论文标题
加密货币投资组合优化具有多元正常钢化稳定过程和寄养风险
Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk
论文作者
论文摘要
我们研究了四个主要加密货币的投资组合优化。我们的时间序列模型是具有多元正常恢复稳定（MNT）分布式残差的广义自动回归有条件异方差（GARCH）模型，用于捕获非高斯加密货币返回动力学。基于时间序列模型，我们从寄养风险方面优化了投资组合。这些复杂的技术尚未在加密货币的背景下进行记录。统计测试表明，MNT分布的GARCH模型与竞争GARCH型模型更适合加密货币回报。我们发现，与现行方法相比，寄养优化的优化产生的盈利投资组合具有更高的风险收益平衡。
We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.
