Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Number of Results 1

WP(18/2021)366. Enhanced Index Replication Based on Smart Beta and Tail-Risk Asset Allocation

Authors: Korzeń Kamil, Ślepaczuk Robert
The following research paper’s main goal is to create an algorithmically managed ETF, which tracks the SPX index and provides a Smart Beta exposure. Authors apply the following simple index replication methods: partial correlation, non-negative…

WP366. Enhanced Index Replication Based on Smart Beta and Tail-Risk Asset Allocation

The following research paper’s main goal is to create an algorithmically managed ETF, which tracks the SPX index and provides a Smart Beta exposure. Authors apply the following simple index replication methods: partial correlation, non-negative least squares, beta coefficient, and dynamic time warping. First, authors are trying to reverse engineer the Index Tracking process in an automated and fair manner - taking into account e.g. transaction costs. Additionally, authors apply a constraint to the total number of assets used in the replication process, which is limited to the certain N. Then, authors develop a Smart Beta framework based on limiting the negative tail-risk. The positive excess return (alpha) is captured and used to compensate for the underperformance of the replicated Index or paid in a form of a dividend. Moreover, with the enhancement methods applied (Kurtosis/Skewness and Excess Return Cushion (ERC) enhancements), the authors’ main goal is to keep the Tracking Error (TE) on a fixed level, although with a significant overweight on the Positive TE and underweight on the Negative TE. In the research paper, the data from 04-Jan-2016 to 31-Dec-2020 is used as the training window, while the first quarter of the year 2021 (Q1 2021) is used as an out-of-sample and out-of-time testing period. Additionally, the authors measure the replicated Index’s performance compared to the SPY, VOO, and IVV ETFs. Authors find a piece of empirical evidence that it is possible to track the SPX Index within the limits of 4-5% TE with the limited number of assets. Moreover, after the implementation of alpha accumulation, the authors outperform the benchmark ETFs in terms of minimizing the TE but did not succeed in providing statistically significant returns better than the SPX Index.


Korzeń Kamil, Ślepaczuk Robert
exchange-traded funds enhanced index replication methods smart beta asset allocation partial correlation non-negative least squares dynamic time warping
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