Semi-nonparametric risk assessment with cryptocurrencies

Abstract

This paper establishes a brand-new perspective of analyzing the risk of crypto assets through a semi-nonparametric approach, discussing its theoretical advantages and testing its performance compared to parametric approaches and in terms of backtesting techniques and different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. Our comprehensive analysis for six cryptocurrencies shows that flexible semi-nonparametric approaches outperform risk measures of most crypto assets (particularly Bitcoin) and tend to provide the most conservative risk assessment. Furthermore, we propose the Median Shortfall as a robust-to-outliers and reliable risk measure for cryptocurrencies and discuss on the choice of the appropriate probability levels according to the assumed distribution. The evidence supports that Median Shortfall at 98.31 % and 98.51 % confidence levels as accurate alternatives to Value-at-Risk at 99 % and Expected Shortfall at 97.5 %.