Factors affecting Cryptocurrency Prices: Evidence from Ethereum

Angela, Olivia; Sun, Yen

doi:10.1109/icimtech50083.2020.9211195

Cited by 13 publications

(9 citation statements)

References 18 publications

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“…A positive shock from the Bitcoin price will increase volatility more on the Ethereum price than a negative shock. Angela & Sun's [23] research shows that Bitcoin significantly affects Ethereum, with a positive coefficient value obtained. Based on the explanation, the hypothesis in this study is: H1: There is a positive relationship between Bitcoin price spillover volatility and Ethereum price…”

Section: Bitcoin Hypothesis With Ethereummentioning

confidence: 97%

EGARCH Model: Volatility Spillover Analysis of Bitcoin Price on Altcoin and S&P 500 Index

Melawati,

Tri Gunarsih

2023

Int. J. Business, Humanit. Educ. Soc. Sci.

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This study aims to analyze the effect of Bitcoin price spillover volatility on Altcoin prices (Ethereum, Tether, Binance Coin) and the price of the S&P 500 Index. The data used is weekly data with a research period from January 2018 to December 2022. The analysis used in this study is the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model. The results show a volatility spillover effect between Bitcoin and Binance Coin with more positive shocks than adverse shocks in Bitcoin price volatility on Binance Coin price. Meanwhile, the spillover volatility between Bitcoin and Ethereum, Tether, and the S&P 500 Index cannot be known because the price data is homoscedastic, so it cannot be continued with EGARCH modelling because the data needs to meet the modelling requirements.

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Section: Bitcoin Hypothesis With Ethereummentioning

confidence: 97%

EGARCH Model: Volatility Spillover Analysis of Bitcoin Price on Altcoin and S&P 500 Index

Melawati,

Tri Gunarsih

2023

Int. J. Business, Humanit. Educ. Soc. Sci.

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“…In this subsection, we will analyze Ethereum prices and relate its downfall with the factors listed in [12], [13]. Although the bull run of January 2018 was stochastic in nature, the prices of Ethereum increased from $0.67 to $1332.62 [9].…”

Section: ) Ethereum Price Analysismentioning

confidence: 99%

“…High volatility in cryptocurrency prices invokes the interest of researchers across the academia as well as industries to predict their prices using the power of machine learning and deep learning algorithms [11]. The volatility of cryptocurrencies depends on factors like volume, mining difficulty, popularity, price of alternate coins, transaction cost, sentiment on the social platform, and the laws of a specific country as well stated in [12], [13]. Due to the involvement of numerous factors, price projection becomes a challenging task for the entire research community.…”

Section: Introductionmentioning

confidence: 99%

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

et al. 2022

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Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days' prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.INDEX TERMS Cryptocurrency, price analysis, volume analysis, deep learning, machine learning, survey, ensemble model, fusion in metrics.

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“…At the same time, the behaviour of Bitcoin on fiat currencies (e.g., USD, Euro) is relative, but the asymmetric relationship with Altcoins is unexplored [23] . The market movement showed that cryptocurrencies are significantly more unpredictably and vary much widespread than fiat currencies [1,24,25] . Since July 2017 crypto market witnessed a boom in ICO (Initial Coin Offerings), and this paradigm decreased the dominance of Bitcoin from 85% to below 45% [26] .…”

Section: Review Of Literaturementioning

confidence: 99%

Followness of Altcoins in the Dominance of Bitcoin: A Phase Analysis

Kulal

2021

macro. manag. & public. polic.

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Due to the transparency, simplicity, and blockchain system, cryptocurrencies gained popularity in the modern world. This led to more use of cryptocurrencies for speculation and investment rather than a medium of exchange. It is crucial to analyse the nature of the crypto market before investing in such currencies. With this intention, the paper tried to know the extent of following (Followness) of altcoins to the bitcoin in the different dominance phases like High Dominance, Low Dominance, and Moderate Dominance. For this purpose, daily closing prices of the Bitcoin and five major altcoins (Ethereum, Litecoin, Namecoin, Doge, and Ripple) are collected for the last five years and analyse the relationship between bitcoin and altcoins. Pearson's correlation coefficient test is used to know the direction of the relationship, and Vector Error Correction Model is used to see the extent of the relation. In general, the empirical result of the study showed cointegration between bitcoin and Altcoin. It also depicted that Altcoin showed a high level of followness in the moderate dominance phase and low followness in the low dominance phase. The study developed a price estimation equation to predict the price of altcoins depending upon the price of bitcoin and its dominance in the crypto market. This paper concludes that the dominance of Bitcoin also has a significant role in the price movement of altcoins.

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Factors affecting Cryptocurrency Prices: Evidence from Ethereum

Cited by 13 publications

References 18 publications

EGARCH Model: Volatility Spillover Analysis of Bitcoin Price on Altcoin and S&P 500 Index

EGARCH Model: Volatility Spillover Analysis of Bitcoin Price on Altcoin and S&P 500 Index

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

Followness of Altcoins in the Dominance of Bitcoin: A Phase Analysis

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