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Risks
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Portfolio Theory, Financial Risk Analysis and Applications
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Portfolio Theory, Financial Risk Analysis and Applications

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 10 January 2025 | Viewed by 1979

Special Issue Editors

Prof. Dr. Stanislaus Maier-Paape

E-Mail Website
Guest Editor
Institut für Mathematik, RWTH Aachen University, D-52062 Aachen, Germany
Interests: asset allocation; risk management; portfolio optimization and quantitative finance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiji Zhu

E-Mail Website
Guest Editor
Department of Mathematics, Western Michigan University, Kalamazoo, MI 49008, USA
Interests: financial mathematics and risk management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Portfolio optimization and related financial risk analysis are central themes in financial mathematics. The pioneering work of Markowitz on optimal portfolio theory has had a profound impact on both financial theory and practice. Early portfolio theory focused on the trade-off between return as an indication of reward and risk measured by volatility.

Nevertheless, portfolio optimization has evolved over the years, with several recent contributions made under the general portfolio theory framework. A major focus has been on systematically handling multiple risks in asset allocation.

Accordingly, we welcome research submissions to this Special Issue concerning new developments in portfolio theory with an emphasis on multiple financial risks, risk diversification, and applications. Contributions may be theoretical, practical, or a combination of both.

We encourage applications supported by statistical approaches, with a preference for distinguishing between in-sample and out-of-sample results to evaluate predictive quality. Comparisons with standard benchmarks such as indices or equal-weight portfolios are also welcome.

We welcome high-quality paper submissions related, but not limited, to the following topics:

- Portfolio theory/optimization;
- Analysis of risk measures and multiple risks;
- Applied risk management;
- Asset allocation in theory and practice;
- Applications in finance and elsewhere;
- Combinations of the above.

Prof. Dr. Stanislaus Maier-Paape
Prof. Dr. Qiji Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • portfolio theory and optimization
  • applied finance
  • asset allocation
  • multiple risks
  • portfolio diversification

Published Papers (2 papers)

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Research

26 pages, 882 KiB  
Article
Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact
by Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo and Jacopo Maria Ricci
Risks 2024, 12(5), 78; https://doi.org/10.3390/risks12050078 - 11 May 2024
Viewed by 514
Abstract
This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and [...] Read more.
This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks. Full article
(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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20 pages, 1352 KiB  
Article
Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios
by Francesco Catalano, Laura Nasello and Daniel Guterding
Risks 2024, 12(4), 66; https://doi.org/10.3390/risks12040066 - 12 Apr 2024
Viewed by 1126
Abstract
Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in [...] Read more.
Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors. Full article
(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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