Asymmetric tail dependence modeling, with application to cryptocurrency market data
نویسندگان
چکیده
Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role world e-commerce, but recent turbulence cryptocurrency market 2018 has raised some concerns about their stability and associated risks. For investors it is crucial to uncover dependence relationships between for a more resilient portfolio diversification. Moreover, stochastic behavior both tails important, as long positions are sensitive decrease prices (lower tail), while short increase (upper tail). In order assess risk types, we develop this paper flexible copula model which able distinctively capture asymptotic or independence its lower upper simultaneously. Our proposed parsimonious smoothly bridges (in each tail) extremal classes interior parameter space. Inference performed using full censored likelihood approach, investigate by simulation estimators’ efficiency under three different censoring schemes reduce impact nonextreme observations. We also local approach temporal dynamics among pairs leading cryptocurrencies. here apply our historical closing five cryotocurrencies share large capitalizations. The results show that outperforms alternative models lower-tail level most and, particular, Ethereum become stronger over time, transitioning from regime years, whilst tail been relatively stable overall at weaker level.
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2022
ISSN: ['1941-7330', '1932-6157']
DOI: https://doi.org/10.1214/21-aoas1568