Diversifying the Meltdown Risk of Cryptocurrencies

Abstract

We study the co-movement of major cryptocurrencies from the view of portfolio management. To do so, we develop two new statistical tools. First, we propose a new measure called the portfolio-conditional correlation defined as the correlation when the portfolio return is below or above a specific level. Second, we develop a new model named the Common Autoregressive Jump Intensity Score-based model (ComARJIS) 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 increases the Sharpe ratio. This strategy indicates 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 economic policy uncertainty and financial market stress in the US.

Keywords: Non-normality, Cryptocurrency, portfolio optimization, Optimal asset allocation, Dynamic Conditional Score Models