Measuring the Time Series of High-Frequency Risk Attitude from Volatility Risk Premium: The Case of Emerging Markets

Zhu, Chao; Zhang, Yuwei; Yi, Zhen

doi:10.1080/1540496x.2021.1990752

Cited by 2 publications

(1 citation statement)

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“…While the period of the COVID-19 pandemic saw significant volatility in Bitcoin and Ethereum returns, it did not produce the largest statistical moments (variance, skewness and kurtosis); the Bitcoin bubbles studied by Contreras Valdez [65] recorded even larger values. One can find several high-frequency data studies of different financial assets (stocks, indices and futures contracts) in [12][13][14][15]66,67].…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Premium of Bitcoin and Ethereum during the COVID-19 and Non-COVID-19 Periods: A High-Frequency Approach

Núñez-Mora,

Contreras-Valdez,

Santillán-Salgado

2023

Mathematics

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This paper reports our findings on the return dynamics of Bitcoin and Ethereum using high-frequency data (minute-by-minute observations) from 2015 to 2022 for Bitcoin and from 2016 to 2022 for Ethereum. The main objective of modeling these two series was to obtain a dynamic estimation of risk premium with the intention of characterizing its behavior. To this end, we estimated the Generalized Autoregressive Conditional Heteroskedasticity in Mean with Normal-Inverse Gaussian distribution (GARCH-M-NIG) model for the residuals. We also estimated the other parameters of the model and discussed their evolution over time, including the skewness and kurtosis of the Normal-Inverse Gaussian distribution. Similarly, we determined the parameters that define the evolution of the estimated variance, i.e., the parameters related to the fitted past variance, square error and long-term average value. We found that, despite the market uncertainty during the COVID-19 emergency period (2020 and 2021), the selected cryptocurrencies’ return volatility and kurtosis were even greater for several other subperiods within our sample’s time frame. Our model represents an analytical tool that estimates the risk premium that should be delivered by Bitcoin and Ethereum and is therefore of interest to risk managers, traders and investors.

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