Optimal algorithms and lower partial moment: ex post results

Nawrocki, David N.

doi:10.1080/00036849100000021

Cited by 67 publications

(31 citation statements)

References 19 publications

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“…The results agree with others studies (e.g. Nawrocki, 1991) that show that the number of assets considered by a LPM approach is lower than from a variance perspective. Moreover, the results also show that the number of assets decreases as the degree of LPM increases.…”

Section: Resultssupporting

confidence: 83%

“…Nawrocki, 1991) is extended by explicitly considering forecasted instead of realized measures of risk, that is, in the solution of Markowitz's problem the variances and semideviations obtained from ARCH-type or autoregressive models are employed instead of the historical ones. In this sense, the work is closer to the real behaviour of investors who extrapolate, in some pertinent way, the expected optimal portfolio.…”

mentioning

confidence: 99%

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Risk forecasting models and optimal portfolio selection

2005

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This study analyses, from an investor's perspective, the performance of several risk forecasting models in obtaining optimal portfolios. The plausibility of the homoscedastic hypothesis implied in the classical Markowitz model is dicussed and more general models which take into account assymetry and time varying risk are analysed. Specifically, it studies whether ARCH-type based models obtain portfolios whose risk-adjusted returns exceed those of the classical Markowitz model. The same analysis is performed with models based on the Lower Partial Moment (LPM) which take into account the assymetry in the distribution of returns. The results suggest that none of the models achieve a clearly superior average performance. It is also found that models based on semivariance perform as well as those based on the variance, but not better than, even if the evaluation criterion is based on the Reward-toSemivariance ratio. When attention turns to the analysis of worst case performance, the results are clearly different. Models which employ LPM with a high degree of risk aversion (n>2) as the risk measure are consistently superior to those which employ a symmetric measure, either homoscedastic or heteroscedastic.

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Section: Resultssupporting

confidence: 83%

mentioning

confidence: 99%

Risk forecasting models and optimal portfolio selection

2005

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“…Using the currency forward contracts has the advantage of potentially reducing the variability of assets' returns, which is in the investors' favour. See Eun & Resnick (1988) We use the n-degree portfolio LPM algorithm to model the portfolio downside risk presented in Nawrocki (1991) and Moreno et al (2005). The following presents the minimum n-degree portfolio LPM formulations…”

Section: Datamentioning

confidence: 99%

The effect of downside risk reduction on UK equity portfolios included with Managed Futures Funds

Tee

2009

International Review of Financial Analysis

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“…Accounting for these two critical limitations, the approaches suggested by Nawrocki (1991) and Estrada (2008) to its exact counterpart as a robustness check and shows that the differences are rather small and even when there is a difference between the approximate semi-deviation and its exact counterpart, the direction of the error is predictable. Estrada (2008, p. 63) concludes that "whenever the approximation errs, it does so on the side of caution, overestimating (by a small amount) the risk of the portfolio."…”

Section: Downside Risk Optimizationmentioning

confidence: 99%

“…Afterwards, Nawrocki (1991) empirically tests the power and differences of the two approaches. According to his empirical results from applying both concepts, the ALPM algorithm as well as the SLPM algorithm, Nawrocki (1991, p. 470) concludes, that "because of its added complexity, the ALPM algorithm does not have the empirical support to recommend it.…”

Section: Co-lower Partial Momentsmentioning

confidence: 99%

Downside Risk Optimization in Securitized Real Estate Markets

Kroencke

Schindler

2010

SSRN Journal

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp10034.pdf In contrast to previous studies in the field of DR optimization in real estate portfolios, the theoretical / technical merit of the Estrada (2008) approach is given by the innovative concept of measuring co-downside movements and using a similar optimization procedure to that in the traditional mean-variance (MV) framework at the same time. To our knowledge, this approach is applied for the first time to portfolios of real estate assets. Furthermore, covering the eight largest securitized real estate markets around the world, the analysis is also the first applying the DR framework in a global context. Non-technical SummaryIn addition to the theoretical merits of the DR framework, the empirical results further support the strength of the applied DR framework compared to the MV framework and provide several interesting and practical implications for investors in international securitized real estate markets. The conducted analysis documents in a comprehensive way that the DR approach suggested by Estrada (2008) has its merits when applying it to securitized real estate portfolio optimization problems. Furthermore, it is shown that portfolio weights differ substantially from MV optimization and the DR framework is superior in an out-of-sample analysis. Thus, the presented DR framework constitutes a useful contribution to investors facing practical portfolio optimization problems in securitized international real estate markets.-II - Das Wichtigste in Kürze

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Optimal algorithms and lower partial moment: ex post results

Cited by 67 publications

References 19 publications

Risk forecasting models and optimal portfolio selection

Risk forecasting models and optimal portfolio selection

The effect of downside risk reduction on UK equity portfolios included with Managed Futures Funds

Downside Risk Optimization in Securitized Real Estate Markets

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