On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models

Jong-Min Kim, Seong-Tae Kim and Sangjin Kim
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Jong-Min Kim: Statistics Discipline, University of Minnesota at Morris, Morris, MN 56267, USA
Seong-Tae Kim: Department of Mathematics, North Carolina A&T State University, Greensboro, NC 27411, USA
Sangjin Kim: Department of Management and Information Systems, Dong-A University, Busan 49236, Korea

Mathematics, 2020, vol. 8, issue 11, 1-15

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.

Keywords: cryptocurrency; gold; S& P 500; GARCH; DCC; copula (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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