Abstract
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price–volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this issue from a viewpoint of fractal analysis, which can cover the nonlinear interactions and the self-similarity properties widely acknowledged in the field of econophysics. The asymmetric cross-correlations between price and volatility for Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC) during the period from June 1, 2016 to December 28, 2020 are investigated using the MF-ADCCA method and quantified via the asymmetric DCCA coefficient. The approaches take into account the nonlinearity and asymmetric multifractal scaling properties, providing new insights in investigating the relationships in a dynamical way. We find that cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH. In contrast, for XRP and LTC, inverted reactions are present where cross-correlations are stronger in uptrend markets.
Highlights
Bitcoin (BTC) was first released in 2009 by a pseudonymous publisher Satoshi Nakamoto [1], who introduced a decentralized and pseudo-anonymous system supported by the block-chain technology
This section explores asymmetric multifractal features of price-volatility cross-correlations and quantifies their coupling levels to clarify the presence of asymmetric volatility effects in cryptocurrency markets
Before we conduct the MF-ADCCA method, we first test the presence of cross-correlations between price changes and volatility changes to confirm that application of detrended fluctuation analysis (DFA)-based methods is appropriate for the analyses
Summary
Bitcoin (BTC) was first released in 2009 by a pseudonymous publisher Satoshi Nakamoto [1], who introduced a decentralized and pseudo-anonymous system supported by the block-chain technology. From other financial assets, the unique system of BTC allows anonymous electric payment to put into practical use while avoiding duplicated transactions. It was not until 2013, when the BTC market underwent two price bubbles within the same year, that BTC began to be recognized by specialists and experts, increasing its awareness to a wider area. The market capitalization is on its rising trend and has surpassed a staggering 58 billion dollars at the end of 2020 Given these idiosyncratic characteristics, modeling the fundamental features of BTC, along with other cryptocurrencies, plays a crucial role in various types of financial analyses
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