Cryptocurrencies have quickly become popular assets, prompting researchers to explore their price predictability and volatility (Bouri et al. 2022). Yet their correlation with traditional stock, commodity, and foreign exchange (forex) markets remains unclear.
Under normal crypto market conditions, cross-quantilogram heatmaps exhibit weak evidence of an asymmetric predictability between ETH, LTC and XRP and BTC under 1-day lags.
Statistical Analysis
GARCH models have revealed that cryptocurrency performances are related and exhibit some degree of fine-grained structure, with Tron (TRX) showing particularly subtle yield distribution characteristics; it seems to complement BTC, ETH and XRP and may even be affected by them.
Correlation network analysis indicates that all cryptocurrencies except USD Tether (USDT) exhibit similar price fluctuations and show strong tendencies for their prices to move in unison. USDT stands out by having relatively stable prices which mirror its US dollar market position.
Dogecoin, MaidSafeCoin, Litecoin, Monero, Ripple and the Euro exchange rates appear to exhibit greater kurtosis than an ideal normal distribution while Bitcoin, Dash and Litecoin demonstrate levels closer to normal distributions.
Time-Series Analysis
The cryptocurrency market is highly unpredictable, with high degrees of interdependence between various cryptocurrencies. This can be explained by their digital nature – depending on technological advancement and individual investor profiles/behavioral finance factors for pricing decisions.
In order to assess the interdependencies among cryptocurrencies, we employ descriptive and GARCH model analyses on an analysis data set comprising nine selected cryptocurrencies that account for over 80% of total market capitalization: Litecoin, Tether, Cardano, Monero Dash IOTA Bitcoin Cash EOS Stellar Lumens TRON and Ethereum Classic are included as possible candidates.
Cross-quantilogram heatmaps (figure 5) demonstrate that BTC is the dominant influencer on returns of other important cryptocurrencies and shows significant negative predictability between ETH, XRP and LTC/BCH returns; additionally there is a positive influence of BTC on XMR returns that decreases with time until its presence disappears altogether during bearish crypto-market conditions.
Cross-Sectional Analysis
Cryptocurrencies and blockchain platforms are two major categories of digital assets, each providing storage for financial transactions; blockchain platforms store not only this type of transaction data but also software code and other forms of data.
Cross-sectional analysis is employed when an analyst wishes to compare two groups of data at one particular point in time, for instance comparing company performance using cross-sectional information. A financial analyst might use cross-sectional data for such comparison.
An effective cryptocurrency cross-sectional analysis typically looks at return rates on currency pairs. To evaluate these returns, the authors of this study used a standard detrended correlation method to measure BTC’s relationship to several Altcoins such as ETH, LTC, XRP, USDT and XLM using their associated return rates as indicators of its association; their results revealed highly correlated returns except for BCH and USDT which showed no association. When these responses are regressed against those of BTC they reveal relative reinforcement effects among them all except BCH which show relative reinforcement effects in competition with its competitor currency BTC.
Regression Analysis
Bitcoin is a digital currency backed by cryptography for security. As it lacks central authority, its transactions and activity records are verified and maintained through an international network of powerful computers known as “miners.” Additionally, it serves as an excellent store of value.
Prices and market caps of cryptocurrencies often depend on various other factors, including circulation levels and demand; assets with low supply but increased demand typically command higher prices and market caps.
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