- Date
- Thursday 24 Oct 2019, 16:00 - 17:00
- Type
- Seminar
- Spoken Language
- English
- Room
- 1-08
- Building
- Polak Building
- Location
- Campus Woudestein
Wolfgang Hãrdle (Humboldt Universitãt zu Berlin)
Cryptocurrencies are becoming an attractive asset class and are the focus of recent quantitative research. The joint dynamics of the cryptocurrency market yields infor-mation on network risk. Utilizing the adaptive LASSO approach, we build a dynamic network of cryptocurrencies and model the latent communities with a dynamic stochas-tic blockmodel. We develop a dynamic covariate-assisted spectral clustering method to uniformly estimate the latent group membership of cryptocurrencies consistently. We show that return inter-predictability and crypto characteristics, including hashing algorithms and proof types, jointly determine the crypto market segmentation. Based on this classification result, it is natural to employ eigenvector centrality to identify a cryptocurrency’s idiosyncratic risk. An asset pricing analysis finds that a cross-sectional portfolio with a higher centrality earns a higher risk premium. Further tests confirm that centrality serves as a risk factor well and delivers valuable information content on cryptocurrency markets.
Co-authors: Li Guo and Yubo Tao
