Diversifying Meltdown Risk in Cryptocurrencies

Abstract

Abstract We study comovement among major cryptocurrencies from a portfolio management perspective. To this end, we develop two new statistical tools. First, we propose a new measure called the portfolio-conditional correlation defined as the correlation conditional on the portfolio return being below or above a given threshold. Second, we develop a new multivariate model named the Common Autoregressive Jump Intensity Score-based (ComARJIS) model in which the time-varying intensity of a common jump in cryptocurrency returns is formulated under the Generalized Autoregressive Score (GAS) framework. Our main findings are as follows: First, we find an adverse downside correlation: the downside correlation is higher than the upside correlation. Second, the ComARJIS model successfully shows the correlation asymmetry of cryptocurrencies. Third, and most importantly, a market-timing strategy with the common jump intensity improves the Sharpe ratio. This result suggests that time diversification could be helpful for cryptocurrency investors even if asset diversification is impossible. Fourth, the meltdown risk represented by the common jump intensity is associated with the financial market stress in the U.S.