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

TL;DR

This paper develops a novel cryptocurrency portfolio optimization method using a multivariate normal tempered stable GARCH model and Foster-Hart risk, demonstrating improved profitability and risk management over existing approaches.

Contribution

It introduces the use of MNTS-GARCH models combined with Foster-Hart risk for cryptocurrency portfolio optimization, a novel approach in this domain.

Findings

MNTS-GARCH model fits cryptocurrency returns better than other GARCH models.

Foster-Hart risk optimization results in more profitable portfolios.

The proposed method improves risk-return balance in cryptocurrency investment.

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.