This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.
Environmental, Social, and Governance (ESG) scores are a key tool for asset managers in designing and implementing ESG investment strategies. They, however, amalgamate a broad range of fundamentally different factors, creating ambiguity for investors as to the underlying drivers of higher or lower ESG scores. We explore the feasibility and performance of more targeted investment strategies based on specific ESG categories, by deconstructing ESG scores into their granular components. First, we investigate the characteristics of the various categories underlying ESG scores. Not all types of ESG categories lend themselves to more focused strategies, which is related to both limits to ESG data disclosure and the fundamental challenge of translating qualitative characteristics into quantitative measures. Second, we consider an investment scheme based on the exclusion of firms with the lowest scores in a given category of interest. In most cases, this strategy allows investors to substantially improve the ESG category score, with a marginal impact on financial performance relative to a broad stock market benchmark. The exclusion results in regional and sectoral biases relative to the benchmark, which may be undesirable for some investors.We then implement a “best-in-class” strategy by excluding firms with the lowest category scores and reinvesting the proceeds in firms with the highest scores, maintaining the same regional and sectoral composition. This approach reduces the tracking error of the portfolio and slightly improves its risk-adjusted performance, while still yielding a large gain in the targeted ESG category score.
This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.
Environmental, Social, and Governance (ESG) scores are a key tool for asset managers in designing and implementing ESG investment strategies. They, however, amalgamate a broad range of fundamentally different factors, creating ambiguity for investors as to the underlying drivers of higher or lower ESG scores. We explore the feasibility and performance of more targeted investment strategies based on specific ESG categories, by deconstructing ESG scores into their granular components. First, we investigate the characteristics of the various categories underlying ESG scores. Not all types of ESG categories lend themselves to more focused strategies, which is related to both limits to ESG data disclosure and the fundamental challenge of translating qualitative characteristics into quantitative measures. Second, we consider an investment scheme based on the exclusion of firms with the lowest scores in a given category of interest. In most cases, this strategy allows investors to substantially improve the ESG category score, with a marginal impact on financial performance relative to a broad stock market benchmark. The exclusion results in regional and sectoral biases relative to the benchmark, which may be undesirable for some investors.We then implement a “best-in-class” strategy by excluding firms with the lowest category scores and reinvesting the proceeds in firms with the highest scores, maintaining the same regional and sectoral composition. This approach reduces the tracking error of the portfolio and slightly improves its risk-adjusted performance, while still yielding a large gain in the targeted ESG category score.
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