Penalized enhanced portfolio replication with asymmetric deviation measures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F24%3A10253768" target="_blank" >RIV/61989100:27510/24:10253768 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10479-023-05576-z" target="_blank" >https://link.springer.com/article/10.1007/s10479-023-05576-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10479-023-05576-z" target="_blank" >10.1007/s10479-023-05576-z</a>
Alternative languages
Result language
angličtina
Original language name
Penalized enhanced portfolio replication with asymmetric deviation measures
Original language description
Passive investment strategies, such as those implemented by Exchange Traded Funds (ETFs), have gained increasing popularity among investors. In this context, smart beta products promise to deliver improved performance or lower risk through the implementation of systematic investing strategies, and they are also typically more cost-effective than traditional active management. The majority of research on index replication focuses on minimizing tracking error relative to a benchmark index, implementing constraints to improve performance, or restricting the number of assets included in portfolios. Our focus is on enhancing the benchmark through a limited number of deviations from the benchmark. We propose a range of innovative investment strategies aimed at minimizing asymmetric deviation measures related to expectiles and quantiles, while also controlling for the deviation of portfolio weights from the benchmark composition through penalization. This approach, as compared to traditional minimum tracking error volatility strategies, places a greater emphasis on the overall risk of the portfolio, rather than just the risk relative to the benchmark. The use of penalization also helps to mitigate estimation risk and minimize turnover, as compared to strategies without penalization. Through empirical analysis using simulated and real-world data, we critically examine the benefits and drawbacks of the proposed strategies in comparison to state-of-the-art tracking models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
<a href="/en/project/GA19-11965S" target="_blank" >GA19-11965S: A network approach to portfolio optimization and tracking problems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Annals of Operations Research
ISSN
0254-5330
e-ISSN
1572-9338
Volume of the periodical
332
Issue of the periodical within the volume
1-3
Country of publishing house
US - UNITED STATES
Number of pages
51
Pages from-to
481-531
UT code for WoS article
001072293500001
EID of the result in the Scopus database
2-s2.0-85172659289