Abstract
This study investigates the volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market by employing the rolling window method and examines relationships between the volatility asymmetry and market efficiency. Whilst we find an inverted asymmetry in the volatility of Bitcoin, its magnitude changes over time, and recently, it has become small. This asymmetric pattern of volatility also exists in higher frequency returns. Other measurements, such as kurtosis, skewness, average, serial correlation, and multifractal degree, also change over time. Thus, we argue that properties of the Bitcoin market are mostly time dependent. We examine efficiency-related measures: the Hurst exponent, multifractal degree, and kurtosis. We find that when these measures represent that the market is more efficient, the volatility asymmetry weakens. For the recent Bitcoin market, both efficiency-related measures and the volatility asymmetry prove that the market becomes more efficient.
Highlights
Bitcoin, advocated by Satoshi Nakamoto [1], was launched in 2009 as the first decentralized cryptocurrency
Since in empirical finance the volatility analysis using the generalized autoregressive conditional heteroscedasticity (GARCH)-type models mainly focuses on daily volatility, here we focus on daily returns, i.e. Δt = 1440 − min
We find that various measurements, such as volatility asymmetry, kurtosis, skewness, serial correlation, and multifractality, are time varying
Summary
Bitcoin, advocated by Satoshi Nakamoto [1], was launched in 2009 as the first decentralized cryptocurrency. Whilst many other cryptocurrencies have been created since its launch, and the cryptocurrency market has grown rapidly, Bitcoin remains the dominant cryptocurrency in terms of market capitalization. Bitcoin dominates about 70% of the total capitalization of 10 cryptocurrencies. Various aspects of Bitcoin, including hedging capabilities [2], bubbles [3], liquidity and efficiency [4], Taylor effect [5], structural breaks [6], transaction activity [7], complexity synchronization [8], long memory effects [9], price clustering [10], rough volatility [11] power-law cross-correlation [12], market structure [13] have been investigated
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