Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

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