Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce, but the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stabil-ity and associated risks. For investors it is crucial to uncover the dependence relationships between cryptocurrencies for a more resilient portfolio diversi-fication. Moreover, the stochastic behavior in both tails is important, as long positions are sensitive to a decrease in prices (lower tail), while short positions are sensitive to an increase in prices (upper tail). In order to assess both risk types, we develop in this paper a flexible copula model which is able to distinctively capture asymptotic dependence or independence in its lower and upper tails simultaneously. Our proposed model is parsimonious and smoothly bridges (in each tail) both extremal dependence classes in the interior of the parameter space. Inference is performed using a full or censored likelihood approach, and we investigate by simulation the estimators’ efficiency under three different censoring schemes which reduce the impact of nonextreme observations. We also develop a local likelihood approach to capture the temporal dynamics of extremal dependence among pairs of leading cryptocurrencies. We here apply our model to historical closing prices of five leading cryotocurrencies which share large cryptocurrency market capitaliza-tions. The results show that our proposed copula model outperforms alternative copula models and that the lower-tail dependence level between most pairs of leading cryptocurrencies and, in particular, Bitcoin and Ethereum has become stronger over time, smoothly transitioning from an asymptotic independence regime to an asymptotic dependence regime in recent years, whilst the upper tail has been relatively more stable overall at a weaker dependence level.
Bibliographical noteKAUST Repository Item: Exported on 2022-09-14
Acknowledged KAUST grant number(s): OSR-CRG2017-3434, OSR-CRG2020-4394
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. OSR-CRG2017-3434 and OSR-CRG2020-4394. The authors would like to thank the Editor, Associate Editor and anonymous referees for many valuable suggestions that have greatly improved the manuscript. Support from the KAUST Supercomputing Laboratory and access to Shaheen is also gratefully acknowledged.