In general, we have witnessed so many characteristics of the most popular cryptocurrency, i.e. Bitcoins, from being an anonymous trader to the exponential rise of miners, it is considered a few of those reasons in the strange flickers in the Bitcoin market.
At the same time, many researchers are working on understanding this behaviour by using numerous analytical models to identify the trading volumes based upon how traders interact with traders from different geographical regions. One of the widely referred papers was published by Ying Chen and his team from the National University of Singapore on the 4th of January in 2020. The report aimed at understanding the swings in Bitcoin trading volumes.
The regressive models or network-based models generally analyze the trading patterns, and the critical findings of Ying’s team were higher transaction proceedings were from North America and Europe. It demonstrates the higher number of users are from these mentioned continents. The statistical data states that in 2018, 97% of the Bitcoins are owned by 4% of the bitcoin traded addresses which means around 1000 users hold 40% of the bitcoins. The previous activities related to bitcoins also shows that there are some of the bitcoin owners, who have a strong potential in creating significant differences in the price of crypto-currencies. One of the meaningful exchanges took place in November 2017, when a user transferred 25,000 bitcoins ($ 159 million) for a business. Since then the researchers in this domain are trying to build models that can help in finding or roughly estimating the most connected people in the market of cryptocurrency, though it is difficult to recognize a bitcoin trader physically, therefore, the prime focus is given to the statistic figures based identity by producing the efficient analytical models for data created during the transactions of bitcoins.
Various models have been tried so far to explore and estimate future predictions similar to the other stocks. Some of the extensively used models to carry out research are the VAR model, VCEM model, GARCH models, network-centric models, etc.
Below we’ll be discussing them in brief.
These are economics-based models that are used in multiple ways apart from estimating the market performances.
VAR model: The Value at Risk (VAR) is a measure of stages of the financial risks within a defined period. In general, it can be defined as calculating the probability of loss in the predefined circumstances to limit the risk exposure for the particular firm or organization.
VCEM model: The Vector Error Correction (VCEM) model is generalizing the univariate autoregressive model to the vector autoregressive model. It is used in the robust analysis of the economic structure. VCEM is about connecting or relating the levels and differences, whereas VAR depends upon multiple independent variables with more than one set of equations. Therefore, VCEM is said to be an enhanced method for analysis.
GARCH model: The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is utilized in studying and examining the financial data. Mostly, it is used in estimating the fluctuating returns of stocks, market indices, and bonds. The outcome of this model is used in analyzing the prices and deciding the potential assets, which can result in a higher value of returns shortly. Apart from this, it also beneficial in allocating assets, managing risks, optimizing the decisions, etc.
Network Analysis: The Network Analysis is about creating the connections between the relating variables in a large panel in the form of graphs. In the case of bitcoins, it can be considered as creating networks of the transaction data in framing the structure to estimate the trade volume.
The above-discussed methods analyst to determine the performance-based upon the finance and these same models are improvised to understand the strange behaviour of the bitcoins as per the website like btcrevolution.io, despite knowing the fact that it is difficult to recognize the traders.
Still, via these models, the analyst tries to figure out the pack of bitcoin traders who are anonymously dominating the crypto-currency market can also be termed as the market influencers. The expeditious rise of the crypto-currency is leading towards the development and revolution in conventional methods of financial data analysis along with the advances in technology.