Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Exploring risk Premia in Cryptocurrency Markets: An Analysis of Factors Influencing Returns |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 35 |
| Date | 2023 |
| Zusammenfassung | This research delves into the complex field of expected returns in the cryptocurrency markets, pinpointing four key factors – low risk, momentum, size, and illiquidity – as central driving forces. While comparing cryptocurrency data from two sources on their quality and equality, only low risk and momentum consistently demonstrate a significant impact across the two datasets examined. The relevance and influence of size and illiquidity factors remain, at this stage, a subject for further detailed exploration. One of the standout observations from this study is the disparities in the outcomes derived from two distinct sources of cryptocurrency data. Such differences not only raise concerns over the overall trustworthiness of cryptocurrency data but also question the robustness of associated academic and financial studies. A case in point: while the CoinGecko dataset yielded an r-squared and adjusted r-squared of 9.5% and 4.1%, respectively, the CoinMetrics dataset reported remarkably higher values of 93.9% and 93.4%. This vast discrepancy, combined with identified issues of skewness and kurtosis, requires a deeper examination of potential outliers or extreme values in the datasets. In comparison, existing literature from Liu et al. (2019) and Borri et al. (2022) present similar thematic factors. Notably, variances in data from different crypto data providers introduce a complex layer of challenges, especially when drawing direct comparisons. This thesis, for instance, unveils subtle inconsistencies not only between CoinMetrics and CoinGecko but also when compared with CoinMarketCap. A significant distinction of this study, as compared to previous works, is the timeline it encompasses. While prior studies predominantly capture the phase when cryptocurrencies were soaring, this thesis also investigates periods of sustained downturns, such as the "crypto winter". Additionally, this research highlights the market shift between 2020 and 2023, marked by a surge in coin numbers contrasted with a decline in average market capitalisation. Despite the in-depth analysis presented in this study, the field of cryptocurrency factor investing remains largely nascent, primarily hindered by data limitations. The evolving landscape of this asset class, coupled with its increasing complexity, underscores the need for future research. This study also underscores the importance of considering the underlying technology of cryptocurrencies – a factor largely unexplored in current literature. It's essential to highlight that the strategies discussed within this thesis, especially the long-short approach, may pose practical challenges when applied in real-world settings, mainly due to inherent difficulties associated with shorting cryptocurrencies. |
| Export |
BibTeX
|