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

Tetsuo Kurosaki and Young Shin Kim

Finance Research Letters, 2022, vol. 45, issue C

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 (search for similar items in EconPapers)
JEL-codes: C13 C22 C52 C61 G11 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:45:y:2022:i:c:s1544612321002245

DOI: 10.1016/j.frl.2021.102143

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