A novel two-stage approach for cryptocurrency analysis

COVID-19 as Information Transmitter to Global Equity Markets: Evidence from CEEMDAN-Based Transfer Entropy Approach

“…In the spirit of the existing literature, we consider IMF1, IMF2, and IMF3 to represent the short term, IMF4 and IMF5 to capture the medium term, and IMF6 and Residual to denote long-term dynamics, respectively [25,58,78,79]. Yang et al [58] interpret the short term as a period driven by investor sentiments and market microstructure, the medium term as representing the effect of significant events, and the long term as representative of fundamental values. ese demarcations imply that our results are mixed in terms of diversification potentials, being present and absent simultaneously in the short and medium term, as well as at the composite scale.…”

“…However, the tools to decompose economic time series into all orthogonal time-scale components have been lacking until now. Further, tools for dealing with noise that usually dominates financial time series in the short term are currently available [57,58]. A typical example is the CEEMDAN which is the latest of the empirical mode decomposition (EMD) sequel started by Huang [59].…”

A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in Asia‐Pacific region

“…The method of NA‐MEMD is a multivariate expansion algorithm of EMD by using the noise‐assisted analysis method (Yang et al, 2020; Zhang, Xu, et al, 2017b). Considering the mode‐alignment (common frequency scales in the same IMF across different time series), NA‐MEMD can effectively extract the common factors from interrelated multivariate data at similar timescales.…”

“…In the field of tourism demand forecasting, since different exogenous factors drive the tourism time series in different ways at different timescales, thus, some popular decomposition methods (such as SSA, STL, and EMD) are extensively introduced to predict the tourism demand (Hassani et al, 2017; Tang et al, 2020; Zhang et al, 2020). Notably, the univariate decomposition‐based method (e.g., EMD‐based) cannot extract IMFs in accuracy timescales and explore the underlying interactional mechanism among the multivariable (Yang et al, 2020). Correspondingly, considering the major source countries' time series, the NA‐MEMD can efficiently decompose the multiple tourism time series into several components on similar timescales simultaneously.…”

“…Compared with other multiscale analyses (such as Fourier analysis and wavelet decomposition), NA‐MEMD holds the following unique merit for tourism analysis. Particularly, different from traditional data decomposition techniques using fixed bases, NA‐MEMD employs empirical, adaptive bases according to the nature of the studied samples, which is especially suitable for non‐stationary, non‐linear, and complex data, such as financial data (Yang et al, 2020). Given the above virtue, this study incorporates this powerful decomposition method, that is, NA‐MEMD, to explore the multiscale relationship between the tourist destination and the corresponding major tourism inbound markets.…”