PropertyValue
?:abstract
  • This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC) Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500 In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500 We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility
is ?:annotates of
?:creator
?:journal
  • Mathematics
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models
?:type
?:who_covidence_id
  • #896385
?:year
  • 2020

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