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Article
Publication date: 12 July 2019

Cynthia Miglietti, Zdenka Kubosova and Nicole Skulanova

This paper aims to empirically investigate the volatility of Bitcoin, Litecoin and the Euro.

Abstract

Purpose

This paper aims to empirically investigate the volatility of Bitcoin, Litecoin and the Euro.

Design/methodology/approach

The authors use quantitative methodologies to assess the annualized volatility of two cryptocurrencies and one international fiat currency. The exchange rate of the currencies is monitored on a daily basis using 1,460 observations from January 1, 2014 to December 31, 2017. The models used include the augmented Dickey–Fuller test, Akaike Information Criteria, autocorrelation function and exchange rate changes determining which currency is the most volatile.

Findings

The findings indicate, based on the statistical measures used, including the standard deviation of selected currencies and annualized volatility, that Litecoin is more volatile than Bitcoin and the Euro and that Bitcoin is more volatile than the Euro. This furthers previous research on cryptocurrency volatility.

Originality/value

The paper provides compelling evidence about the volatility of Litecoin and Bitcoin. The volatility of cryptocurrencies is furthered with data that are more current. The findings are important for investors, financial markets and central banks.

Details

Studies in Economics and Finance, vol. 37 no. 2
Type: Research Article
ISSN: 1086-7376

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Article
Publication date: 27 May 2021

Onur Polat and Eylül Kabakçı Günay

The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper…

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193

Abstract

Purpose

The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization.

Design/methodology/approach

In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis.

Findings

Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively).

Research limitations/implications

The study can be extended by including more cryptocurrencies and high-frequency data.

Originality/value

The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

Details

Studies in Economics and Finance, vol. 38 no. 5
Type: Research Article
ISSN: 1086-7376

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Article
Publication date: 4 March 2021

Vaibhav Aggarwal

Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities…

Abstract

Purpose

Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies.

Design/methodology/approach

The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020.

Findings

Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar.

Originality/value

This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

Details

International Journal of Innovation Science, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1757-2223

Keywords

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Article
Publication date: 4 October 2019

A. Can Inci and Rachel Lagasse

This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period…

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6867

Abstract

Purpose

This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios.

Design/methodology/approach

The mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios.

Findings

The authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios.

Originality/value

This is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.

Details

Journal of Capital Markets Studies, vol. 3 no. 2
Type: Research Article
ISSN: 2514-4774

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Article
Publication date: 27 September 2021

Thomas Dimpfl and Dalia Elshiaty

Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the…

Abstract

Purpose

Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market.

Design/methodology/approach

The paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares.

Findings

The authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies.

Research limitations/implications

The authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates.

Originality/value

To date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).

Details

The Journal of Risk Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 19 June 2020

Yaman Omer Erzurumlu, Tunc Oygur and Alper Kirik

Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external…

Abstract

Purpose

Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework.

Design/methodology/approach

The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”.

Findings

Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin.

Originality/value

The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.

Details

Studies in Economics and Finance, vol. 37 no. 3
Type: Research Article
ISSN: 1086-7376

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Article
Publication date: 6 September 2019

Calvin W. H. Cheong

This study aims to examine the properties of four major cryptocurrencies and how they can be used as a simpler alternative mode of hedging foreign exchange (FX) risks as…

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1571

Abstract

Purpose

This study aims to examine the properties of four major cryptocurrencies and how they can be used as a simpler alternative mode of hedging foreign exchange (FX) risks as compared to existing mainstream financial risk management techniques.

Design/methodology/approach

This study uses a combination of visual data representations and the classic Fama and Macbeth (1973) two-pass procedure regressions.

Findings

The findings show that cryptocurrencies can be a more effective hedge against FX risks as compared to other common hedging instruments and/or techniques such as gold or a diversified currency portfolio.

Research limitations/implications

The conclusions were arrived at based only on a small group of cryptocurrency, i.e. Bitcoin, Ethereum, Litecoin and Ripple. Other cryptocurrencies such as Dogecoin or ZCash might exhibit different properties.

Practical implications

Cryptocurrencies can be cost-effective and cost-efficient instruments that provide a solid hedge for investors and/or firms that are exposed to global FX volatility. Its ease of trade and virtually zero barriers to entry makes it an easily accessible alternative hedge instrument as compared to more complex items such as derivatives.

Originality/value

If cryptocurrencies are to be accepted into mainstream usage, a detailed examination of its various uses is necessary. In particular, as they are often touted to be the future of currency, its properties and price behavior relative to other mainstream financial instruments need to be well-understood, not only by finance professionals but also by laypersons.

Details

The Journal of Risk Finance, vol. 20 no. 4
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 21 September 2021

Mahdi Ghaemi Asl, Muhammad Mahdi Rashidi and Seyed Ali Hosseini Ebrahim Abad

The purpose of this study is to investigate the correlation between the price return of leading cryptocurrencies, including Bitcoin, Ethereum, Ripple, Litecoin, Monero…

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86

Abstract

Purpose

The purpose of this study is to investigate the correlation between the price return of leading cryptocurrencies, including Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Peercoin and Dash, and stock return of technology companies' indices that mainly operate on the blockchain platform and provide financial services, including alternative finance, democratized banking, future payments and digital communities.

Design/methodology/approach

This study employs a Bayesian asymmetric dynamic conditional correlation multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (BADCC-MGARCH) model with skewness and heavy tails on daily sample ranging from August 11, 2015, to February 10, 2020, to investigate the dynamic correlation between price return of several cryptocurrencies and stock return of the technology companies' indices that mainly operate on the blockchain platform. Data are collected from multiple sources. For parameter estimation and model comparison, the Markov chain Monte Carlo (MCMC) algorithm is employed. Besides, based on the expected Akaike information criterion (EAIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and weighted Deviance Information Criterion (wDIC), the skewed-multivariate Generalized Error Distribution (mvGED) is selected as an optimal distribution for errors. Finally, some other tests are carried out to check the robustness of the results.

Findings

The study results indicate that blockchain-based technology companies' indices' return and price return of cryptocurrencies are positively correlated for most of the sampling period. Besides, the return price of newly invented and more advanced cryptocurrencies with unique characteristics, including Monero, Ripple, Dash, Stellar and Peercoin, positively correlates with the return of stock indices of blockchain-based technology companies for more than 93% of sampling days. The results are also robust to various sensitivity analyses.

Research limitations/implications

The positive correlation between the price return of cryptocurrencies and the return of stock indices of blockchain-based technology companies can be due to the investors' sentiments toward blockchain technology as both cryptocurrencies and these companies are based on blockchain technology. It could also be due to the applicability of cryptocurrencies for these companies, as the price return of more advanced and capable cryptocurrencies with unique features has a positive correlation with the return of stock indices of blockchain-based technology companies for more days compared to the other cryptocurrencies, like Bitcoin, Litecoin and Ethereum, that may be regarded more as speculative assets.

Practical implications

The study results may show the positive role of cryptocurrencies in improving and developing technology companies that mainly operate on the blockchain platform and provide financial services and vice versa, suggesting that managers and regulators should pay more attention to the usefulness of cryptocurrencies and blockchains. This study also has important risk management and diversification implications for investors and companies investing in cryptocurrencies and these companies' stock. Besides, blockchain-based technology companies can add cryptocurrencies to their portfolio as hedgers or diversifiers based on their strategy.

Originality/value

This is the first study analyzing the connection between leading cryptocurrencies and technology companies that mainly operate on the blockchain platform and provide financial services by employing the Bayesian ssymmetric DCC-MGARCH model. The results also have important implications for investors, companies, regulators and researchers for future studies.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 16 August 2021

Seyram Pearl Kumah and Jones Odei-Mensah

The paper aims to examine the asymmetric response of three major altcoins to shocks in six African fiat currencies in a time-frequency space.

Abstract

Purpose

The paper aims to examine the asymmetric response of three major altcoins to shocks in six African fiat currencies in a time-frequency space.

Design/methodology/approach

Data are for the period 10th August 2015 to 2nd February 2019 at a daily frequency. The authors capture the time and frequency information in the return series of the currencies using the ensemble empirical mode decomposition. The authors implemented quantile regression and quantile-in-quantile regression on the decomposed series to test the response of altcoins to both positive and negative shocks in the fiat currencies across time to see if the altcoins are viable alternatives to African fiat currencies.

Findings

The outcome of the study suggests that altcoins behave differently from African fiat currencies and are viable alternative digital currencies and good hedges for African fiat currencies from the medium-term.

Research limitations/implications

Policymakers in Africa and across the globe can follow this paper to mitigate currency crises by adopting altcoins as alternatives to fiat currencies. Forex traders can also mitigate trade risk by using altcoins to hedge dollar/African fiat currency exchange rate risk.

Originality/value

The research was conducted by the authors and has not been published in any journal.

Details

International Journal of Development Issues, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1446-8956

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Article
Publication date: 11 April 2021

Eray Gemici and Müslüm Polat

This study aims to examine the volatility spillovers between Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH) as they are related to structural breaks.

Abstract

Purpose

This study aims to examine the volatility spillovers between Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH) as they are related to structural breaks.

Design/methodology/approach

This study examines the daily period from August 7, 2015 to July 10, 2018 by conducting causality-in-mean and causality-in-variance tests among cryptocurrencies.

Findings

The findings showed that there was one-way causality-in-mean from BTC to LTC and ETH, but there was no causality-in-mean from LTC and ETH to BTC. On the other hand, considering the structural breaks included in the variance equations, the estimation results showed that there were short-term causality-in-variance from LTC to BTC and long-term causality-in-variance from BTC to LTC.

Originality/value

This study fills the gap by contributing in two ways. First, to the best of the authors’ knowledge, this is the first study that used the cross-correlation function (CCF) of causality to explore causality-in-variance among cryptocurrencies. Second, this study considers the structural breaks in variance in the return series.

Details

Studies in Economics and Finance, vol. 38 no. 4
Type: Research Article
ISSN: 1086-7376

Keywords

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