Enhanced index tracking with CVaR-based ratio measures
G Guastaroba , Renata Mansini , Włodzimierz Ogryczak , M. Grazia Speranza
AbstractThe enhanced index tracking problem (EITP) calls for the determination of an optimal portfolio of assets with the bi-objective of maximizing the excess return of the portfolio above a benchmark and minimizing the tracking error. The EITP is capturing a growing attention among academics, both for its practical relevance and for the scientific challenges that its study, as a multi-objective problem, poses. Several optimization models have been proposed in the literature, where the tracking error is measured in terms of standard deviation or in linear form using, for instance, the mean absolute deviation. More recently, reward-risk optimization measures, like the Omega ratio, have been adopted for the EITP. On the other side, shortfall or quantile risk measures have nowadays gained an established popularity in a variety of financial applications. In this paper, we propose a class of bi-criteria optimization models for the EITP, where risk is measured using the weighted multiple conditional value-at-risk (WCVaR). TheWCVaR is defined as a weighted combination ofmultiple CVaR measures, and thus allows a more detailed risk aversion modeling compared to the use of a single CVaR measure. The application of the WCVaR to the EITP is analyzed, both theoretically and empirically. Through extensive computational experiments, the performance of the optimal portfolios selected by means of the proposed optimization models is compared, both in-sample and, more importantly, out-of-sample, to the one of the portfolios obtained using another recent optimization model taken from the literature.
|Journal series||Annals of Operations Research, ISSN 0254-5330, e-ISSN 1572-9338|
|Publication size in sheets||2.4|
|Keywords in English||Enhanced index tracking · Quantile risk measures · Conditional value-at-risk · Mean-risk models · Risk-reward ratios · Risk-averse optimization · Stochastic dominance · Linear programming|
|Score||= 70.0, 15-09-2020, ArticleFromJournal|
|Publication indicators||= 0; = 0; : 2018 = 1.334; : 2018 = 2.284 (2) - 2018=2.267 (5)|
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