Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/269930 
Year of Publication: 
2020
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 8 [Issue:] 1 [Article No.:] 1780838 [Year:] 2020 [Pages:] 1-27
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
Cumulative Prospect Theory (CPT) is rooted in behavioural psychology and has demonstrated to possess sufficient explanatory power for use in actual deci­ sion-making problems. In this study, two distinct asset classes (i.e. assets with extremely lower or higher CPT values) are classified and pre-selected for optimisa­ tion purposes using the differential evolution algorithm. Data on two asset classes namely cryptocurrencies and traditional indices were used in the study. The data were sourced from the Bloomberg database and spans the period August 2016 to March 2018. Probability weighting function with 1- and 2- parameters are used to obtain the CPT values of cryptocurrencies, indices, and mixed assets (i.e. crypto­ currencies and indices). We observe that portfolios consisting of assets of any kind with extremely lower CPT values generally outperform those with higher CPT values. Moreover, portfolios made up of mixed assets generate benefits in terms of improvement of the returns, but it tends also to increase volatility significantly.
Subjects: 
Cryptocurrencies indices
cumulative prospect theory
differential evolution copula
CVaR
portfolio optimisation
JEL: 
C02
G11
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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