Sampling Errors and Portfolio Efficient Analysis

Kroll, Yoram; Levy, Haim

doi:10.2307/2330403

Cited by 58 publications

(33 citation statements)

References 9 publications

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“…However, the existing evidence about the discriminatory power (e.g., Kroll andLevy 1980, Nelson andPope 1991) refers to comparisons of the SD and MV efficient subsets in cases where MV accounts for diversification, while SD does not. Compared to the earlier, "approximative" necessary tests, the exact tests developed above improve the power of SD criteria decisively.…”

Section: Discussionmentioning

confidence: 99%

Efficient Diversification According to Stochastic Dominance Criteria

2004

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T his paper develops the first operational tests of portfolio efficiency based on the general stochastic dominance (SD) criteria that account for an infinite set of diversification strategies. The main insight is to preserve the cross-sectional dependence of asset returns when forming portfolios by reexpressing the SD criteria in T -dimensional Euclidean space, with elements representing rates of return in T different states of nature. We characterize subsets of this state-space that dominate a given evaluated return vector by first-and secondorder SD. This allows us to derive simple SD efficiency measures and test statistics, computable by standard mathematical programming algorithms. The SD tests and efficiency measures are illustrated by an empirical application that analyzes industrial diversification of the market portfolio.

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Section: Discussionmentioning

confidence: 99%

Efficient Diversification According to Stochastic Dominance Criteria

2004

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“…Despite its obvious advantages, SD is not widely used in an empirical context because it is marred by sampling error considerations (see e.g. Kroll and Levy, 1980). Recent theoretical work on sampling error in SD tests with iid data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of portfolio insurance strategies using stochastic dominance criteria

Annaert

Osselaer

Verstraete³

2009

Journal of Banking & Finance

107

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“…Thus, a multitude of probabilistic models was proposed. This was also the case with the use of modeling in finance (e.g., Kroll & Levy, 1980;Pastor, 2000). However, no simple and adequate methods for handling imprecise data or vague information, which may stem, for example, from the use of natural language and subjective statements, were available until the mid-1960's when Zadeh (1965) proposed fuzzy-sets theory (see Bellman & Zadeh, 1970).…”

Section: Fuzzy Modeling In Financementioning

confidence: 89%