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• W4386367825 abstract "This study investigates the properties of risk measure, value at risk (VaR) and conditional VaR (CVaR), using high-frequency Bitcoin data. These data allow us to conduct a high statistical analysis. Our findings reveal a disparity in VaR and CVaR values between the left and right tails of the return probability distributions. We refer to this disparity as “long–short asymmetry”. In the high-frequency domain, the tail distribution can be accurately described by a power-law function. Moreover, the ratio of CVaR to VaR is expected to be determined solely by the power-law exponent. Through empirical analysis, we confirm that this ratio property holds true for high confidence levels. Furthermore, we investigate the relationship between risk measures (VaR and CVaR) and realized volatility. We observe that they trace a trajectory in a two-dimensional plane. This trajectory changes gradually, indicating periods of both high and low risk." @default.
• W4386367825 created "2023-09-02" @default.
• W4386367825 creator A5071144499 @default.
• W4386367825 date "2023-09-01" @default.
• W4386367825 modified "2023-09-28" @default.
• W4386367825 title "Properties of VaR and CVaR Risk Measures in High-Frequency Domain: Long–Short Asymmetry and Significance of the Power-Law Tail" @default.
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• W4386367825 doi "https://doi.org/10.3390/jrfm16090391" @default.
• W4386367825 hasPublicationYear "2023" @default.
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