Quantitative Methods for ESG Finance - Tapa dura

Acerca del autor

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.”

De la contraportada

In Quantitative Methods for ESG Finance, distinguished risk and finance experts Dr. Cyril Shmatov and Cino Robin Castelli deliver a startlingly insightful combination of the theoretical and quantitative bases underlying risk factor investing and risk management with ESG applications. Written from the quantitative analyst’s perspective, this book discusses both investment management (buy-side) and investment banking (sell-side) applications. It also offers explanations of the proliferation of newly available, “alternative” data sources available to analysts and the quantitative techniques necessary to process them.

The authors pay particular attention to climate risk because of its increasing general importance. They offer an overview of recent quantitative advances and the evolving regulatory landscape in the climate risk space, as well as an in-depth discussion of the financial impact assessment of various climate risk-driven scenarios.

In every chapter, theoretical discussions are accompanied by functional code snippets and walkthroughs of a Python Jupyter notebook containing publicly available data and demonstrating practical examples that apply the techniques introduced in the book.

Investment managers will be particularly interested in the authors’ exposition of risk factor investing, portfolio and index construction, and ESG scoring, while bankers will likely focus on the discussion of a newly emerging class of ESG-driven financial products and new financial risk management applications, many of which are driven by new financial regulations.

An indispensable resource for finance professionals—including investment managers and investment bankers—and researchers, Quantitative Methods for ESG Finance will also earn a place in the libraries of graduate students of business and finance seeking a one-stop reference for the latest quantitative applications in ESG finance.

De la solapa interior

In Quantitative Methods for ESG Finance, distinguished risk and finance experts Dr. Cyril Shmatov and Cino Robin Castelli deliver a startlingly insightful combination of the theoretical and quantitative bases underlying risk factor investing and risk management with ESG applications. Written from the quantitative analyst's perspective, this book discusses both investment management (buy-side) and investment banking (sell-side) applications. It also offers explanations of the proliferation of newly available, "alternative" data sources available to analysts and the quantitative techniques necessary to process them.

The authors pay particular attention to climate risk because of its increasing general importance. They offer an overview of recent quantitative advances and the evolving regulatory landscape in the climate risk space, as well as an in-depth discussion of the financial impact assessment of various climate risk-driven scenarios.

In every chapter, theoretical discussions are accompanied by functional code snippets and walkthroughs of a Python Jupyter notebook containing publicly available data and demonstrating practical examples that apply the techniques introduced in the book.

Investment managers will be particularly interested in the authors' exposition of risk factor investing, portfolio and index construction, and ESG scoring, while bankers will likely focus on the discussion of a newly emerging class of ESG-driven financial products and new financial risk management applications, many of which are driven by new financial regulations.

An indispensable resource for finance professionals--including investment managers and investment bankers--and researchers, Quantitative Methods for ESG Finance will also earn a place in the libraries of graduate students of business and finance seeking a one-stop reference for the latest quantitative applications in ESG finance.