A Literature Review for Nonparametric Frontier Methods Applied to Portfolio Analysis
Infosys Science Foundation Series in Mathematical Sciences, ISSN: 2364-4044, Vol: Part F3593, Page: 235-257
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Book Chapter Description
This chapter systematically summarizes previous research on nonparametric frontier methods employed for portfolio performance evaluation and benchmarking, including the diversified and nondiversified frontier models. The former is based on the diversified portfolio frontier, which explicitly considers the diversification effect when combining portfolios, while the latter directly derived from the production involves the envelope of the convex or nonconvex combinations of observed portfolios. Both mainstream methods are reviewed and discussed in single-horizon and multi-horizon portfolio analysis frameworks, respectively, covering the latest developments in this field, existing opportunities, and potential directions for future research. Additionally, we provide a generic production possibility set (PPS) formula for each class of methods to analytically clarify their modeling ideas. This literature study, along with a comprehensive summary of conceptual, methodological, and empirical developments, serves as a reference and guideline for future investigative work on the applications of nonparametric frontier methods in portfolio performance assessment.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85208143392&origin=inward; http://dx.doi.org/10.1007/978-981-97-6972-8_11; https://link.springer.com/10.1007/978-981-97-6972-8_11; https://dx.doi.org/10.1007/978-981-97-6972-8_11; https://link.springer.com/chapter/10.1007/978-981-97-6972-8_11
Springer Science and Business Media LLC
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