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Entropy-Based Methods for Finance and Risk Management

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 6250

Special Issue Editors

Department of Economics, Roma TRE University, Via Silvio d’Amico, 77, 00145 Roma, Italy
Interests: energy and commodity finance; mathematical finance; quantitative finance
Special Issues, Collections and Topics in MDPI journals
Department of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, Italy
Interests: multi-agent systems for the study of public opinion formation and methods from the theory of representation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Entropy seeks submissions for a Special Issue on Entropy-Based Methods for Finance and Risk Management.

The concept of entropy originates from thermodynamics in the 19th century, but today is used in many research fields. In recent years, the applications of entropy in finance and economics have increased considerably, as demonstrated also by the significant number of papers in this field featured in many journals dealing with entropy-related topics

In mathematics, an abstract definition of entropy is known as Shannon information entropy, but many definitions and applications of entropy have been proposed in the literature, thanks to the generality of its concept.

In finance, entropy has been employed to understand turning points in foreign exchange rate time series, to propose an alternative measure to the standard deviation in stock markets, and to study option and asset pricing through an entropic methodology. In risk management, entropy-based measures of risk and rare-event probabilities have been introduced to innovate the traditional risk management tools, such as the value-at-risk.

In this Special Issue, we welcome innovative contributions and applications in all areas of Finance and Risk Management in which any definition of entropy plays a central role.

The primary acceptance criterion for submission will be the high quality and originality of the contribution. This is an open call for all researchers in this area. 

We especially welcome innovative contributions related to, but are not limited to, the following main topics:

  • Stock markets
  • Energy finance
  • Commodity finance
  • Credit Risk
  • Market Risk
  • Liquidity Risk
  • Operational Risk
  • Climate Risk
  • Asset and Derivative Pricing
  • Network Modelling
  • Portfolio Optimization
  • Systemic Risk
  • Insurance
  • Portfolio selection

Assoc. Prof. Loretta Mastroeni
Dr. Pierluigi Vellucci
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. Entropy 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 2600 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.

Published Papers (3 papers)

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Research

21 pages, 2002 KiB  
Article
A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment
Entropy 2023, 25(9), 1245; https://doi.org/10.3390/e25091245 - 22 Aug 2023
Viewed by 1044
Abstract
Minimizing a company’s operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, [...] Read more.
Minimizing a company’s operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of uncertainty or risk involved. In this study, we focus on a processing production plant as a model for a chain of operations and supply chain actions. We consider the stochastically varying production and transportation costs from the site to the plant, as well as from the plant to the customer base. Through stochastic optimization, we demonstrate that the plant producer can benefit from improved financial outcomes by setting higher sale prices while simultaneously lowering optimized production costs. This can be accomplished by selectively choosing producers whose production cost probability density function follows a Pareto distribution. Notably, a lower Pareto exponent yields better supply chain cost optimization predictions. Alternatively, a Gaussian stochastic fluctuation may be proposed as a more suitable choice when trading off optimization and simplicity. Although this may result in slightly less optimal performance, it offers advantages in terms of ease of implementation and computational efficiency. Full article
(This article belongs to the Special Issue Entropy-Based Methods for Finance and Risk Management)
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30 pages, 746 KiB  
Article
Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators
Entropy 2022, 24(12), 1705; https://doi.org/10.3390/e24121705 - 22 Nov 2022
Cited by 2 | Viewed by 1070
Abstract
We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are [...] Read more.
We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are efficiently exploiting all the information available in the market. Estimators from the GEL family present some optimal statistical properties, such as robustness to misspecifications and better properties in finite samples. Unlike generalized method of moments (GMM) estimators, the bias for GEL estimators does not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. Due to these better properties in finite samples, our main hypothesis is that portfolio efficiency tests using GEL estimators may have better properties in terms of size, power, and robustness. Using Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. Extensive empirical analyses show the properties of the estimators for different sample sizes and portfolio types for two asset pricing models. Full article
(This article belongs to the Special Issue Entropy-Based Methods for Finance and Risk Management)
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21 pages, 575 KiB  
Article
The Properties of Alpha Risk Parity Portfolios
Entropy 2022, 24(11), 1631; https://doi.org/10.3390/e24111631 - 10 Nov 2022
Viewed by 3077
Abstract
Risk parity is an approach to investing that aims to balance risk evenly across assets within a given universe. The aim of this study is to unify the most commonly-used approaches to risk parity within a single framework. Links between these approaches have [...] Read more.
Risk parity is an approach to investing that aims to balance risk evenly across assets within a given universe. The aim of this study is to unify the most commonly-used approaches to risk parity within a single framework. Links between these approaches have been identified in the published literature. A key point in risk parity is being able to identify and control the contribution of each asset to the risk of the portfolio. With alpha risk parity, risk contributions are given by a closed-form formula. There is a form of antisymmetry—or self-duality—in alpha risk portfolios that lie between risk budgeting and minimum-risk portfolios. Techniques from information geometry play a key role in establishing these properties. Full article
(This article belongs to the Special Issue Entropy-Based Methods for Finance and Risk Management)
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