Quantitative Methods for ESG Finance

A quantitative analyst’s introduction to the theory and practice of ESG finance

In Quantitative Methods for ESG Finance, accomplished risk and ESG experts Dr. Cyril Shmatov and Cino Robin Castelli deliver an incisive and essential introduction to the quantitative basis of ESG finance from a quantitative analyst’s perspective. The book combines the theoretical and mathematical bases underlying risk factor investing and risk management with accessible discussions of ESG applications.

The authors explore the increasing availability of non-traditional data sources for quantitative analysts and describe the quantitative/statistical techniques they’ll need to make practical use of these data. The book also offers:

• A particular emphasis on climate change and climate risks, both due to its increasing general importance and accelerating regulatory change in the space
• Practical code examples in a Python Jupyter notebook that use publicly available data to demonstrate the techniques discussed in the book
• Expansive discussions of risk factor investing, portfolio construction, ESG scoring, new ESG-driven financial products, and new financial risk management applications, particularly those making use of the proliferation of “alternative data”, both text and images

A must-read guide for quantitative analysts, investment managers, financial risk managers, investment bankers, and other finance professionals with an interest in ESG-driven investing, Quantitative Methods for ESG Finance will also earn a place on the bookshelves of graduate students of business and finance.

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            3

Introduction and Book Overview               5

1. Overview        5

2. Why ESG Finance?       5

3. Why Quantitative Methods?   6

4. Target Audience and Timing of This Book         6

5. Book Outline  7

Chapter 1. Introduction to ESG Finance   10

1.1. Preface: ESG is not a niche strategy anymore.             10

1.2. Introduction and Definitions.              12

1.3. ESG Investment Performance.            27

1.4. Sustainability and Sustainable Finance.          31

Chapter 2. Factor Investing and Smart Beta          47

2.1. Index Construction Basics    47

2.2. Smart Beta Indexes 48

2.3. Risk Factor Investing              57

2.4. Fama-MacBeth Regressions 59

2.5. Expanding the Risk Factor Universe  62

Chapter 3. ESG Ratings   65

3.1. Introduction              65

3.2. Overview of ESG Rating Methodologies          67

3.3. Regression Trees as an Alternative Scoring Technique              72

3.4. Random Forest         82

Chapter 4. Alternative Data         89

4.1. What Are Alternative Data and Their ESG Applications            89

4.2. Processing Satellite Data      96

Chapter 5. Alternative Text Data               122

5.1. Alternative Text Data on ESG              122

5.2. Corporate ESG Reports.        125

5.3. Topic Modeling         131

5.4. Latent Dirichlet Allocation    136

5.5. Outlier Topics           145

Chapter 6. Introduction to Agent-Based-Modeling for ESG Finance             147

6.1. Preface.       147

6.2. Use of Agent-Based Models in other fields and their applicability to ESG Finance.        148

Chapter 7. Climate Risk: Macro Perspective           185

7.1. Climate Change: Background Information and Definitions      185

7.2. Regulatory Response to Climate Change        208

7.3. Climate Change Modeling     214

7.4. Carbon Risk and Carbon Pricing         223

7.5. Climate Risk in Investment Practice  228

Chapter 8. Stress Testing for Banks           233

8.1. Stress Testing as a Risk Management Tool    233

8.2. Macroeconomic Stress Scenarios for Climate Risk      239

8.3. Climate Loss Modeling           250

8.4. Climate Stress Testing Exercise          252

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