Portfolio selection: should investors include crypto‐assets? A multiobjective approach

Youssef, Meriem; Naoua, Bouthaina Ben; Abdelaziz, Fouad Ben; Chibane, Messaoud

doi:10.1111/itor.13203

Cited by 8 publications

(5 citation statements)

References 34 publications

(41 reference statements)

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“…Cong et al (2021) build a dynamic asset pricing model of a cryptocurrency to conduct peer-to-peer transactions and find that the adoption of tokens lowers users' transaction costs on the platform. Youssef et al (2023) find that adding alternative crypto-assets to a portfolio improves portfolio performance and the efficiency frontier. Cong and He (2019) analyze how the decentralization of blockchain relates to the quality of consensus.…”

Section: Related Literaturementioning

confidence: 89%

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Combating fraudulent returns using blockchain technology

Liu

Cai

Niu

et al. 2023

Int Trans Operational Res

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Fraudulent returns have become increasingly frequent in the development of online retail platforms, leading to huge losses for retailers. A blockchain‐based system can help eliminate this problem primarily because the data stored in a blockchain network cannot be modified. In this paper, we study the impact of blockchain technology that aims to eliminate fraudulent return behavior on retailers and their upstream suppliers. We develop a game‐theoretic model that comprises a supplier, a retailer, and customers and analyze the equilibrium outcomes. The results show that the retailer would (not) adopt blockchain technology if the efficiency of the supplier's investment in the innovation of its products is high (low). Moreover, if the retailer's loss from the return is low (high), the adoption of blockchain technology decreases (increases) the supplier's investment and the wholesale and retail prices. In terms of its effect on profitability, we reveal that if the fraudulent return losses are high, using blockchain technology always benefits the supplier; otherwise, it increases retailer profitability and supply chain performance but may make the supplier suffer losses in profits. Interestingly, the retailer would adopt blockchain technology to ensure profitability in situations where the number of customers without fraudulent returns and the intensity of losses are low; however, this weakens supplier profitability and meanwhile reduces supply chain performance.

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Section: Related Literaturementioning

confidence: 89%

“…Youssef et al. (2023) find that adding alternative crypto‐assets to a portfolio improves portfolio performance and the efficiency frontier. Cong and He (2019) analyze how the decentralization of blockchain relates to the quality of consensus.…”

Section: Related Literaturementioning

confidence: 99%

Combating fraudulent returns using blockchain technology

Liu

Cai

Niu

et al. 2023

Int Trans Operational Res

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“…Researchers such as Ćosić and Časni (2019) argue that cryptocurrencies can enhance portfolio efficiency, while others highlight the challenges for digital currency portfolio selection as a result of their volatility and skewness (Aljinović et al , 2021; Bowala and Singh, 2022). The role of cryptocurrencies as hedging tools is also debated: some studies indicate a low correlation with traditional assets and potential portfolio diversification benefits (Andrianto and Diputra, 2017; Tan et al , 2023; Youssef et al , 2023); meanwhile, others note increasing correlations, especially during turbulent periods like the COVID-19 pandemic (Iyer, 2022; Klein et al , 2018; Smales, 2019). These conflicting insights underline the complexities of integrating cryptocurrencies into investment portfolios and underscore the need for further research to understand their full implications for retail investors.…”

Section: Literature Reviewmentioning

confidence: 99%

Do cryptocurrency investors in the UK need more protection?

Wang

2024

JFRC

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Purpose The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners. Design/methodology/approach This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links. Findings The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions. Originality/value This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

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“…A multi-objective approach was used to analyze a range of assets, including Bitcoin, Ethereum, Ripple, NASDAQ, S&P500, Dow-Jones, crude oil, and gold, within the context of investment portfolio selection [51]. They framed the problem as a bi-objective optimization, where investors aimed to optimize both portfolio risk and return.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

Bâra,

Georgescu,

Oprea

et al. 2024

IEEE Access

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In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec's Probability of Failure (POF) test and Christoffersen's test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD→BRENT and BTCUSD→S&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.INDEX TERMS backtesting, Bitcoin, Brent crude oil, GARCH model, price volatility, S&P500

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Portfolio selection: should investors include crypto‐assets? A multiobjective approach

Cited by 8 publications

References 34 publications

Combating fraudulent returns using blockchain technology

Combating fraudulent returns using blockchain technology

Do cryptocurrency investors in the UK need more protection?

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

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