NDLI: DynOpt: Incorporating dynamics into mean-variance portfolio optimization

Content Provider	IEEE Xplore Digital Library
Author	Signoretto, M. Suykens, J.A.K.
Copyright Year	2013
Description	Author affiliation: ESAT-SCD/SISTA, Katholieke Univ. Leuven, Leuven, Belgium (Signoretto, M.; Suykens, J.A.K.)
Abstract	Mean-variance (MV) portfolio theory leads to relatively simple and elegant numerical problems. Nonetheless, the approach has been criticized for treating the market parameters as if they were constant over time. We propose a novel convex optimization problem that extends an existing MV formulation with chance constraint(s) by accounting for the portfolio dynamics. The core idea is to consider a multiperiod scenario where portfolio weights are implicitly regarded as the output of a state-space dynamical system driven by external inputs. The approach leverages a result on realization theory and uses the nuclear norm to penalize complex dynamical behaviors. The proposed ideas are illustrated by two case studies.
Starting Page	48
Ending Page	54
File Size	237362
Page Count	7
File Format	PDF
ISBN	9781467359214
DOI	10.1109/CIFEr.2013.6611696
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Portfolios Optimization Convex functions Reactive power Vectors Resource management Artificial neural networks
Content Type	Text
Resource Type	Article

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