Artikel

Risk return trade-off in relaxed risk parity portfolio optimization

This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the model and permitting the portfolio to violate the risk parity condition. This paper proposes the incorporation of an explicit target return goal with an intuitive target return approach into a second-order-cone model of a risk parity optimization. When the target return is greater than risk parity return, a violation to risk parity allocations occurs that is controlled using a computational construct to obtain near-risk parity portfolios to retain as much risk parity-like traits as possible. This model is used to demonstrate empirically that higher returns can be achieved than risk parity without the risk contributions deviating dramatically from the risk parity allocations. Furthermore, this study reveals that the relaxed risk parity model exhibits advantageous traits of robustness to expected returns, which should not deter the use of expected returns in risk parity model.

Sprache
Englisch

Erschienen in
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 13 ; Year: 2020 ; Issue: 10 ; Pages: 1-28 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
asset allocation
equal-risk budget
marginal risk contribution
Markowitz
portfolio optimization
relaxation
risk parity
risk-based
robust
robust optimization

Ereignis
Geistige Schöpfung
(wer)
Gambeta, Vaughn
Kwon, Roy
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2020

DOI
doi:10.3390/jrfm13100237
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Gambeta, Vaughn
  • Kwon, Roy
  • MDPI

Entstanden

  • 2020

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