Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk
| Autor: | Tetsuo Kurosaki, Youngshin Kim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Cryptocurrency
Heteroscedasticity 050208 finance Computer science Autoregressive conditional heteroskedasticity 05 social sciences Multivariate normal distribution Context (language use) FOS: Economics and business Portfolio Management (q-fin.PM) 0502 economics and business Econometrics Portfolio 050207 economics Portfolio optimization Time series Finance Quantitative Finance - Portfolio Management |
| DOI: | 10.48550/arxiv.2010.08900 |
| Popis: | 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. Comment: 15 pages, 5 tables, 1 figure |
| Databáze: | OpenAIRE |
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