Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/313285 
Autor:innen: 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Journal of Capital Markets Studies (JCMS) [ISSN:] 2514-4774 [Volume:] 5 [Issue:] 1 [Year:] 2021 [Pages:] 28-48
Verlag: 
Emerald, Bingley
Zusammenfassung: 
Purpose - It 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/approach - This 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 andMDPas comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies. Findings - Dynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the na€ive strategy (1/N) under various estimationwindows, 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 implications - Sophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio. Originality/value - To 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.
Schlagwörter: 
Cryptocurrencies
Minimum variance
Equal risk contribution
Most diversified portfolio
Multivariate GARCH
JEL: 
F30
G11
G15
Persistent Identifier der Erstveröffentlichung: 
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