What Is Mature and What Is Still Emerging in the Cryptocurrency Market?

Dróżdż, S.; Kwapień, Jarosław; Wątorek, Marcin

doi:10.3390/e25050772

Cited by 16 publications

(3 citation statements)

References 91 publications

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“…Figure 6 thus illustrates the short-term memory of the series of price increase indicators. Last, the log-log plot is not linear but convex, thus confirming the multifractal feature of the time series of Bitcoin prices, documented in other studies [24]. Precisely, a Hurst exponent of 0.48 seems to lead the dynamic at small time scales, whereas a Hurst exponent with a slightly higher value describes the dynamic at greater scales.…”

Section: Empirical Analysissupporting

confidence: 79%

Maxwell’s Demon and Information Theory in Market Efficiency: A Brillouin’s Perspective

Brouty

Garcin

2022

The 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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Section: Empirical Analysissupporting

confidence: 79%

Maxwell’s Demon and Information Theory in Market Efficiency: A Brillouin’s Perspective

Brouty

Garcin

2022

The 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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“…Refs. [23,24] came to a similar conclusion that cryptocurrencies characteristics are nearly indistinguishable from the forex markets in well-established financial markets.…”

Section: Literature Reviewmentioning

confidence: 77%

A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates

Ndlovu

Chikobvu

2023

Data

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In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.

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“…Cryptocurrency is a form of digital currency that is purchased via a website or smartphone application, and which can be bought and sold much like stocks and bonds (Drożdż et al, 2023). However, unlike traditional investments, cryptocurrency markets are open 24-hours a day and 7-days a week, making them more widely available and accessible.…”

Section: Cryptocurrency Potential Risks and Convergence With Gamblingmentioning

confidence: 99%

Cryptocurrency Trading, Day Trading, and Gambling Behavior: Examining the Moderating Effects of Financially Focused Self-Concept and Gambling Motives

Louderback,

McCullock,

LaPlante

2024

Preprint

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Background and Aims: Cryptocurrency and day trading have grown in popularity over the past decade following the creation of the first cryptocurrency, Bitcoin, in 2009. These activities share important features with gambling, including risking money on an uncertain outcome, a chance of monetary rewards, and the potential to experience harm (e.g., financial or relationship problems). However, little is known about cryptocurrency and day trading engagement in the adult population, including associations with gambling behavior, harm, and psychological factors that might moderate these relationships. Methods: We analyzed cross-sectional data for n = 822 adults from an online panel in the U.S. to examine: (1) the extent to which cryptocurrency trading, day trading, and gambling are associated, (2) relationships between cryptocurrency trading, day trading, and higher risk gambling, and (3) whether financially focused self-concept and four types of gambling motives moderate these relationships. Results: We found moderate to strong positive intercorrelations between cryptocurrency and day trading, and gambling behavior, including engagement and risk. We identified significant moderating effects of financially focused self-concept, and coping motives for gambling, on the relationship between cryptocurrency trading and gambling frequency, and between day trading and gambling frequency. For the models predicting higher risk gambling status, the only significant moderators were financially focused self-concept for the day trading model, and the enhancement motive for the cryptocurrency and day trading models. Conclusions: Our results have important implications for research on gambling-adjacent activities and gambling, as well as on the moderating roles of psychosocial concepts in these relationships.

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What Is Mature and What Is Still Emerging in the Cryptocurrency Market?

Cited by 16 publications

References 91 publications

Maxwell’s Demon and Information Theory in Market Efficiency: A Brillouin’s Perspective

Maxwell’s Demon and Information Theory in Market Efficiency: A Brillouin’s Perspective

A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates

Cryptocurrency Trading, Day Trading, and Gambling Behavior: Examining the Moderating Effects of Financially Focused Self-Concept and Gambling Motives

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