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Titlebook: Uncertain Portfolio Optimization; Zhongfeng Qin Book 2016 Springer Science+Business Media Singapore 2016 Credibility Theory.Portfolio Opti

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發(fā)表于 2025-3-21 18:58:05 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Uncertain Portfolio Optimization
編輯Zhongfeng Qin
視頻videohttp://file.papertrans.cn/942/941071/941071.mp4
概述Presents a comprehensive and up-to-date guide to uncertain portfolio optimization.Can serve as a valuable reference source for academics, researchers and practitioners.Provides an efficient approach t
叢書(shū)名稱(chēng)Uncertainty and Operations Research
圖書(shū)封面Titlebook: Uncertain Portfolio Optimization;  Zhongfeng Qin Book 2016 Springer Science+Business Media Singapore 2016 Credibility Theory.Portfolio Opti
描述This book provides a new modeling approach for portfolio optimization problems involving a lack of sufficient historical data. The content mainly reflects the author’s extensive work on uncertainty portfolio optimization in recent years. Considering security returns as different variables, the book presents a series of portfolio optimization models in the framework of credibility theory, uncertainty theory and chance theory, respectively. As such, it offers readers a comprehensive and up-to-date guide to uncertain portfolio optimization models.
出版日期Book 2016
關(guān)鍵詞Credibility Theory; Portfolio Optimization; Uncertainty Theory; Fuzzy Random Variable; Mean-Variance Mod
版次1
doihttps://doi.org/10.1007/978-981-10-1810-7
isbn_softcover978-981-10-9451-4
isbn_ebook978-981-10-1810-7Series ISSN 2195-996X Series E-ISSN 2195-9978
issn_series 2195-996X
copyrightSpringer Science+Business Media Singapore 2016
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Interval Mean-Semiabsolute Deviation Model,ance model, Li and Xu (2009) proposed an interval goal programming model on the assumption that security returns are fuzzy variables, and Wu et al. (2013) revisited interval mean-variance analysis by assuming that the expected returns and covariances of assets are both intervals.
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Uncertain Mean-Semiabsolute Deviation Model,k of uncertainty theory (Liu 2007). In particular, several researchers have studied portfolio optimization in which security returns are assumed to be uncertain variables. The first attempt is Qin et al. (2009) who formulated the uncertain counterpart of mean-variance model. As extensions, Liu and Q
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Uncertain Mean-LPMs Model,attracted more and more attentions since investors are often sensitive to downside losses, relative to upside gains. Moreover, it requires simpler theoretical assumptions to justify its application. In portfolio management, investors always prefer securities with smaller downside risk. In the situat
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