In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.
Purpose This study examines the inter-linkages between Bitcoin prices and CEE stock markets (Hungary, the Czech Republic, Poland, Romania and Croatia). Design/methodology/approach The 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. Findings First, 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 implications The 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/value This 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.
To protect against risks arising from fluctuations in spot prices and better manage risk, investors might evaluate futures markets. The role of price discovery in the futures markets and the possibility of reducing certain risks increase the importance of researching the relationship between spot and futures prices. This study aims to determine whether there is a relationship between the Bitcoin spot prices and the Bitcoin futures prices. To this end, the relationship between the two markets is analyzed using Johansen Cointegration analysis and Vector Error Correction Model (VECM) using the daily data of the period 02.23.2017 – 08.31.2021. Unit root tests show that each series are not stationary at the level values and that the first differences of the series are stationary. The results of the cointegration analysis show that there is a long-term equilibrium relationship between the bitcoin spot market and the bitcoin futures market, and it is a single cointegration vector. The Granger causality test based on the vector error correction model was used to determine the causality relationship between the series. It has been determined that there is a unidirectional causality relationship from the Bitcoin spot market to the Bitcoin futures market. Bitcoin is a new financial tool that attracts the attention of investors. Investors make transactions on Bitcoin for speculative purposes. Therefore, unlike other investment instruments, spot prices in the bitcoin market affect futures prices.
This paper sets out to explore the nexus between economic policy uncertainty (EPU) and digital currencies. An integrated survey takes place based on eleven primary studies. Furthermore, an econometric analysis is conducted by the threshold ARCH, simple asymmetric ARCH and non-linear ARCH specifications covering the bull and the bear markets as well as the highly volatile period up to the present. Threshold ARCH is found to provide the best fit for estimations. Outcomes reveal that Bitcoin is strongly connected with EPU while Ethereum and Litecoin are not but are strongly linked with Bitcoin performance. Moreover, weak negative effects of the VIX on both cryptocurrencies are detected while oil exerts weak positive impacts on Ethereum. Overall, Ethereum and Litecoin could serve for diversifiers against Bitcoin or hedgers against traditional assets during highly stressed periods with the advantage of not being affected by economic policy uncertainty news.
Syed Jawad Hussain Shahzad, Elie Bouri, Mobeen Ur Rehman, David Roubaud
Abstract We compare the weak/strong hedging abilities of three alternative assets, namely bitcoin, gold and US VIX futures, against the downside movements in BRICS stock market indices. Results from the cross‐quantilogram approach indicate that bitcoin and gold are weak hedges. Analysis from the recursive sampling shows that each of bitcoin, gold and VIX futures has a time‐varying hedging role in some BRICS countries, which has been shaped by the COVID‐19 outbreak. Results from the conditional diversification benefits show appealing roles for the three alternative assets for investors in BRICS stock markets. However, gold appears to have higher and more stable diversification benefits in China, especially during the COVID‐19 outbreak. Conversely, VIX futures offer higher diversification benefits in Brazil, Russia, India and South Africa during the abrupt of the COVID‐19 outbreak.
Yash Wadalkar, Yellamraju V H Sai Tarun, Jaiesh Singhal, Reena Sonkusare
Bitcoin, one of the most famous and high-in- demand cryptocurrencies, is a type of digital asset that is extremely difficult to track and make predictions upon. In addition, Bitcoin price does not correlate with market- movements, therefore, predicting its price action and its locus is an ordeal. In this paper, we have followed a comparative analysis approach, wherein we are using four different models to predict the trend of BTC Time series data. The results justify that the models have achieved accurate forecasting trends. During the period of 16th to 31st December 2020, Bitcoin prices experienced considerably high swings, due to the increased demand for it. In quantitative terms, the prices experienced fluctuations to the tune of 8000 USD. Despite these enormous price changes, we were able to achieve a model, that helped us attain a Mean Absolute Error (MAE) of 153.55 USD and Mean Square Error (MSE) of 43231.80 USD. Conventional Bitcoin price predicting researches follow a single to two model approach. However, for a highly volatile asset like Bitcoin, making long-term predictions and generalizing them based on limited number of models results in low accuracy outputs. This gap has been bridged in our research, we have worked with different models, as well as fragmented the time intervals into smaller portions, post which the prediction was made for only 2 days. Using this approach, we attained results with least error rates. The results obtained clearly show that ARIMA is the best model for predicting the future trends for BTC time series data. It takes into account the different types of decompositions like Regular Trend, Sessional and Residual Trend making the model give the best results.
Abstract This paper adopts the fractional cointegrated vector autoregressive (FCVAR) model to examine high‐frequency price discovery of bitcoin spot and futures prices from December 18, 2017 to July 31, 2020. We find that bitcoin spot and futures prices exhibit long memory properties and they are fractionally cointegrated. The result shows that the bitcoin futures market dominates the price discovery process. Interestingly, during the Covid‐19 pandemic, the bitcoin price discovery leadership has switched to the spot market. Moreover, we find that the bitcoin futures market follows a long‐run contango. The nonfractional CVAR model overestimates the price discovery of the futures market.
After Nakamoto introduced Bitcoin in 2008 as an alternative online payment system, it became an appealing investment vehicle and an attractive area of study for many researchers. Although much research has been done on the valuation of Bitcoin, comparatively little treatment has been spent on Bitcoin’s relationship to established economic indicators, at least in IS research. This study examines Bitcoin across its history using a time-series structural break analysis to differentiate five distinct periods within Bitcoin’s evolution. Data obtained includes the open-high-low-close and volume Bitcoin trading data by the minute from October 15, 2010 until January 1, 2020. Within each period, we examine Bitcoin’s closing price in relation to an established set of economic indicators that encompass economic health, long-term economic stability, monetary policy, and investor sentiment. We find that Bitcoin has matured from a speculative trading mechanism to an independent investment instrument that is responsive to underlying macroeconomic factors.
Purpose: The purpose of this research is to analyze the price movements of bitcoin, which has become a new phenomenon in financial markets since 2009, the first year of its release, and can be defined as virtual money or crypto money, to be seen as a financial investment tool. Design/Methodology: In the study, volatility, return behavior and reliability as a financial investment tool are examined with autoregressive Conditional Variable Variance modeling. In this context, symmetrical and asymmetrical ARCH models were used. Findings: As a result of the analysis; it has been found that it has an asymmetric effect in the first period for the bitcoin return series examined with symmetric and asymmetric ARCH models. In addition, it has been determined that shocks occurring in the bitcoin return series according to the half-life criteria are exposed to the volatility effect for more than 30 days in each period. It has been determined that bitcoin, which is examined by periods, has higher volatility in its first years. Limitations: The volatility of bitcoin, which has become a new phenomenon in financial markets today, can be defined as virtual money or crypto money, has been analyzed. Originality/Value: In fact, there are many virtual currencies or cryptocurrencies traded in the market. However, among many virtual currencies, bitcoin is the most known and the most market volume. Analyzing the price movements of bitcoin, which has started to be seen as a financial investment tool, is of great importance in the framework of reliability. The examination made in this respect constitutes the original value of the research.
Ghulame Rubbaniy, Ali Awais Khalid, Aristeidis Samitas
This study adds to the inconclusive debate on safe-haven properties of cryptocurrencies during Covid-19 by analyzing the use of wavelet coherence framework on the global Covid-19 fear index, cryptocurrency implied volatility index (VCRIX), and cryptocurrency returns. Our findings show that a non-financial market-based proxy of market stress that represents fear of households and retail investors reveals cryptocurrencies as safe-haven assets; however, a financial market-based proxy of market turbulence exposes that cryptocurrencies behave like traditional assets during the times of Covid-19 pandemic. Our findings support that long-term investors can invest in the cryptocurrency market to hedge the risks during Covid-19 pandemic.
Amaç: Bu araştırmanın amacı, Bitcoin ve altcoin kripto para piyasalarında finansal balonların varlığını araştırmaktır. Tasarım/Yöntem: Çalışmada, Bitcoin ve piyasa değeri açısından Bitcoin’den sonra gelen ilk beş kripto para birimine (Ethereum, Litecoin, Chainlink, Ripple ve Cardano) ait veriler kullanılmıştır. Kripto para piyasasında finansal balonların tespitinde GSADF testi kullanılmıştır. Bulgular: Çalışma sonucunda Bitcoin ve altcoinlerde finansal balonlar tespit edilmiştir. Bitcoin, Ethereum, Ripple ve Chainlink için tespit edilen balonlar istatistiksel olarak anlamlı iken Litecoin ve Cardano için tespit edilen finansal balonlar istatistiksel olarak anlamlı değildir. Sınırlılıklar: Çalışmada altcoin kripto para piyasasını temsilen piyasa değeri bakımından ilk beş kripto para birimine ait veriler kullanılmıştır. 2021 yılı başında çeşitli borsalarda işlem gören dört binden fazla altcoin olduğu göz önünde bulundurulduğunda çalışmanın veri setinin çalışmanın kısıtını oluşturduğu söylenebilir. Özgünlük/Değer: Çalışmadan elde edilen bulgular, araştırmacılar, politika yapıcılar, profesyoneller ve yatırımcılar açısından önem arz etmektedir. Çalışmada kullanılan veri setinin güncel olması 2020 yılı sonunda gerçekleşen finansal balonların tespitini olanaklı kılmıştır. Bu nedenle de çalışmanın ilgili literatüre katkı sağlaması beklenmektedir.
The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.
Abstract This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.
T. Babatunde Oluwagbenga, Ojo O. Oluwadare, S. Yaya OlaOluwa
This paper tries to identify which class of GARCH variants best describe the volatility of bitcoin prices. The prices of bitcoin from August 7, 2015 to November 28, 2018 with 1210 daily observations were used. Five classical and four fractional integrated GARCH variants were estimated and based on the obtained AIC and SBIC, the four fractional integrated GARCH models fits better than the classical GARCH models. Results further showed that HYGARCH with Generalized error distribution (GED) tends to be the best fitted model for the bitcoin prices with better forecasting performance based on MAPFE and TI measures.