A0359
Title: Asymmetric tail dependence modeling, with application to cryptocurrency market data
Authors: Yan Gong - KAUST (Saudi Arabia) [presenting]
Raphael Huser - King Abdullah University of Science and Technology (Saudi Arabia)
Abstract: Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce. Still, the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stability and associated risks. For investors, it is crucial to uncover the dependence relationships between cryptocurrencies for more resilient portfolio diversification. Moreover, the stochastic behavior in both tails is important. In order to assess both risk types, we develop a flexible copula model which can distinctively capture asymptotic dependence or independence in its lower and upper tails. The proposed model is parsimonious and smoothly bridges (in each tail) both extremal dependence classes in the interior of the parameter space. The inference is performed using a full or censored likelihood approach. We also develop a local likelihood approach to capture the temporal dynamics of extremal dependences among five leading cryptocurrencies. The results of Bitcoin and Ethereum show that our proposed copula model outperforms alternative copula models and that the lower tail dependence level 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 more stable. A full picture of the tail dependence structures between all pairs of cryptocurrencies would provide valuable information to investigators for risk mitigation.
