@misc{Fleicher_Karlheinz_Statistically_2019,
 author={Fleicher, Karlheinz and Nietert, Bernhard},
 identifier={DOI: 10.15611/sps.2019.17.01},
 year={2019},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Śląski Przegląd Statystyczny = Silesian Statistical Review, 2019, Nr 17 (23), s. 9-29},
 language={eng},
 abstract={Semivariance is an intuitive risk measure because it concentrates on the shortfall below a target and not on total variation. To successfully use semivariance in practice, however, a statistical estimator of semivariance is needed; Josephy and Aczel provide such an estimator. Unfortunately, they have not correctly proven asymptotic unbiasedness and mean squared error consistency of their estimator since their proof contains a mistake. This paper corrects the computational mistake in Josephy-Aczel’s original proof and, that way, allows researchers and practitioners in the field of downside portfolio selection, hedging, downside asset pricing, risk measurement in a regulatory context, and performance measurement to work with a meaningfully specified downside measure},
 title={Statistically (optimal) estimators of semivariance: A correction of Josephy-Aczel’s proof},
 type={artykuł},
 keywords={risk analysis, semivariance, statistical estimation, analiza ryzyka, semiwariancja, estymacja statystyczna},
}