Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk

【Author】 Kurosaki, Tetsuo; Kim, Young Shin

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【Abstract】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.

【Keywords】Cryptocurrencies; Foster-Hart risk; GARCH modeling; Multivariate normal tempered stable process; Portfolio optimization; Value at risk

【发表时间】2022 MAR

【收录时间】2022-03-17

【文献类型】期刊

【主题类别】

区块链应用-金融领域-