A Comparison of Optimal Cryptocurrency Portfolios Performance Based on Downside Risk Measures: An Analysis of Quantile-Based Risk Measures
Subject Areas : Journal of Capital Market Analysis
Mostafa Shabani
1
(MSc in Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
Hossein Ghanbari
2
(PhD Candidate in Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
emran mohammadi
3
(School of Industrial Engineering, Iran University of Science and Technology,iran)
Seyed Ali Mousavi Loleti
4
(Student of industrial engineering with a major in financial engineering, Department of Industrial Engineering, Tehran Branch, Iran University of Science and Technology, Tehran, Iran.)
Keywords: Portfolio optimization, Downside risk measures, Conditional Value at Risk, Conditional Drawdown at Risk,
Abstract :
In the realm of burgeoning financial landscapes, the cryptocurrency market has garnered substantial attention owing to its pronounced profit potential. Despite witnessing remarkable expansion in recent years, investing in this market is concurrently entwined with heightened risk levels. Consequently, the imperative of selecting a judicious strategy and criteria for investment and risk assessment within the cryptocurrency market cannot be overstated. Among diverse risk measures, Quantile Based Risk Measures emerge as pivotal tools due to their intrinsic capacity to precisely discern adverse risks. Consequently, both investors and scholars exhibit a keen inclination towards leveraging this category of risk measures. This research delves into the scrutiny and juxtaposition of portfolio efficiency comprising cryptocurrencies, employing Conditional value-at-risk and conditional dropdown value at risk as two paramount risk measures based on percentiles. Such an examination not only facilitates investors in adeptly steering their cryptocurrency investment portfolios through well-informed decision-making but also contributes to an enhanced comprehension of these risk measures and their potential application in diverse investment contexts. To bolster the practical utility of these models in authentic settings, operational constraints have been deliberately incorporated into their design. The research outcomes underscore that the conditional value-at-risk model yields superior performance. Adopting it as a preferred criterion empowers investors to make decisions imbued with greater discernment and evidence-based rationale in managing their investment portfolios. These findings not only deepen the understanding of these risk criteria and their applicability in investment decisions but also furnish investors with insights essential for making judicious and optimal
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