Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution

Cesarone, Francesco; Cesetti, Raffaello; Orlando, Giuseppe; Martino, Manuel L; Ricci, Jacopo Maria

doi:10.3390/math11010050

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…The out-of-sample performance of portfolios described above is evaluated using several performance measures typically adopted in the literature (see, e.g., Bruni et al (2017); Cesarone et al (2022) and references therein). Let R out be the out-of-sample return of the portfolio, and…”

Section: Performance Measuresmentioning

confidence: 99%

Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact

Giunta,

Orlando,

Carleo

et al. 2024

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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.

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Section: Performance Measuresmentioning

confidence: 99%

Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact

Giunta,

Orlando,

Carleo

et al. 2024

Risks

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“…The out-of-sample performance of the 16 Mean-Variance-VaR efficient portfolios is compared with that of the Equally Weighted (EW) portfolio and with For each portfolio strategy, we evaluate the out-of-sample results by using the following performance measures commonly employed in the literature (see, e.g., Cesarone and Colucci 2018;Bruni et al 2017;Cesarone et al 2022b), and described below.…”

Section: Empirical Setup and Performance Measuresmentioning

confidence: 99%

Mean-Variance-VaR portfolios: MIQP formulation and performance analysis

2023

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Value-at-risk is one of the most popular risk management tools in the financial industry. Over the past 20 years, several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a mixed-integer quadratic programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the more binding optimal Mean-Variance-VaR portfolios seems to be generally better than that of the Equally Weighted and of the Mean-Variance-CVaR portfolios.

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Using GAN-generated market simulations to guide genetic algorithms in index tracking optimization

Silva

Filho

2023

Applied Soft Computing

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Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution

Cited by 7 publications

References 66 publications

Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact

Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact

Mean-Variance-VaR portfolios: MIQP formulation and performance analysis

Using GAN-generated market simulations to guide genetic algorithms in index tracking optimization

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