In this paper, we compare the mean–variance portfolio modeling based on the possibilistic representation of the future stock returns to the one based on the classical probabilistic modelization of the same returns. There exist several different definitions of possibilistic mean, possibilistic variance and possibilistic covariance. In this paper, we consider definitions recently proposed in the literature formodeling portfolio selection problems: the possibilistic mean and variance à laCarlsson–Fullér–Majlender, the lower possibilistic mean and variance, and the upper possibilistic mean and variance. In particular, we mean to answer to the following research questions: first, to check whether, from a methodological and theoretical standpoint, it is possible to detect elements of superiority of one of the two approaches with respect to the other one; then, to check whether, from an operational point of view, one of the two approaches is more effective than the other one in terms of virtual-future performances.We disclosed that, on the basis of the results we obtained, the winner is the probabilistic approach.

In this paper, we compare the mean-variance portfolio modeling based on the possibilistic representation of the future stock returns to the one based on the classical probabilistic modelization of the same returns. There exist several different definitions of possibilistic mean, possibilistic variance and possibilistic covariance. In this paper, we consider definitions recently proposed in the literature for modeling portfolio selection problems: the possibilistic mean and variance a la Carlsson-Fuller-Majlender, the lower possibilistic mean and variance, and the upper possibilistic mean and variance. In particular, we mean to answer to the following research questions: first, to check whether, from a methodological and theoretical standpoint, it is possible to detect elements of superiority of one of the two approaches with respect to the other one; then, to check whether, from an operational point of view, one of the two approaches is more effective than the other one in terms of virtual-future performances. We disclosed that, on the basis of the results we obtained, the winner is the probabilistic approach.

Possibilistic mean–variance portfolios versus probabilistic ones: the winner is…

Marco Corazza
;
Carla Nardelli
2019-01-01

Abstract

In this paper, we compare the mean-variance portfolio modeling based on the possibilistic representation of the future stock returns to the one based on the classical probabilistic modelization of the same returns. There exist several different definitions of possibilistic mean, possibilistic variance and possibilistic covariance. In this paper, we consider definitions recently proposed in the literature for modeling portfolio selection problems: the possibilistic mean and variance a la Carlsson-Fuller-Majlender, the lower possibilistic mean and variance, and the upper possibilistic mean and variance. In particular, we mean to answer to the following research questions: first, to check whether, from a methodological and theoretical standpoint, it is possible to detect elements of superiority of one of the two approaches with respect to the other one; then, to check whether, from an operational point of view, one of the two approaches is more effective than the other one in terms of virtual-future performances. We disclosed that, on the basis of the results we obtained, the winner is the probabilistic approach.
File in questo prodotto:
File Dimensione Formato  
2019-Corazza_Nardelli-Possibilistic_mean_variance_portfolios_versus_probabilistic_ones_the_winner_is...-DEAF.pdf

non disponibili

Descrizione: Articolo nella versione dell'editore.
Tipologia: Versione dell'editore
Licenza: Accesso chiuso-personale
Dimensione 612.28 kB
Formato Adobe PDF
612.28 kB Adobe PDF   Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3709755
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact