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Econometrics 2017, 5(2), 18; doi:10.3390/econometrics5020018

The Univariate Collapsing Method for Portfolio Optimization

1
Department of Banking and Finance, University of Zurich, Zurich 8032, Switzerland
2
Swiss Finance Institute, Zurich 8006, Switzerland
Academic Editor: In Choi
Received: 9 October 2016 / Revised: 4 February 2017 / Accepted: 17 February 2017 / Published: 5 May 2017
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Abstract

The univariate collapsing method (UCM) for portfolio optimization is based on obtaining the predictive mean and a risk measure such as variance or expected shortfall of the univariate pseudo-return series generated from a given set of portfolio weights and multivariate set of assets under interest and, via simulation or optimization, repeating this process until the desired portfolio weight vector is obtained. The UCM is well-known conceptually, straightforward to implement, and possesses several advantages over use of multivariate models, but, among other things, has been criticized for being too slow. As such, it does not play prominently in asset allocation and receives little attention in the academic literature. This paper proposes use of fast model estimation methods combined with new heuristics for sampling, based on easily-determined characteristics of the data, to accelerate and optimize the simulation search. An extensive empirical analysis confirms the viability of the method. View Full-Text
Keywords: asset allocation; backtest-overfitting; non-ellipticity asset allocation; backtest-overfitting; non-ellipticity
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Paolella, M.S. The Univariate Collapsing Method for Portfolio Optimization. Econometrics 2017, 5, 18.

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