Cryptocurrency portfolio optimization using Value-at-Risk measure

Hrytsiuk, Petro; Babych, Tetiana; Bachyshyna, Larysa

doi:10.2991/smtesm-19.2019.75

Cited by 11 publications

(12 citation statements)

References 12 publications

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“…The proposed algorithm displayed good performance in estimating both VaR and ES. Hrytsiuk et al (2019) showed that the cryptocurrency returns can be described by the Cauchy distribution and obtained the analytical expressions for VaR risk measures and performed calculations accordingly. As a result of the optimisation, the sets of optimal cryptocurrency portfolios were built in their experiments.…”

Section: Crypto-asset Portfolio Researchmentioning

confidence: 99%

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Cryptocurrency trading: a comprehensive survey

et al. 2022

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In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

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Section: Crypto-asset Portfolio Researchmentioning

confidence: 99%

“…tions and major feature engineering have the potential to further improve the predictive power. More Intelligent Evolutionary Optimisation (IEO) for hyperparameter optimisation is core problem when tuning the overall optimization process of machine learning models(Huan et al 2020). Lu et al (2020) proposed a CNN-LSTM based method for stock price prediction.…”

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confidence: 99%

Cryptocurrency trading: a comprehensive survey

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“…: Sefiane i Benbouziane, 2012; Škarica i Lukač, 2012; Tarczyński, 2014; Gluzicka, 2016; Goudarzi et. al., 2017;Hrytsiuk et al, 2019;Mercurio et. al., 2020).…”

Section: Wstępmentioning

confidence: 99%

“…naive strategy, classical Markowitz model and its modifications, optimization strategies taking into account e. g. the value at risk, the diversification level, taxonomic measures of investment attractiveness or investment strategies based on different methods of computational intelligence, such as genetic algorithms or hybrid models (see for example: Sefiane and Benbouziane, 2012; Škarica and Lukač, 2012;Tarczyński, 2014;Gluzicka, 2016;Goudarzi et. al., 2017;Hrytsiuk, 2019;Mercurio et. al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Selected Portfolio Strategies Based on the Example of Cryptocurrency Portfolios

Kądziołka

2021

Economic and Regional Studies / Studia Ekonomiczne I Regionalne

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Subject and purpose of work: The purpose of this work was to compare selected portfolio strategies in terms of return rates in order to answer the question whether the method of determining the weights of the portfolio and reduction of the number of portfolio elements characterized by strong positive correlation of rates of return have an impact on its profitability. Materials and methods: The analysis used publicly available data, selected portfolio methods and hierarchical clustering. Both short- and long-term investment strategies were examined. Results: None of analyzed strategies allows to achieve higher rates of return in any given (arbitrarily selected) period than other analyzed strategies. Portfolios with a reduced number of elements in most cases did make it possible to achieve a higher rate of return than the benchmark portfolios consisting of 15 cyptocurrencies. Conclusions: While making investment decisions, one should bear in mind that the realized rate of return may significantly differ from the expected rate of return of the portfolio, which is only a forecast.

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“…The proposed algorithm displayed good performance in estimating both VaR and ES. Hrytsiuk et al [123] showed that the cryptocurrency returns can be described by the Cauchy distribution and obtained the analytical expressions for VaR risk measures and performed calculations accordingly. As a result of the optimisation, the sets of optimal cryptocurrency portfolios were built in their experiments.…”

Section: Crypto-asset Portfolio Researchmentioning

confidence: 99%

Cryptocurrency Trading: A Comprehensive Survey

Fang¹,

Ventre²,

Basios³

et al. 2020

Preprint

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In the recent years, the tendency of the number of financial institutions including cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Even though they share some commonalities with more traditional assets, they have a separate nature of its own and their behaviour as an asset is still under the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 118 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

show abstract

Cryptocurrency portfolio optimization using Value-at-Risk measure

Cited by 11 publications

References 12 publications

Cryptocurrency trading: a comprehensive survey

Cryptocurrency trading: a comprehensive survey

Comparison of Selected Portfolio Strategies Based on the Example of Cryptocurrency Portfolios

Cryptocurrency Trading: A Comprehensive Survey

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