Information Flow between Bitcoin and Other Investment Assets

Abstract

This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic rise–fall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets.

Information Flow between Global Equities and Cryptocurrencies: A VMD-Based Entropy Evaluating Shocks from COVID-19 Pandemic

“…However, since market participants react to information at different times, the multiscales reveal negative information flow, especially in the short-and medium-terms. is is in line with the findings of Jang et al [87] who found an asymmetric flow of information from other investment assets to Bitcoin. For the sake of asset allocation and risk management, the negative information flow between Bitcoin and global equities at various investment horizons brings the findings of Zhang et al [88] to light.…”

“…Specifically, from Figure 3, there are both positive and negative flows from global equities to Bitcoin, and the reverse is also true. Notwithstanding, there are more negative flows than there are positive flows which establish the asymmetric information flow by Jang et al [87]. Also, the patterns of information flow within each scale do not seem to deviate significantly from each other, which establishes the long-run memory in Bitcoin [89].…”

A Stochastic Process for Music: The Example of K-pop Music

“…Subsequently, the size of the rolling window is determined to quantify the subsequence bundles consisting of 𝑺 binary numbers. Each subsequence bundle is converted from a binary sequence to a new decimal number, 𝑿 𝑺 [10][11][12][13][14][15]. Then, the entropy of the random variable 𝑿 𝑺 is derived as…”

Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

“…Based on TE, Bekiros et al [34] investigated the network dynamics in US equity and commodity markets, and Lim et al [35] analyzed the information flow between industrial sectors in credit default swaps and stock markets in the US based on TE from the aspects of intra-structures and inter-structures. Recently, Jang et al [36] studied the causal relationship between Bitcoin, gold, S&P 500 index, and US dollars using TE, and Yue et al [37,38] analyzed information transfers between stock market sectors in China and compared between the US and China stock markets. These prior studies can support our idea to use TE as the measure of causal relationships.…”

“…The schematic representation of transfer entropy is in Figure 3. In this study, we focused on the TE under the following conditions of two lags k = l = 1, which is commonly selected because these settings about lags can be safely assumed on the weak form of the efficient market hypothesis (EMH) and the random walk behavior of stock prices [34,36]. Then, we can express the equation of (1,1)-history TE as follows:…”