*3.2. Methodology*

In this paper, we employ a rich set of quantitative techniques such as Pearson correlation, VAR Granger causality, SVAR Granger causality, and Copulas with two specific kinds (Gaussian and Student's-t). Importantly, Huynh et al. (2018) indicated that there are contagion risks among cryptocurrencies by using three kinds of Copulas (Normal, Clayton, and Gumbel). This study also indicated that these pairs capture the Clayton Copulas (for the left-tail dependence). However, this study fails to evaluate the extreme values, which is one of the characteristics in financial returns data. Therefore, we further investigation on Gaussian Copulas (normally known as normal) and Student's-t Copulas (for extreme value in tail dependence) (Chen and Fan 2006). Therefore, we focused on the Gaussian and Student's-t Copulas for further investigation. Lastly, this paper also takes a closer look in this market by the Pearson, VAR, and SVAR Granger causality employed by Zhang et al. (2010), Shabri Abd. Majid et al. (2009), Ding (2010), Tudor (2011), Vinh (2014), Su (2017) for an explanation of spillover effects, to understand which coin influences another one. We will present the basic framework of our methodologies to use in the following sections.
