Quantitative Methods for ESG Finance (eBook)

CYRIL SHMATOV, PHD is Head of Enterprise Stress Testing (Managing Director) at Citigroup and Adjunct Associate Professor of Industrial Engineering and Operations Research at Columbia University. An area of particular interest for Cyril is ESG (Environmental, Social and Corporate Governance) Finance, including the management of financial risks associated with Climate Change and the quantitative analysis techniques that make such management possible. Cyril's recent research focuses on leveraging alternative data for Climate Risk quantification and management, including its practical aspects from a financial institution's perspective. Cyril holds a PhD in Applied Mathematics from Columbia University. CINO ROBIN CASTELLI is Director at Citi, Business Unit Manager for Enterprise Risk Management, the area that covers, amongst other topics, Climate Risk. Prior to this position, Robin was the Chief Strategy Officer for Quantitative Risk and Stress Testing, the division of Risk tasked with developing all the quantitative models used for Market Risk, Counterparty Risk, Credit and Obligor Risk Analytics, Risk Capital Analytics, and Stress Testing. Robin is also co-founder and former Executive VP for Business Development at MacroUSA, in the field of Unmanned Ground Vehicles, and prior to that, co-founder, CEO and president of Macroswiss SA. Robin holds a Bachelor's Degree and a Master's Degree in Molecular Biology, summa cum laude, from Università degli Studi di Milano-Bicocca, with an evolutionary biology thesis on "Chromosomal rearrangements as speciation mechanisms."

Foreword vii

Introduction and Book Overview 1

1. Overview 1

2. Why ESG Finance? 2

3. Why Quantitative Methods? 2

4. Target Audience and Timing of This Book 2

5. Book Outline 3

1 Introduction to ESG Finance 5

1.1 Preface: ESG Is Not a Niche Strategy Anymore 5

1.2 Introduction and Definitions 7

1.3 ESG Investment Performance 21

1.4 Sustainability and Sustainable Finance 25

2 Factor Investing and Smart Beta 39

2.1 Index Construction Basics 39

2.2 Smart Beta Indexes 40

2.3 Risk Factor Investing 48

2.4 Fama- MacBeth Regressions 50

2.5 Expanding the Risk Factor Universe 53

3 ESG Ratings 55

3.1 Introduction 55

3.2 Overview of ESG Rating Methodologies 57

3.3 Regression Trees as an Alternative Scoring Technique 61

3.4 Random Forest 69

4 Alternative Data 75

4.1 What Are Alternative Data and Their ESG Applications? 75

4.2 How to Validate an ESG Data Provider 81

4.3 Processing Satellite Data 83

5 Alternative Text Data 105

5.1 Alternative Text Data on ESG 105

5.2 Corporate ESG Reports 108

5.3 Topic Modeling 114

5.4 Latent Dirichlet Allocation 118

5.5 Outlier Topics 126

6 Introduction to Agent- Based Modeling for ESG Finance 129

6.1 Preface 129

6.2 Use of Agent- Based Models in Other Fields and Their Applicability to ESG Finance 131

6.3 Use of ABMs in the ESG Field 132

6.4 General Overview of ABMs 133

6.5 General Operating Principles of ABMs 136

6.6 Example of the PartE Framework Applied to an ESG Scenario 136

6.7 Why We Should Look Closely at ABMs 138

6.8 Challenges in the Use of ABMs 139

6.9 Example: Buildup of a Population Model ABM 140

6.10 In- Depth Review: ABMs in Academic and Regulatory Publications 154

7 Climate Risk: Macro Perspective 165

7.1 Climate Change: Background Information and Definitions 165

7.2 Regulatory Response to Climate Change 185

7.3 Climate Change Modeling 191

7.4 Carbon Risk and Carbon Pricing 199

7.5 Climate Risk in Investment Practice 202

8 Stress Testing for Banks 207

8.1 Stress Testing as a Risk Management Tool 207

8.2 Macroeconomic Stress Scenarios for Climate Risk 213

8.3 Climate Loss Modeling 220

8.4 Climate Stress Testing Exercise 223

8.5 Concluding Notes 224

Index 227