Abstract
PurposeIt is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP).Design/methodology/approachThis study applied dynamic covariances from multivariate GARCH(1,1) with Student’s-t-distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies.FindingsDynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naïve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark.Practical implicationsSophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio.Originality/valueTo the best of the author’s knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate-t-distribution and rolling windows method.
Highlights
The popularity of cryptocurrencies has attracted investors to add cryptocurrencies into their portfolios
The black line represents a portfolio without transaction cost, while the red line indicates a portfolio with transaction cost (50 bps)
Note(s): These figures show optimal weights from MVP, most diversified portfolio (MDP), and equal risk contribution (ERC) portfolio based on dynamic conditional correlation (DCC)-GARCH (1,1) and asymmetric DCC (ADCC)-GARCH (1,1) with multivariate Student’-t-distribution and rolling windows method with 1750 days of forecast length
Summary
The popularity of cryptocurrencies has attracted investors to add cryptocurrencies into their portfolios. This research applied dynamic optimization on MVP, ERC and MDP. Some research explored the diversification advantage through multivariate GARCH based on bivariate portfolios (Basher and Sadorsky, 2016; Ahmad et al, 2018; Jalkh et al, 2020; Yousaf and Ali, 2020a, 2020b), while this study applies GARCH estimations on ten risky assets. While the conventional ERC, MVP and MDP optimization do not use time-varying covariances, this study applies dynamic parameters from multivariate GARCH. Following previous literature (Liu, 2019; Schellinger, 2020), this research took into account the cryptocurrency bubbles from mid-2017 until the beginning of 2018 This approach reflects a realistic portrait of the cryptos, and it gives a more informed investment strategy.
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