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The assessment and quantification of risk provide an indispensable part of robust decision-making; to be effective, many professionals need a firm grasp of both the fundamental concepts and of the tools of the trade. Business Risk and Simulation Modelling in Practice is a comprehensive, in-depth, and practical guide that aims to help business risk managers, modelling analysts and general management to understand, conduct and use quantitative risk assessment and uncertainty modelling in their own situations. Key content areas include:
* Detailed descriptions of risk assessment processes, their objectives and uses, possible approaches to risk quantification, and their associated decision-benefits and organisational challenges.
* Principles and techniques in the design of risk models, including the similarities and differences with traditional financial models, and the enhancements that risk modelling can provide.
* In depth coverage of the principles and concepts in simulation methods, the statistical measurement of risk, the use and selection of probability distributions, the creation of dependency relationships, the alignment of risk modelling activities with general risk assessment processes, and a range of Excel modelling techniques.
* The implementation of simulation techniques using both Excel/VBA macros and the [...] Excel add-in. Each platform may be appropriate depending on the context, whereas the core modelling concepts and risk assessment contexts are largely the same in each case. Some additional features and key benefits of using [...] are also covered.
Business Risk and Simulation Modelling in Practice reflects the author's many years in training and consultancy in these areas. It provides clear and complete guidance, enhanced with an expert perspective. It uses approximately one hundred practical and real-life models to demonstrate all key concepts and techniques; these are accessible on the companion website.
The assessment and quantification of risk provide an indispensable part of robust decision-making; to be effective, many professionals need a firm grasp of both the fundamental concepts and of the tools of the trade. Business Risk and Simulation Modelling in Practice is a comprehensive, in-depth, and practical guide that aims to help business risk managers, modelling analysts and general management to understand, conduct and use quantitative risk assessment and uncertainty modelling in their own situations. Key content areas include:
* Detailed descriptions of risk assessment processes, their objectives and uses, possible approaches to risk quantification, and their associated decision-benefits and organisational challenges.
* Principles and techniques in the design of risk models, including the similarities and differences with traditional financial models, and the enhancements that risk modelling can provide.
* In depth coverage of the principles and concepts in simulation methods, the statistical measurement of risk, the use and selection of probability distributions, the creation of dependency relationships, the alignment of risk modelling activities with general risk assessment processes, and a range of Excel modelling techniques.
* The implementation of simulation techniques using both Excel/VBA macros and the [...] Excel add-in. Each platform may be appropriate depending on the context, whereas the core modelling concepts and risk assessment contexts are largely the same in each case. Some additional features and key benefits of using [...] are also covered.
Business Risk and Simulation Modelling in Practice reflects the author's many years in training and consultancy in these areas. It provides clear and complete guidance, enhanced with an expert perspective. It uses approximately one hundred practical and real-life models to demonstrate all key concepts and techniques; these are accessible on the companion website.
MICHAEL REES is an independent consultant and trainer for financial modelling. He works for a wide range of clients, including major corporations, private equity firms, fund managers, strategy consultants and risk management consultants.
Preface xvii
About the Author xxiii
About the Website xxv
Part I An Introduction to Risk Assessment - Its Uses, Processes, Approaches, Benefits and Challenges
Chapter 1 The Context and Uses of Risk Assessment 3
1.1 Risk Assessment Examples 3
1.1.1 Everyday Examples of Risk Management 4
1.1.2 Prominent Risk Management Failures 5
1.2 General Challenges in Decision-Making Processes 7
1.2.1 Balancing Intuition with Rationality 7
1.2.2 The Presence of Biases 9
1.3 Key Drivers of the Need for Formalised Risk Assessment in Business Contexts 14
1.3.1 Complexity 14
1.3.2 Scale 15
1.3.3 Authority and Responsibility to Identify and Execute Risk-Response Measures 16
1.3.4 Corporate Governance Guidelines 16
1.3.5 General Organisational Effectiveness and the Creation of Competitive Advantage 18
1.3.6 Quantification Requirements 18
1.3.7 Reflecting Risk Tolerances in Decisions and in Business Design 19
1.4 The Objectives and Uses of General Risk Assessment 19
1.4.1 Adapt and Improve the Design and Structure of Plans and Projects 20
1.4.2 Achieve Optimal Risk Mitigation within Revised Plans 20
1.4.3 Evaluate Projects, Set Targets and Reflect Risk Tolerances in Decision-Making 21
1.4.4 Manage Projects Effectively 21
1.4.5 Construct, Select and Optimise Business and Project Portfolios 22
1.4.6 Support the Creation of Strategic Options and Corporate Planning 25
Chapter 2 Key Stages of the General Risk Assessment Process 29
2.1 Overview of the Process Stages 29
2.2 Process Iterations 30
2.3 Risk Identification 32
2.3.1 The Importance of a Robust Risk Identification Step 32
2.3.2 Bringing Structure into the Process 32
2.3.3 Distinguishing Variability from Decision Risks 34
2.3.4 Distinguishing Business Issues from Risks 34
2.3.5 Risk Identification in Quantitative Approaches: Additional Considerations 35
2.4 Risk Mapping 35
2.4.1 Key Objectives 35
2.4.2 Challenges 35
2.5 Risk Prioritisation and Its Potential Criteria 36
2.5.1 Inclusion/Exclusion 36
2.5.2 Communications Focus 37
2.5.3 Commonality and Comparison 38
2.5.4 Modelling Reasons 39
2.5.5 General Size of Risks, Their Impact and Likelihood 39
2.5.6 Influence: Mitigation and Response Measures, and Management Actions 40
2.5.7 Optimising Resource Deployment and Implementation Constraints 41
2.6 Risk Response: Mitigation and Exploitation 42
2.6.1 Reduction 42
2.6.2 Exploitation 42
2.6.3 Transfer 42
2.6.4 Research and Information Gathering 43
2.6.5 Diversification 43
2.7 Project Management and Monitoring 44
Chapter 3 Approaches to Risk Assessment and Quantification 45
3.1 Informal or Intuitive Approaches 46
3.2 Risk Registers without Aggregation 46
3.2.1 Qualitative Approaches 46
3.2.2 Quantitative Approaches 48
3.3 Risk Register with Aggregation (Quantitative) 50
3.3.1 The Benefits of Aggregation 50
3.3.2 Aggregation of Static Values 51
3.3.3 Aggregation of Risk-Driven Occurrences and Their Impacts 52
3.3.4 Requirements and Differences to Non-Aggregation Approaches 54
3.4 Full Risk Modelling 56
3.4.1 Quantitative Aggregate Risk Registers as a First Step to Full Models 56
Chapter 4 Full Integrated Risk Modelling: Decision-Support Benefits 59
4.1 Key Characteristics of Full Models 59
4.2 Overview of the Benefits of Full Risk Modelling 61
4.3 Creating More Accurate and Realistic Models 62
4.3.1 Reality is Uncertain: Models Should Reflect This 62
4.3.2 Structured Process to Include All Relevant Factors 63
4.3.3 Unambiguous Approach to Capturing Event Risks 63
4.3.4 Inclusion of Risk Mitigation and Response Factors 66
4.3.5 Simultaneous Occurrence of Uncertainties and Risks 66
4.3.6 Assessing Outcomes in Non-Linear Situations 67
4.3.7 Reflecting Operational Flexibility and Real Options 67
4.3.8 Assessing Outcomes with Other Complex Dependencies 71
4.3.9 Capturing Correlations, Partial Dependencies and Common Causalities 73
4.4 Using the Range of Possible Outcomes to Enhance Decision-Making 74
4.4.1 Avoiding "The Trap of the Most Likely" or Structural Biases 76
4.4.2 Finding the Likelihood of Achieving a Base Case 78
4.4.3 Economic Evaluation and Reflecting Risk Tolerances 82
4.4.4 Setting Contingencies, Targets and Objectives 83
4.5 Supporting Transparent Assumptions and Reducing Biases 84
4.5.1 Using Base Cases that are Separate to Risk Distributions 85
4.5.2 General Reduction in Biases 85
4.5.3 Reinforcing Shared Accountability 85
4.6 Facilitating Group Work and Communication 86
4.6.1 A Framework for Rigorous and Precise Work 86
4.6.2 Reconcile Some Conflicting Views 86
Chapter 5 Organisational Challenges Relating to Risk Modelling 87
5.1 "We Are Doing It Already" 87
5.1.1 "Our ERM Department Deals with Those Issues" 88
5.1.2 "Everybody Should Just Do Their Job Anyway!" 88
5.1.3 "We Have Risk Registers for All Major Projects" 89
5.1.4 "We Run Sensitivities and Scenarios: Why Do More?" 89
5.2 "We Already Tried It, and It Showed Unrealistic Results" 89
5.2.1 "All Cases Were Profitable" 90
5.2.2 "The Range of Outcomes Was Too Narrow" 90
5.3 "The Models Will Not Be Useful!" 91
5.3.1 "We Should Avoid Complicated Black Boxes!" 91
5.3.2 "All Models Are Wrong, Especially Risk Models!" 91
5.3.3 "Can You Prove that It Even Works?" 92
5.3.4 "Why Bother to Plan Things that Might Not Even Happen?" 93
5.4 Working Effectively with Enhanced Processes and Procedures 93
5.4.1 Selecting the Right Projects, Approach and Decision Stage 93
5.4.2 Managing Participant Expectations 95
5.4.3 Standardisation of Processes and Models 95
5.5 Management Processes, Culture and Change Management 96
5.5.1 Integration with Decision Processes 96
5.5.2 Ensuring Alignment of Risk Assessment and Modelling Processes 97
5.5.3 Implement from the Bottom Up or the Top Down? 98
5.5.4 Encouraging Issues to Be Escalated: Don't Shoot the Messenger! 99
5.5.5 Sharing Accountability for Poor Decisions 99
5.5.6 Ensuring Alignment with Incentives and Incentive Systems 100
5.5.7 Allocation and Ownership of Contingency Budgets 101
5.5.8 Developing Risk Cultures and Other Change Management Challenges 102
Part II The Design of Risk Models - Principles, Processes and Methodology
Chapter 6 Principles of Simulation Methods 107
6.1 Core Aspects of Simulation: A Descriptive Example 107
6.1.1 The Combinatorial Effects of Multiple Inputs and Distribution of Outputs 107
6.1.2 Using Simulation to Sample Many Diverse Scenarios 110
6.2 Simulation as a Risk Modelling Tool 112
6.2.1 Distributions of Input Values and Their Role 113
6.2.2 The Effect of Dependencies between Inputs 114
6.2.3 Key Questions Addressable using Risk-Based Simulation 114
6.2.4 Random Numbers and the Required Number of Recalculations or Iterations 115
6.3 Sensitivity and Scenario Analysis: Relationship to Simulation 116
6.3.1 Sensitivity Analysis 116
6.3.2 Scenario Analysis 119
6.3.3 Simulation using DataTables 121
6.3.4 GoalSeek 121
6.4 Optimisation Analysis and Modelling: Relationship to Simulation 122
6.4.1 Uncertainty versus Choice 122
6.4.2 Optimisation in the Presence of Risk and Uncertainty 129
6.4.3 Modelling Aspects of Optimisation Situations 131
6.5 Analytic and Other Numerical Methods 133
6.5.1 Analytic Methods and Closed-Form Solutions 133
6.5.2 Combining Simulation Methods with Exact Solutions 135
6.6 The Applicability of Simulation Methods 135
Chapter 7 Core Principles of Risk Model Design 137
7.1 Model Planning and Communication 138
7.1.1 Decision-Support Role 138
7.1.2 Planning the Approach and Communicating the Output 138
7.1.3 Using Switches to Control the Cases and Scenarios 139
7.1.4 Showing the Effect of Decisions versus Those of Uncertainties 140
7.1.5 Keeping It Simple, but not Simplistic: New Insights versus Modelling Errors 144
7.2 Sensitivity-Driven Thinking as a Model Design Tool 146
7.2.1 Enhancing Sensitivity Processes for Risk Modelling 150
7.2.2 Creating Dynamic Formulae 151
7.2.3 Example: Time Shifting for Partial Periods 153
7.3 Risk Mapping and Process Alignment 154
7.3.1 The Nature of Risks and Their Impacts 155
7.3.2 Creating Alignment between Modelling and the General Risk Assessment Process 156
7.3.3 Results Interpretation within the Context of Process Stages 157
7.4 General Dependency Relationships 158
7.4.1 Example: Commonality of Drivers of Variability 159
7.4.2 Example: Scenario-Driven Variability 160
7.4.3 Example: Category-Driven Variability 162
7.4.4 Example: Fading Impacts 168
7.4.5 Example: Partial Impact Aggregation by Category in a Risk Register 170
7.4.6 Example: More Complex Impacts within a Category 171
7.5 Working with Existing Models 173
7.5.1 Ensuring an Appropriate Risk Identification and Mapping 173
7.5.2 Existing Models using Manual Processes or Embedded Procedures 174
7.5.3 Controlling a Model Switch with a Macro at the Start and End of a Simulation 175
7.5.4 Automatically Removing Data Filters at the Start of...
| Erscheinungsjahr: | 2015 |
|---|---|
| Fachbereich: | Betriebswirtschaft |
| Genre: | Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| Inhalt: | 464 S. |
| ISBN-13: | 9781118904053 |
| ISBN-10: | 1118904052 |
| Sprache: | Englisch |
| Herstellernummer: | 1W118904050 |
| Einband: | Gebunden |
| Autor: | Rees, Michael |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 251 x 172 x 32 mm |
| Von/Mit: | Michael Rees |
| Erscheinungsdatum: | 21.09.2015 |
| Gewicht: | 0,948 kg |
MICHAEL REES is an independent consultant and trainer for financial modelling. He works for a wide range of clients, including major corporations, private equity firms, fund managers, strategy consultants and risk management consultants.
Preface xvii
About the Author xxiii
About the Website xxv
Part I An Introduction to Risk Assessment - Its Uses, Processes, Approaches, Benefits and Challenges
Chapter 1 The Context and Uses of Risk Assessment 3
1.1 Risk Assessment Examples 3
1.1.1 Everyday Examples of Risk Management 4
1.1.2 Prominent Risk Management Failures 5
1.2 General Challenges in Decision-Making Processes 7
1.2.1 Balancing Intuition with Rationality 7
1.2.2 The Presence of Biases 9
1.3 Key Drivers of the Need for Formalised Risk Assessment in Business Contexts 14
1.3.1 Complexity 14
1.3.2 Scale 15
1.3.3 Authority and Responsibility to Identify and Execute Risk-Response Measures 16
1.3.4 Corporate Governance Guidelines 16
1.3.5 General Organisational Effectiveness and the Creation of Competitive Advantage 18
1.3.6 Quantification Requirements 18
1.3.7 Reflecting Risk Tolerances in Decisions and in Business Design 19
1.4 The Objectives and Uses of General Risk Assessment 19
1.4.1 Adapt and Improve the Design and Structure of Plans and Projects 20
1.4.2 Achieve Optimal Risk Mitigation within Revised Plans 20
1.4.3 Evaluate Projects, Set Targets and Reflect Risk Tolerances in Decision-Making 21
1.4.4 Manage Projects Effectively 21
1.4.5 Construct, Select and Optimise Business and Project Portfolios 22
1.4.6 Support the Creation of Strategic Options and Corporate Planning 25
Chapter 2 Key Stages of the General Risk Assessment Process 29
2.1 Overview of the Process Stages 29
2.2 Process Iterations 30
2.3 Risk Identification 32
2.3.1 The Importance of a Robust Risk Identification Step 32
2.3.2 Bringing Structure into the Process 32
2.3.3 Distinguishing Variability from Decision Risks 34
2.3.4 Distinguishing Business Issues from Risks 34
2.3.5 Risk Identification in Quantitative Approaches: Additional Considerations 35
2.4 Risk Mapping 35
2.4.1 Key Objectives 35
2.4.2 Challenges 35
2.5 Risk Prioritisation and Its Potential Criteria 36
2.5.1 Inclusion/Exclusion 36
2.5.2 Communications Focus 37
2.5.3 Commonality and Comparison 38
2.5.4 Modelling Reasons 39
2.5.5 General Size of Risks, Their Impact and Likelihood 39
2.5.6 Influence: Mitigation and Response Measures, and Management Actions 40
2.5.7 Optimising Resource Deployment and Implementation Constraints 41
2.6 Risk Response: Mitigation and Exploitation 42
2.6.1 Reduction 42
2.6.2 Exploitation 42
2.6.3 Transfer 42
2.6.4 Research and Information Gathering 43
2.6.5 Diversification 43
2.7 Project Management and Monitoring 44
Chapter 3 Approaches to Risk Assessment and Quantification 45
3.1 Informal or Intuitive Approaches 46
3.2 Risk Registers without Aggregation 46
3.2.1 Qualitative Approaches 46
3.2.2 Quantitative Approaches 48
3.3 Risk Register with Aggregation (Quantitative) 50
3.3.1 The Benefits of Aggregation 50
3.3.2 Aggregation of Static Values 51
3.3.3 Aggregation of Risk-Driven Occurrences and Their Impacts 52
3.3.4 Requirements and Differences to Non-Aggregation Approaches 54
3.4 Full Risk Modelling 56
3.4.1 Quantitative Aggregate Risk Registers as a First Step to Full Models 56
Chapter 4 Full Integrated Risk Modelling: Decision-Support Benefits 59
4.1 Key Characteristics of Full Models 59
4.2 Overview of the Benefits of Full Risk Modelling 61
4.3 Creating More Accurate and Realistic Models 62
4.3.1 Reality is Uncertain: Models Should Reflect This 62
4.3.2 Structured Process to Include All Relevant Factors 63
4.3.3 Unambiguous Approach to Capturing Event Risks 63
4.3.4 Inclusion of Risk Mitigation and Response Factors 66
4.3.5 Simultaneous Occurrence of Uncertainties and Risks 66
4.3.6 Assessing Outcomes in Non-Linear Situations 67
4.3.7 Reflecting Operational Flexibility and Real Options 67
4.3.8 Assessing Outcomes with Other Complex Dependencies 71
4.3.9 Capturing Correlations, Partial Dependencies and Common Causalities 73
4.4 Using the Range of Possible Outcomes to Enhance Decision-Making 74
4.4.1 Avoiding "The Trap of the Most Likely" or Structural Biases 76
4.4.2 Finding the Likelihood of Achieving a Base Case 78
4.4.3 Economic Evaluation and Reflecting Risk Tolerances 82
4.4.4 Setting Contingencies, Targets and Objectives 83
4.5 Supporting Transparent Assumptions and Reducing Biases 84
4.5.1 Using Base Cases that are Separate to Risk Distributions 85
4.5.2 General Reduction in Biases 85
4.5.3 Reinforcing Shared Accountability 85
4.6 Facilitating Group Work and Communication 86
4.6.1 A Framework for Rigorous and Precise Work 86
4.6.2 Reconcile Some Conflicting Views 86
Chapter 5 Organisational Challenges Relating to Risk Modelling 87
5.1 "We Are Doing It Already" 87
5.1.1 "Our ERM Department Deals with Those Issues" 88
5.1.2 "Everybody Should Just Do Their Job Anyway!" 88
5.1.3 "We Have Risk Registers for All Major Projects" 89
5.1.4 "We Run Sensitivities and Scenarios: Why Do More?" 89
5.2 "We Already Tried It, and It Showed Unrealistic Results" 89
5.2.1 "All Cases Were Profitable" 90
5.2.2 "The Range of Outcomes Was Too Narrow" 90
5.3 "The Models Will Not Be Useful!" 91
5.3.1 "We Should Avoid Complicated Black Boxes!" 91
5.3.2 "All Models Are Wrong, Especially Risk Models!" 91
5.3.3 "Can You Prove that It Even Works?" 92
5.3.4 "Why Bother to Plan Things that Might Not Even Happen?" 93
5.4 Working Effectively with Enhanced Processes and Procedures 93
5.4.1 Selecting the Right Projects, Approach and Decision Stage 93
5.4.2 Managing Participant Expectations 95
5.4.3 Standardisation of Processes and Models 95
5.5 Management Processes, Culture and Change Management 96
5.5.1 Integration with Decision Processes 96
5.5.2 Ensuring Alignment of Risk Assessment and Modelling Processes 97
5.5.3 Implement from the Bottom Up or the Top Down? 98
5.5.4 Encouraging Issues to Be Escalated: Don't Shoot the Messenger! 99
5.5.5 Sharing Accountability for Poor Decisions 99
5.5.6 Ensuring Alignment with Incentives and Incentive Systems 100
5.5.7 Allocation and Ownership of Contingency Budgets 101
5.5.8 Developing Risk Cultures and Other Change Management Challenges 102
Part II The Design of Risk Models - Principles, Processes and Methodology
Chapter 6 Principles of Simulation Methods 107
6.1 Core Aspects of Simulation: A Descriptive Example 107
6.1.1 The Combinatorial Effects of Multiple Inputs and Distribution of Outputs 107
6.1.2 Using Simulation to Sample Many Diverse Scenarios 110
6.2 Simulation as a Risk Modelling Tool 112
6.2.1 Distributions of Input Values and Their Role 113
6.2.2 The Effect of Dependencies between Inputs 114
6.2.3 Key Questions Addressable using Risk-Based Simulation 114
6.2.4 Random Numbers and the Required Number of Recalculations or Iterations 115
6.3 Sensitivity and Scenario Analysis: Relationship to Simulation 116
6.3.1 Sensitivity Analysis 116
6.3.2 Scenario Analysis 119
6.3.3 Simulation using DataTables 121
6.3.4 GoalSeek 121
6.4 Optimisation Analysis and Modelling: Relationship to Simulation 122
6.4.1 Uncertainty versus Choice 122
6.4.2 Optimisation in the Presence of Risk and Uncertainty 129
6.4.3 Modelling Aspects of Optimisation Situations 131
6.5 Analytic and Other Numerical Methods 133
6.5.1 Analytic Methods and Closed-Form Solutions 133
6.5.2 Combining Simulation Methods with Exact Solutions 135
6.6 The Applicability of Simulation Methods 135
Chapter 7 Core Principles of Risk Model Design 137
7.1 Model Planning and Communication 138
7.1.1 Decision-Support Role 138
7.1.2 Planning the Approach and Communicating the Output 138
7.1.3 Using Switches to Control the Cases and Scenarios 139
7.1.4 Showing the Effect of Decisions versus Those of Uncertainties 140
7.1.5 Keeping It Simple, but not Simplistic: New Insights versus Modelling Errors 144
7.2 Sensitivity-Driven Thinking as a Model Design Tool 146
7.2.1 Enhancing Sensitivity Processes for Risk Modelling 150
7.2.2 Creating Dynamic Formulae 151
7.2.3 Example: Time Shifting for Partial Periods 153
7.3 Risk Mapping and Process Alignment 154
7.3.1 The Nature of Risks and Their Impacts 155
7.3.2 Creating Alignment between Modelling and the General Risk Assessment Process 156
7.3.3 Results Interpretation within the Context of Process Stages 157
7.4 General Dependency Relationships 158
7.4.1 Example: Commonality of Drivers of Variability 159
7.4.2 Example: Scenario-Driven Variability 160
7.4.3 Example: Category-Driven Variability 162
7.4.4 Example: Fading Impacts 168
7.4.5 Example: Partial Impact Aggregation by Category in a Risk Register 170
7.4.6 Example: More Complex Impacts within a Category 171
7.5 Working with Existing Models 173
7.5.1 Ensuring an Appropriate Risk Identification and Mapping 173
7.5.2 Existing Models using Manual Processes or Embedded Procedures 174
7.5.3 Controlling a Model Switch with a Macro at the Start and End of a Simulation 175
7.5.4 Automatically Removing Data Filters at the Start of...
| Erscheinungsjahr: | 2015 |
|---|---|
| Fachbereich: | Betriebswirtschaft |
| Genre: | Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| Inhalt: | 464 S. |
| ISBN-13: | 9781118904053 |
| ISBN-10: | 1118904052 |
| Sprache: | Englisch |
| Herstellernummer: | 1W118904050 |
| Einband: | Gebunden |
| Autor: | Rees, Michael |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 251 x 172 x 32 mm |
| Von/Mit: | Michael Rees |
| Erscheinungsdatum: | 21.09.2015 |
| Gewicht: | 0,948 kg |
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