Time series analysis of Cryptocurrency returns and volatilities

Malladi, Rama K.; Dheeriya, Prakash L.

doi:10.1007/s12197-020-09526-4

Cited by 41 publications

(23 citation statements)

References 39 publications

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“…The models were evaluated based on several criteria, and the bestfitting models with the best forecasting performance were selected. Recently, Malladi et.al, [7] investigated the relationships between both Bitcoin and Ripple renderings and volatility, the authors used the Autoregressive Mobiles Average Exogenous Input (ARMAX), the Autoregressive Generalized Conditionally Heteroscedastic (GARCH) model, the Vector Autoregression (VAR) model, and Granger causality tests and showed that the Bitcoin crash of 2018 might have been described by these time series approaches. They also discovered that global stock and gold returns do not cause Bitcoin's returns, but Ripple's returns have a direct impact on Bitcoin pricing.…”

Section: A Statistical and Traditional Techniquesmentioning

confidence: 99%

Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification

El-Berawi¹,

Belal²,

Ellatif³

2021

IJACSA

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This paper proposes a deep learning based predictive model for forecasting and classifying the price of cryptocurrency and the direction of its movement. These two tasks are challenging to address since cryptocurrencies prices fluctuate with extremely high volatile behavior. However, it has been proven that cryptocurrency trading market doesn't show a perfect market property, i.e., price is not totally a random walk phenomenon. Based upon this, this study proves that the price value forecast and price movement direction classification is both predictable. A recurrent neural networks based predictive model is built to regress and classify prices. With adaptive dynamic features selection and the use of external dependable factors with a potential degree of predictability, the proposed model achieves unprecedented performance in terms of movement classification. A naïve simulation of a trading scenario is developed and it shows a 69% profitability score a cross a six months trading period for bitcoin.

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Section: A Statistical and Traditional Techniquesmentioning

confidence: 99%

Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification

El-Berawi¹,

Belal²,

Ellatif³

2021

IJACSA

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“…Empirical studies on legal issues surrounding CC include the studies of Böhme et al ( 2015 ), Yermack ( 2015 ), Ju et al ( 2016 ), and Lim et al ( 2016 ). Other studies are related to social media (Mai et al 2018 ; Xie et al 2020 ), investment (Wu and Pandey 2014 ; Brière et al 2015 ; Callen-Naviglia and Alabdan 2016 ), markets (Bhattacharjee 2016 ; Malladi and Dheeriya 2021 ), and currency (Davidson and Block 2015 ; McCallum 2015 ; Carrick 2016 ; Polasik et al 2016 ; Li and Wang 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Some recent studies have found that some of the volatility of CCs can be accounted for by market sentiment and memory (Cheah and Fry 2015 ; Kristoufek 2015 ; Malladi and Dheeriya 2021 ). In these cases, the “memory” of shocks of CC prices is a semi-important determinant of CC prices.…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 effective reproductive rate on cryptocurrency

2022

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The importance of cryptocurrency to the global economy is increasing steadily, which is evidenced by a total market capitalization of over $2.18T as of December 17, 2021, according to coinmarketcap.com (Coin, 2021). Cryptocurrencies are too confusing for laymen and require more investigation. In this study, we analyze the impact that the effective reproductive rate, an epidemiological indicator of the spread of COVID-19, has on both the price and trading volume of eight of the largest digital currencies—Bitcoin, Ethereum, Tether, Ripple, Litecoin, Bitcoin Cash, Cardano, and Binance. We hypothesize that as the rate of spread decreases, the trading price of the digital currency increases. Using Generalized Autoregressive Conditional Heteroskedasticity models, we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region. These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.

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“…Among the recent financial applications, hidden Markov models have been used to understand the behavior of the decentralized, loosely regulated 28 , and highly speculative 29 market of cryptocurrencies. From these studies, Giudici and Abu Hashish (2020) 30 investigated how the prices of Bitcoin 31 switch between "bear," "stable," and "bull" regimes.…”

Section: Introductionmentioning

confidence: 99%

Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices

Kurbucz¹,

Pósfay²,

Jakovác³

2022

Preprint

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The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function. Then, a binary series is generated from the first difference of Bitcoin exchange rate (against the United States Dollar). Finally, the minimum number of parameters describing the linear laws of this series is identified through stepped time windows. Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets inducted by the COVID-19 pandemic (12 March 2020), and before the record-breaking surge in the price of Bitcoin (Q4 2020 -Q1 2021). In addition, the locally high values of this third parameter are often related to short-term price peaks, which suggests price manipulation.

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Time series analysis of Cryptocurrency returns and volatilities

Cited by 41 publications

References 39 publications

Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification

Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification

Impact of COVID-19 effective reproductive rate on cryptocurrency

Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices

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