Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.

[1]  Lina Juškaitė,et al.  INVESTIGATION OF THE FEASIBILITY OF INCLUDING DIFFERENT CRYPTOCURRENCIES IN THE INVESTMENT PORTFOLIO FOR ITS DIVERSIFICATION , 2022, Journal Business, Management and Economics Engineering.

[2]  Tea Šestanović,et al.  Cryptocurrency Portfolio Selection—A Multicriteria Approach , 2021, Mathematics.

[3]  Ruppa K. Thulasiram,et al.  Novel Data-Driven Resilient Portfolio Risk Measures Using Sign and Volatility Correlations , 2021, 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC).

[4]  G. Klinkova,et al.  Scrutinizing Distributions Proves That IQ Is Inherited and Explains the Fat Tail , 2020, Applied Mathematics.

[5]  Yechi Ma,et al.  Portfolio optimization in the era of digital financialization using cryptocurrencies , 2020, Technological Forecasting and Social Change.

[6]  Aerambamoorthy Thavaneswaran,et al.  Data-Driven Adaptive Regularized Risk Forecasting , 2020, 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC).

[7]  Aerambamoorthy Thavaneswaran,et al.  Portfolio Optimization Using a Novel Data-Driven EWMA Covariance Model with Big Data , 2020, 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC).

[8]  Ruppa K. Thulasiram,et al.  Fuzzy Value-at-Risk Forecasts Using a Novel Data-Driven Neuro Volatility Predictive Model , 2019, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC).

[9]  D. Baur,et al.  Asymmetric Volatility in Cryptocurrencies , 2018, Economics Letters.

[10]  Marcio Pereira Basilio,et al.  Investment portfolio formation via multicriteria decision aid: a Brazilian stock market study , 2018 .

[11]  Yaohao Peng,et al.  The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression , 2018, Expert Syst. Appl..

[12]  A. Thavaneswaran,et al.  Generalized value at risk forecasting , 2018, Communications in Statistics - Theory and Methods.

[13]  G. Klinkova,et al.  Due to Instability Gambling is the best Model for most Financial Products , 2017 .

[14]  Samuli Honkapuro,et al.  Enhancement of equity portfolio performance using data envelopment analysis , 2012, Eur. J. Oper. Res..

[15]  B. Abraham,et al.  Joint Estimation Using Quadratic Estimating Function , 2011 .

[16]  Kin Keung Lai,et al.  Mean-Variance-Skewness-Kurtosis-based Portfolio Optimization , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[17]  M. Thompson,et al.  Filtering via estimating functions , 1999 .

[18]  S. Nakamoto,et al.  Bitcoin: A Peer-to-Peer Electronic Cash System , 2008 .