Abstract

Using a Vector Autoregressive (VAR) model, this paper investigates the relationships of key performance metrics of crypto ecosystem chains through the analysis of results generated by Impulse Response Functions (IRFs) and Variance Decomposition. Granger Causality test was also used to identify any presence of directional influence to determine causal effects. This study finds that certain cryptocurrencies were able to retain value and maintain their position as ecosystem chains, while others such as Avalanche (AVAX) came into question. Furthermore, matured ecosystems have different behavioral properties as compared to newer additions such as Arbitrium (ARB) and Optimism (OP). Both Total Value Locked (TVL) and Bitcoin (BTC) price possesses strong causality onto other variables investigated, notably in legacy ecosystems; Ethereum (ETH), Binance (BNB), Fantom (FTM). Contrary to popular belief, Trading Volume (V) and Circulating Supply (CS) had little causal impact suggesting a lesser role in predictions of other variables.

Keywords: Cryptocurrencies, Bitcoin, Market capitalization, Trading volume, Circulating supply, Total value locked, Vector autoregressive model, Variance decomposition, Impulse response function, Granger causality test, Decentralized finance