What is the link between the risk profile of a cryptocurrency portfolio and the market provenance of its components

Abstract

In this thesis, we aim to fill the lack of scientific literature that covers the field of cryptocurrency portfolio management by analyzing the link between the risk profile of a cryptocurrency portfolio and the market provenance of its components. In fact, despite the fact that more and more investors consider cryptocurrencies as a speculative tool, the lack of knowledge that covers this topic still procures a lot of uncertainty. In order to do find an answer to our research question, we firstly define the market provenance of a digital-asset by stating that investors can construct their portfolio from two different types of components: cryptocurrencies traded on a decentralized exchange platform and cryptocurrencies traded on a centralized exchange platform. Through the reviewing of existent literature, we make the emphasis of two key features that will constitute the basis of our hypothesis: (1) decentralized exchange platforms are less liquid than centralized exchange platforms (Barbon and Ranaldo, 2021) and (2) there is a negative correlation between liquidity and expected returns in the cryptocurrency market (Zhang and Li, 2020). By linking these two statements with our research question, we take as a main hypothesis that market provenance effects exist and affect the returns and the risk profile of a portfolio. We aim to verify this hypothesis by conducting the risk-return analysis of two portfolios constructed from two different market provenance: a centralized exchange platform (Binance) and a decentralized exchange platform (Uniswap). In order to have a wider range of results, we follow the work of Mazanec (2021) who studies cryptocurrencies and compares three different portfolio strategies. By extracting the historical data from January 2020 to June 2022, we allocate the weights of the components according the Equally-weighted model and two models following Markowitz’s mean-variance framework: the Maximum Sharpe ratio model and the Minimum Volatility model. To carry out the empirical study, we use the software Python following the work of Lewinson (2020). At the end of this thesis, we confirmed by reviewing the results of our analysis that the market provenance of a digital-asset has an impact on the returns and the risk profile of a portfolio. In fact, our calculations showed that the portfolio exclusively constructed from components bought on a decentralized exchange platform has higher returns and a riskier profile than the portfolio exclusively constructed from components bought on a centralized exchange platform. After positioning our results according existent scientific literature, we found out that despite several differences related to the sample period of the data, the outcomes of our study were relevant and aligned with the methodology of Mazanec (2021) we followed.