Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility

Omane-Adjepong, Maurice; Alagidede, Paul; Akosah, Nana Kwame

doi:10.1016/j.physa.2018.09.013

Cited by 55 publications

(19 citation statements)

References 39 publications

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“…The emergence of Bitcoin and other cryptocurrencies in the market has been studied during this decade (Gil-Alana et al, 2020). The literature on cryptocurrencies has covered a wide range of divergent aspects, beginning with the technical and investment characteristics of Bitcoin in relation to risk and returns (Wei, 2018;Tan et al, 2020;Omane-Adjepong et al, 2019;Kosc et al, 2019;Da Gama Silva et al, 2019;Brauneis and Mestel, 2018;Bouri, et al, 2019;Antonakakis et al, 2019;Yi et al, 2018;Koutmos, 2018). There are vast sum of studies focusing on Bitcoin suitability as an asset to diversify the risks of other traditional assets, which is consistent with the purpose of this paper.…”

Section: Literature Reviewsupporting

confidence: 60%

Bitcoin and CEE stock markets: fresh evidence from using the DECO-GARCH model and quantile on quantile regression

Hung

2021

EJMBE

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PurposeThis study examines the inter-linkages between Bitcoin prices and CEE stock markets (Hungary, the Czech Republic, Poland, Romania and Croatia).Design/methodology/approachThe dynamic contemporaneous nexus has been analyzed using both the multivariate DECO-GARCH model proposed by Engle and Kelly (2012) and quantile on quantile (QQ) methodology proposed by Sim and Zhou (2015). Our study is implemented using the daily data spanning from 6 September 2012 to 12 August 2019.FindingsFirst, the findings show that the average return equicorrelation across Bitcoin prices and CEE stock indices are positive, even though it is found to be time-varying over the research period shown. Second, the Bitcoin-CEE stock market association has positive signs for most pairs of quantiles of both variables and represents a rather similar pattern for the cases of Poland, the Czech Republic and Croatia. However, a weaker and primarily negative connectedness is found for Hungary and Romania, respectively. Furthermore, the interconnectedness between the co-movements in the Bitcoin market and stock returns changes significantly across quantiles of both variables within each nation, indicating that the Bitcoin-stock market relationship is dependent on both the cycle of the stock market and the nature of Bitcoin price shocks.Practical implicationsThe evidence documented in this study has significant implications for divergent economic agents, including global investors, risk managers and policymakers, who would benefit from a comprehensive knowledge of the Bitcoin-stock market relationship to build efficient risk-hedging models and to conduct appropriate policy reactions to information spillover effects in different time horizons.Originality/valueThis paper is the first study employing both the multivariate DECO-GARCH model and QQ methodology to shed light on the nexus between Bitcoin prices and the stock markets in CEE countries. The DECO model uses more information to compute dynamic correlations between each pair of returns than standard dynamic conditional correlation (DCC) models, declining the estimation noise of the correlations. Besides, QQ approach allows us to capture some nuanced features of the Bitcoin-stock market relationship and explore the interdependence in its entirely. Therefore, the main contribution of this article to the related literature in this field is significant.研究目的本研究旨在探討比特幣的價格與中東歐股市(匈牙利、捷克共和國、波蘭、羅馬尼亞和克羅地亞) 之相互聯繫.研究設計/方法/理念研究使用恩格爾與凱利(2012)(Engle and Kelly (2012)) 提出的多變量DECO-GARCH模型及Sim 與Zhou(2015)(Sim and Zhou ( 2015)) 研製的分位數-分位數方法來分析動態同期的聯繫。我們的研究使用由2012年9月6日至2019年8月12日期間取得的每日數據來進行.研究結果首先、研究結果顯示、跨比特幣價格與中東歐股價指數的平均回報當量關聯是正相關的，即使在研究期間被發現是隨時間而變化的。第二、比特幣與中東歐股市之聯繫在大多數兩變數分位數對而言出現正相關跡象，而且，這聯繫在波蘭、捷克共和國及克羅地亞而言表現一個頗相似的模式。唯就匈牙利而言、這聯繫則較弱、而羅馬尼亞則主要是負聯繫。研究結果亦顯示: 比特幣市場內的聯動與股票回報間之內在關聯會在每個國家內跨兩個變數的分位數而顯著地改變，這顯示比特幣-股市關係是取決於股市的週期和比特幣價格衝擊的本質.實際的意義本研究所記載的證據、對不同的經濟行為者而言極具意義 (這包括國際投資者、風險管理經理和政策制定者)，因他們會受惠於對比特幣-股市關係的全面認識，他們可建立有效的風險對沖模型、及在不同時間範圍對資訊溢出效應進行適當的政策反應.研究的原創性/價值本文為首個研究使用多變量DECO-GARCH模型和分位數-分位數(QQ)方法、來解釋比特幣價格與中東歐國家之股市的關係。這DECO模型使用比標準動態條件關係模型更多資訊，來計算每對回報間之動態關係，這能減少估測雜訊，而且，QQ方法讓我們可以取得比特幣-股市關係的一些細微特徵及全面地探索其相互依賴性。因此，本文的主要貢獻是在這學術領域內有關的文獻上.

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Section: Literature Reviewsupporting

confidence: 60%

Bitcoin and CEE stock markets: fresh evidence from using the DECO-GARCH model and quantile on quantile regression

Hung

2021

EJMBE

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“…e emergence of cryptocurrencies has warranted several studies. Most studies either concentrate on cryptocurrencies in isolation [52][53][54] or combined with other assets [10,18,50,51]. Prior studies that combine cryptocurrencies with other assets mostly look out for hedge, safe haven, and diversification opportunities [10,15,18,51]; spillover sentiments which are greatly influenced by major market events [50]; and financial contagion of cryptocurrencies [55].…”

Section: Empirical Reviewmentioning

confidence: 99%

“…As a result, pertinent inferences for portfolio diversification, policy decisions, investing risk, and risk management schemes in cryptocurrencies and global equities could be well accentuated. e emergence of cryptocurrencies has warranted several studies [10,18,[50][51][52][53][54][55]. However, the quantity of information that flows between cryptocurrencies and other financial assets are less captured by prior literature.…”

Section: Introductionmentioning

confidence: 99%

Information Flow between Global Equities and Cryptocurrencies: A VMD‐Based Entropy Evaluating Shocks from COVID‐19 Pandemic

2021

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The world has witnessed the adverse impact of the COVID-19 pandemic. Accordingly, it is expected that information transmission between equities and digital assets has been altered due to the hostile impact of the pandemic outbreak on financial markets. As a result, the ensuing perverse risk among markets is presumed to rise during severe uncertainties occasioned by the COVID-19 pandemic. The impetus of this study is to examine the degree of asymmetry and nonlinear directional causality between global equities and cryptocurrencies in the frequency domain. Hence, we employ both the variational mode decomposition (VMD) and the Rényi effective transfer entropy techniques. Analyses of the study are presented for three sample periods; these are the full sample period, the pre-COVID-19 period, and the COVID-19 pandemic period. We gauge a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between global equities and cryptocurrencies for the sample periods. However, the COVID-19 pandemic period appears to be driving the estimates for the full sample period, which indicates a negative flow. Thus, the direction and significance of the information flow between the markets for the full sample correspond to the one observed during the COVID-19 pandemic period. We, consequently, establish a significant directional, dynamical, and scale-dependent information flow between global equities and cryptocurrencies. Notwithstanding, throughout the study samples, we mainly find a negative significant information flow from global equities to cryptocurrencies. We detect that most cryptocurrencies exhibit similar behaviour of information flow to global equities for each of the sample periods. The outcome provides pertinent signals to investors with diverse investment horizons who would want to diversify, hedge, or employ cryptocurrencies as a safe haven for global equities during uncertainties, specifically the COVID-19 pandemic.

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“…Although Bitcoin is the first decentralised digital currency and remains the cryptocurrency market leader, the number of them is still increasing, reaching 2864 cryptocurrencies traded in April 2020 with a market capitalization of $201 billion (www.investing.com). Therefore, research in these markets has increased rapidly in order to gain an understanding of several aspects which are key factors for investors to gauge the risks related to an investment in cryptocurrencies, such as, the dynamics of coin creation, competition and destruction in the cryptocurrency industry (Feder et al, 2018), price volatility (Dyhrberg, 2016;Katsiampa, 2017;Sovbetov, 2018), price clustering (Urquhart, 2017), speculation (Cheah and Fry, 2015 ;Yermack, 2015;Blau, 2017), transaction costs (Kim, 2017), the market efficiency (Urquhart, 2016;Nadarajah and Chu, 2017;Bariviera, 2017;Vidal-Tomás, Ibañez and Farinos, 2018), market returns and volatility (Omane-Adjepong et al, 2019), robustness (Charles and Darné, 2019), and persistence in the cryptocurrencies market (Caporale et al, 2018, Bouri, 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility persistence in cryptocurrency markets under structural breaks

Abakah

Gil-Alana

Madigu

et al. 2020

International Review of Economics & Finance

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Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility

Cited by 55 publications

References 39 publications

Bitcoin and CEE stock markets: fresh evidence from using the DECO-GARCH model and quantile on quantile regression

Bitcoin and CEE stock markets: fresh evidence from using the DECO-GARCH model and quantile on quantile regression

Information Flow between Global Equities and Cryptocurrencies: A VMD‐Based Entropy Evaluating Shocks from COVID‐19 Pandemic

Volatility persistence in cryptocurrency markets under structural breaks

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