Pandemic Risk Management in Operations and Finance: Modeling the Impact of COVID-19 (Computational Risk Management)

"COVID-19 has spread around the world, causing tremendous structural change, and severely affecting global supply chains and financial operations. As such there is a need for analytic tools help deal with the impact of the pandemic on the worlds economies; these tools are not panaceas and certainly wont cure the problems faced, but they offer a means to aid governments, firms, and individuals in coping with specific problems. This book provides an overview of the COVID-19 pandemic and evaluates its effect on financial and supply chain operations. It then discusses epidemic modeling, presenting sources of quantitative and text data, and describing how models are used to illustrate the pandemic impact on supply chains, macroeconomic performance on financial operations. It highlights the specific experiences of the banking system, which offers predictions of the impact on the Swedish banking sector. Further, it examines models related to pandemic planning, such as evaluation of financial contagion, debt risk analysis, and health system efficiency performance, and addresses specific models of pandemic parameters. The book demonstrates various tools using available data on the ongoing COVID-19 pandemic. While it includes some citations, it focuses on describing the methods and explaining how they work, rather than on theory. The data sets and software presented were all selected on the basis of their widespread availability to any reader with computer links"--Page [4] of cover Pandemic Risk Management in Operations and Finance Preface Acknowledgements Contents About the Authors Chapter 1: Introduction 1.1 The Nature of the Problem 1.2 Alternate Approaches 1.3 Conclusion References Chapter 2: Comparison with Past Pandemics 2.1 Epidemic Impact 2.2 SEIR Model 2.3 Coronavirus Progress 2.4 Measures 2.5 Chinese Experience 2.6 Conclusions References Chapter 3: System Dynamics Modeling of Contagion Effects 3.1 Financial Contagion 3.2 System Dynamics Tools 3.3 Accounts Receivable Data and Model 3.3.1 System Dynamics Model 3.3.2 Firm Performance 3.4 Results and Discussion 3.4.1 Social Interaction 3.5 Conclusions References Chapter 4: Text Mining Support to Pandemic Planning 4.1 Text Mining of Financial Data 4.2 Investor Sentiment 4.3 Support Vector Machines 4.4 Experiment 4.4.1 Data Description 4.4.2 Sentiment Calculation 4.4.3 Prediction 4.4.4 Investment Performance 4.5 Conclusion References Chapter 5: Macroeconomic Impact 5.1 Policy Options 5.2 Global Response 5.3 Summary References Chapter 6: Supply Chain Impact 6.1 Pandemic Disruption 6.2 Network Analysis 6.2.1 SARS 6.2.2 MERS 6.2.3 Ebola 6.2.4 COVID-19 6.3 Conclusion References Chapter 7: Debt Risk Analysis Using Two-Tier Networks 7.1 Two-Tier Counterparty Risk-Contagion Network Model 7.1.1 Definition of Two-Tier Counterparty Risk-Contagion Networks 7.1.2 Division of Two-Tier Risk-Contagion Networks 7.1.3 Measurement of an Organization ́s External Risk Exposure (ERE) 7.2 Risk-Contagion Channel: Node Degree 7.2.1 Distribution of Risk-Contagion Channel: Degree Distribution 7.2.2 Risk-Contagion Path: Average Shortest Path 7.2.3 Aggregation of Risk-Contagion Network: Clustering Coefficient 7.3 Regression Model 7.4 Empirical Study on Nonfinancial Corporate Debt Risk Contagion 7.4.1 Sample Data 7.4.2 Construction of the First Layer Network 7.4.3 Construction of the Second Layer Network 7.4.4 Identify Key Risk-Contagion Nodes 7.4.5 Ownership and External Guarantee Scale 7.4.6 Determinants of External Risk Exposure 7.5 Inferences 7.6 Conclusions References Chapter 8: The Effect of COVID-19 on the Banking Sector 8.1 Short-Term Impacts on Bank Performance Indicators 8.2 Long-Term Impact on the Banking Industry Is Limited 8.3 Pandemic Possibly Increases Systemic Risks in the Banking Industry 8.4 Discussions and Suggestions 8.5 Conclusions References Chapter 9: Assessment of Smart Healthcare Services 9.1 Technology Acceptance Model 9.1.1 Attitude 9.1.2 Perceived Usefulness 9.1.3 Perceived Ease of Use 9.1.4 Subjective Norm 9.1.5 Perceived Risk 9.2 Technology Transfer 9.3 Department Difference 9.4 Research Methodology 9.4.1 Measurements 9.4.2 Data Collection 9.4.3 Data Analysis and Results 9.5 Measurement Model 9.5.1 Reflective Measurement Evaluation 9.5.2 Formative Measurement Evaluation 9.5.3 Measurement Invariance Assessment 9.5.4 Hypotheses Testing and Multigroup Analysis 9.6 Discussion and Implications 9.6.1 Theoretical Implications 9.7 Conclusion References Chapter 10: Healthcare Efficiency Modeling 10.1 Nominal and Robust DEA Models 10.1.1 Basic CCR Model 10.1.2 Robust DEA Model 10.2 Efficiency Analysis for US Hospitals 10.2.1 Data 10.3 Results and Analysis 10.4 Conclusion References Chapter 11: Recapitulation 11.1 Problem Background 11.2 Conclusion Reference