True versus Spurious Long Memory in Cryptocurrencies

Rambaccussing, Dooruj; Mazibas, Murat

doi:10.3390/jrfm13090186

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…On the other hand, to define the LTC status i, it is necessary to use information from the state (i À 9), according to the Markov model of order 9. In the literature, there is divergence regarding the existence of memory in the series of cryptocurrency markets (Rambaccussing and Mazibas 2020). For example, Tiwari et al (2018) and Soylu et al (2020) oppose the existence of long memory in the series of returns.…”

Section: Empirical Results and Analysismentioning

confidence: 99%

Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting

et al. 2022

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With the growing popularity of digital currencies known as cryptocurrencies, there is a need to develop models capable of robustly analyzing and predicting the value of future returns in these markets. In this article, we extract behavior rules to predict the values of future returns in the Bitcoin, Ethereum, Litecoin, and Ripple closing series. We used categorical data in the analyses and Markov chain models from the first to the tenth order to propose a new way of establishing possible future scenarios, in which we analyze the dependence of memory on the dynamics of the process. We used the measurements of accuracy Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation for the choice of the best models. Our findings reveal that cryptocurrencies have long-range memory. Bitcoin, Ethereum, and Ripple exposed seven steps of memory, while Litecoin displayed nine memory steps. From the transitions between states that happened the most, we defined decision rules that assisted in the definition of future returns in the series. Our results can support the decisions of traders, investors, crypto-traders, and policy-makers.

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Section: Empirical Results and Analysismentioning

confidence: 99%

Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting

et al. 2022

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“…The study reports that long memory exists only in Ethereum returns. However, these tests fail to reject the null hypothesis of long memory in most cases across different volatility proxies [ 61 ]. In this study, we address and focus on the existence of long memory in five cryptocurrencies using OHLC volatility models.…”

Section: Discussionmentioning

confidence: 99%

Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data

Sheraz

Dedu

Preda

2022

Entropy

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This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency’s log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson’s, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.

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Modeling Bitcoin price volatility: long memory vs Markov switching

Chkili

2021

Eurasian Econ Rev

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True versus Spurious Long Memory in Cryptocurrencies

Cited by 7 publications

References 29 publications

Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting

Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting

Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data

Modeling Bitcoin price volatility: long memory vs Markov switching

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