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Keywords = shadow riskless rate

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33 pages, 3170 KB  
Article
Environmental, Social and Governance-Valued Portfolio Optimization and Dynamic Asset Pricing
by Davide Lauria, W. Brent Lindquist, Stefan Mittnik and Svetlozar T. Rachev
J. Risk Financial Manag. 2025, 18(3), 153; https://doi.org/10.3390/jrfm18030153 - 13 Mar 2025
Viewed by 3389
Abstract
Environmental, social and governance (ESG) ratings (scores) provide quantitative measures for socially responsible investment. We consider ESG scores to be a third independent variable—on par with financial risk and return—and incorporate such numeric scores into dynamic asset pricing. Based on this incorporation, we [...] Read more.
Environmental, social and governance (ESG) ratings (scores) provide quantitative measures for socially responsible investment. We consider ESG scores to be a third independent variable—on par with financial risk and return—and incorporate such numeric scores into dynamic asset pricing. Based on this incorporation, we develop the entire investment process for the ESG market: portfolio optimization and efficient frontier, capital market line (the market portfolio), risk-assessment measures and hedging instruments (options). There is currently no riskless asset available in such an ESG market; to address this, we develop the so-called shadow riskless rate, applicable to markets having only risky assets. We believe this to be the first paper that fully develops, under a single dynamic pricing framework, the entire investment process for an ESG market. As there are significant differences in methodologies developed by providers of ESG scores, we do not take the position that data from any single agency are to be favored. Consequently, we utilize ESG scores from Refinitiv in the manuscript’s empirical studies and redo all computations using S&P Global RobeoSAM ESG scores. Full article
(This article belongs to the Special Issue Empirical Research on Asset Pricing and Portfolio Selection)
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19 pages, 1107 KB  
Article
An Empirical Implementation of the Shadow Riskless Rate
by Davide Lauria, Jiho Park, Yuan Hu, W. Brent Lindquist, Svetlozar T. Rachev and Frank J. Fabozzi
Risks 2024, 12(12), 187; https://doi.org/10.3390/risks12120187 - 26 Nov 2024
Viewed by 1033
Abstract
We address the problem of asset pricing in a market where there are no risky assets. Previous work developed a theoretical model for a shadow riskless rate (SRR) for such a market, based on the drift component of the state-price deflator for that [...] Read more.
We address the problem of asset pricing in a market where there are no risky assets. Previous work developed a theoretical model for a shadow riskless rate (SRR) for such a market, based on the drift component of the state-price deflator for that asset universe. Assuming that asset prices are modeled by correlated geometric Brownian motion, in this work, we develop a computational approach to estimate the SRR from empirical datasets. The approach employs principal component analysis to model the effects of individual Brownian motions, singular value decomposition to capture abrupt changes in the condition number of the linear system whose solution provides the SRR values, and regularization to control the rate of change of the condition number. Among other uses such as option pricing and developing a term structure of interest rates, the SRR can be used as an investment discriminator between different asset classes. We apply this computational procedure to markets consisting of various groups of stocks, encompassing different asset types and numbers. The theoretical and computational analysis provides the drift as well as the total volatility of the state-price deflator. We investigate the time trajectory of these two descriptive components of the state-price deflator for the empirical datasets. Full article
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