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Tesi etd-05182010-150852


Thesis type
Tesi di dottorato di ricerca
Author
RECCHIA, RAFFAELLA
email address
recchia@di.unipi.it, recchia.raffaella@gmail.com
URN
etd-05182010-150852
Title
Robust asset allocation problems: a new class of risk measure based models
Settore scientifico disciplinare
MAT/09
Corso di studi
MATEMATICA PER LE DECISIONI ECONOMICHE
Commissione
tutor Prof.ssa Scutellà, Maria Grazia
Parole chiave
  • risk measures
  • robustness
  • Portfolio optimization
  • mathematical models
  • computational experimentation
Data inizio appello
31/05/2010;
Consultabilità
completa
Riassunto analitico
Many optimization problems involve parameters which are not known in advance, but can only be forecast or estimated. This is true, for example, in portfolio asset allocation. Such problems fit perfectly into the framework of Robust Optimization that, given optimization problems with uncertain parameters, looks for solutions that will achieve good objective function values for the realization of these parameters in given uncertainty sets.<br> <br>Aim of this dissertation is to compare alternative forms of robustness in the context of portfolio asset allocation. Starting with the concept of convex risk measures, a new family of models, called &#34;norm-portfolio&#34; models, is firstly proposed where not only the values of the uncertainty parameters, but also their degree of feasibility are specified. This relaxed form of robustness is obtained by exploiting the link between convex risk measures and classical robustness. <br><br>Then, we test some norm-portfolio models, as well as various robust strategies from the literature, with real market data on three different data sets. The objective of the computational study is to compare alternative forms of relaxed robustness - the relaxed robustness characterizing the &#34;norm-portfolio&#34; models, the so-called soft robustness and the CVaR robustness. In addition, the models above are compared to a more classical robust model from the literature, in order to experiment similarities and dissimilarities between robust models based on convex risk measures and more traditional robust approaches. <br>
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