Network theory figures prominently and the book demonstrates how it can be used to gauge the risk in the digital sectors of the economy. Predicting the unpredictable black swan events is also discussed in view of a wider adoption of economic simulations. The journey concludes by looking at how individuals perceive risk and make decisions as they operate in a virtual social network. This book interests the academic audience, but it also features insights and novel research results that are relevant for practitioners and policymakers.
Author(s): Anton Gerunov
Series: Studies in Systems, Decision and Control, 219
Publisher: Springer
Year: 2022
Language: English
Pages: 251
City: Cham
Introduction
Contents
1 Understanding Risk
1.1 Introduction
1.2 Definitions and Typologies
1.2.1 Risk: A Short History
1.2.2 Defining Risk
1.2.3 Types of Risks
1.2.4 The Risk Curve
1.3 Qualitative Evaluation of Risk
1.4 A Mathematical Formulation
1.4.1 The Utility Function
1.4.2 Attitudes to Risk
1.4.3 Incorporating Risk Preferences
1.5 Risk Aversion
1.5.1 The Arrow–Pratt Measure of Risk Aversion
1.5.2 Risk Aversion Under Alternative Utility Specifications
1.6 Measuring the Risk Premium
1.6.1 Average Risk Premia in Europe
1.6.2 Variance Across Countries and Industries
1.6.3 Different Risk Premium Regimes
1.7 Conclusion
References
2 Standard Risk Metrics
2.1 Introduction
2.2 Risk as a Random Variable
2.2.1 Normal Distribution
2.2.2 Family of Fat Tails Distributions
2.2.3 Log-Normal Distribution
2.2.4 Power Law Distributions
2.3 Simple Risk Metrics
2.3.1 Measures of Variance
2.3.2 Market Beta
2.3.3 Risk and Return Tradeoffs
2.4 Advanced Risk Metrics
2.4.1 Value at Risk
2.4.2 Expected Shortfall
2.4.3 Risk as Anomaly
2.5 Empirical Stability of Metrics
2.5.1 Simple Risk Metrics
2.5.2 Value at Risk
2.5.3 Expected Shortfall
2.6 Conclusion
References
3 Risk in Digital Assets
3.1 Introduction
3.2 Information Assets
3.2.1 Types of Information Assets
3.2.2 Sources and Types of Risks
3.2.3 Estimating Risk Probability and Impact
3.3 Valuation of Information Assets
3.3.1 Market Metrics
3.3.2 Expert Estimates
3.4 Digital Financial Assets
3.4.1 Types of Digital Financial Assets
3.4.2 Types of Risks in Digital Financial Assets
3.4.3 Estimating Risk Probability and Impact
3.5 Risk Modeling for Emerging Assets
3.5.1 Stylized Facts and Traditional Risk Metrics
3.5.2 Statistical Properties of the Series
3.5.3 Risk Management Methodology
3.6 Conclusion
References
4 Networks of Risk
4.1 Introduction
4.2 Networks and Risk
4.2.1 System Structure and Risk
4.2.2 Network Diffusion
4.2.3 Social Networks
4.3 Network Topologies
4.3.1 Random Erdös-Renyi Networks
4.3.2 Growth and Preferential Attachment in Networks
4.3.3 Small World Networks
4.3.4 Network Metrics
4.3.5 Network Robustness and Resilience
4.4 Network Construction
4.4.1 Network Data and Visualization
4.4.2 Bayesian Belief Networks
4.5 Network Sources of Risk
4.5.1 Overall Dynamics of the Digital Sectors
4.5.2 Network Sources of Risk for the Digital Sectors
4.6 Conclusion
References
5 Analyzing Rare Risks
5.1 Introduction
5.2 Expert Judgment
5.2.1 Individual Judgment
5.2.2 Judgment Decomposition
5.2.3 Structured Analogies
5.2.4 Group Judgment Methods
5.2.5 Uncertainty and Calibration
5.3 Statistical Methods
5.3.1 Pure Statistical Methods
5.3.2 Judgment-Adjusted Forecast
5.4 Prediction Markets
5.5 Monte Carlo Methods and Scenarios
5.6 Modeling the Unexpected in the Digital Economy
5.6.1 Overall Model Structure
5.6.2 Key Results
5.7 Conclusion
References
6 Humans in the Network
6.1 Introduction
6.2 Utility and Risk Preferences
6.2.1 Constant Absolute Risk Aversion (CARA)
6.2.2 Constant Relative Risk Aversion (CRRA)
6.2.3 Hyperbolic Absolute Risk Aversion (HARA)
6.2.4 Advanced Utility Specifications
6.2.5 The Utility of Utility Functions
6.3 Prospect Theory
6.4 Neural and Behavioral Foundations
6.4.1 Heuristics, Biases and Decision Architecture
6.4.2 Neural Circuitry of Risk
6.5 Mediating Online Environment
6.6 Information and Influence
6.6.1 Humans as Intuitive Statisticians
6.6.2 Performance Under Uncertainty
6.6.3 Influence of Online Social Networks
6.6.4 Modeling Human Decisions
6.7 Conclusion
References
Concluding Remarks
