A statistically optimal estimator of semivariance

Josephy, Norman; Aczel, Amir D.

doi:10.1016/0377-2217(93)90067-w

Cited by 13 publications

(16 citation statements)

References 6 publications

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“…Following J.P. Morgan RiskMetrics and many empirical studies, assuming 4 that μ = 0, an asymptotically unbiased and strongly consistent estimator of the semivariance for a sample of size T is given by (see Josephy and Aczel [1993]) 5 where T denotes the number of terms for which x t < 0, and the scaled semi-standard deviation which we employ for the computation of value at risk is given by the square root of (9).…”

Section: The Semi-variancementioning

confidence: 99%

The Effect of Asymmetries on Stock Index Return Value‐at‐Risk Estimates

Brooks

Persand²

2003

The Journal of Risk Finance

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

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Section: The Semi-variancementioning

confidence: 99%

The Effect of Asymmetries on Stock Index Return Value‐at‐Risk Estimates

Brooks

Persand²

2003

The Journal of Risk Finance

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“…Markowitz (1993) transformed the mean-semi-variance portfolio optimization problem into the mean-variance optimization problem and used the critical line algorithm to obtain the optimal solution. Josephy and Aczel (1993) proposed an unbiased, consistent and effective estimators for the semi-variance.…”

Section: Semi-variancementioning

confidence: 99%

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Sun¹,

Liu

et al. 2019

JRFM

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The literature on portfolio selection and risk measurement has considerably advanced in recent years. The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk measurement. In the summary of the characteristics of financial data, we summarize the literature on fat tail and dependence characteristic of financial data. In the portfolio selection model part, we cover three models: mean-variance model, global minimum variance (GMV) model and factor model. In the portfolio risk measurement part, we first classify risk measurement methods into two categories: moment-based risk measurement and moment-based and quantile-based risk measurement. Moment-based risk measurement includes time-varying covariance matrix and shrinkage estimation, while moment-based and quantile-based risk measurement includes semi-variance, VaR and CVaR.

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“…Also, note that to obtain an asymptotically unbiased and strongly consistent estimator of the semivariance [17], we should use the factor m (m−1) 2 instead of 1 m in the objective function of (2).…”

Section: Mean-semivariance Project Portfolio Selection Problemmentioning

confidence: 99%