Data-Driven Adaptive Regularized Risk Forecasting

Liang, You; Thavaneswaran, A.; Zhu, Zimo; Thulasiram, Ruppa K.; Hoque, Md. Erfanul

doi:10.1109/compsac48688.2020.00-77

Cited by 11 publications

(2 citation statements)

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“…As the value of a cryptocurrency is driven purely by the trust that is placed on them, and the transactions can happen at any point in time, volatility associated with cryptocurrencies is very high Baur and Dimpfl (2018); Peng et al (2018). Studies Thavaneswaran et al (2020Thavaneswaran et al ( , 2019; Liang et al (2020) focus on the estimation of an investment's volatility, along with other risk metrics like VaR. In 1952, Markowitz introduced modern portfolio theory with a framework to calculate optimal weights of assets in an investment portfolio Selection (1959).…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Bowala

Singh

2022

JRFM

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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.

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Section: Literature Reviewmentioning

confidence: 99%

“…Recent studies in the area of computational finance have identified the negative effect of high kurtosis of the returns on traditional approaches Thavaneswaran et al (2019Thavaneswaran et al ( , 2020; Liang et al (2020). Furthermore, the normality assumption of returns leads to the underestimation of risks.…”

Section: Introductionmentioning

confidence: 99%