Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk
نویسندگان
چکیده
We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on model, we optimize in terms Foster-Hart risk. Those sophisticated techniques are not yet documented context cryptocurrency. Statistical tests suggest that MNTS GARCH fits better returns than competing GARCH-type models. find yields more profitable risk-return balance prevailing approach.
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ژورنال
عنوان ژورنال: Finance Research Letters
سال: 2022
ISSN: ['1544-6131', '1544-6123']
DOI: https://doi.org/10.1016/j.frl.2021.102143