Access the full text.
Sign up today, get DeepDyve free for 14 days.
Andrew Urquhart (2016)
The Inefficiency of BitcoinInformation Systems & Economics eJournal
Sydney Ludvigson, Serena Ng (2009)
A Factor Analysis of Bond Risk PremiaCapital Markets: Asset Pricing & Valuation
T. Huynh, Tobias Burggraf, Mei Wang (2020)
Gold, platinum, and expected Bitcoin returnsJournal of Multinational Financial Management
Econometrica, 37
Michał Polasik, A. Piotrowska, T. Wisniewski, Radoslaw Kotkowski, Geoffrey Lightfoot (2015)
Price Fluctuations and the Use of Bitcoin: An Empirical InquiryInternational Journal of Electronic Commerce, 20
(2018)
Factors affecting bitcoin price in the cryptocurrency market: an empirical study
Ross Phillips, D. Gorse (2018)
Cryptocurrency price drivers: Wavelet coherence analysis revisitedPLoS ONE, 13
Victor Troster (2018)
Testing for Granger-causality in quantilesEconometric Reviews, 37
Yongcai Jiang, Gangjin Wang, Danyan Wen, Xiao-guang Yang (2020)
Business conditions, uncertainty shocks and Bitcoin returnsEvolutionary and Institutional Economics Review
Journal of Economic Dynamics and Control, 2
Yhlas Sovbetov (2018)
Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and MoneroEconomics of Networks eJournal
Hui Xiao, Yiguo Sun (2020)
Forecasting the Returns of Cryptocurrency: A Model Averaging ApproachJournal of Risk and Financial Management
N. Usman, K. Nduka (2022)
Announcement Effect of COVID-19 on CryptocurrenciesAsian Economics Letters
(2021)
Risk aversion and bitcoin returns in extreme quantiles
Maria Bontempi, Michele Frigeri, R. Golinelli, Matteo Squadrani (2021)
EURQ : A New Web Search‐based Uncertainty IndexEconomica
Conghui Chen, Lanlan Liu, Ningru Zhao (2020)
Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19Emerging Markets Finance and Trade, 56
Ifigeneia Georgoula, Demitrios Pournarakis, Christos Bilanakos, Dionisios Sotiropoulos, G. Giaglis (2015)
Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin PricesERN: Management of Technological Innovation & R&D in Developing Economies (Topic)
A. Sakov, P. Bickel (2000)
An Edgeworth expansion for the m out of n bootstrapped medianStatistics & Probability Letters, 49
Econometrica, 64
Elie Bouri, Rangan Gupta, C. Lau, David Roubaud, Shixuan Wang (2018)
Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantilesThe Quarterly Review of Economics and Finance, 69
R. Engle, C. Granger (1987)
Co-integration and error correction: representation, estimation and testingEconometrica, 55
Zheng-Zheng Li, R. Tao, Chiwei Su, O. Lobonț (2019)
Does Bitcoin bubble burst?Quality & Quantity, 53
Review of Financial Studies, 28
S. Johansen (1991)
Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive ModelsEconometrica, 59
Imlak Shaikh (2020)
Policy uncertainty and Bitcoin returnsBorsa Istanbul Review, 20
Theodore Panagiotidis, T. Stengos, Orestis Vravosinos (2018)
On the determinants of bitcoin returns: A LASSO approachFinance Research Letters
Tony Klein, Hien Thu, T. Walther (2018)
Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performanceInternational Review of Financial Analysis
U. Koumba, Calvin Mudzingiri, J. Mba (2020)
Does uncertainty predict cryptocurrency returns? A copula-based approachMacroeconomics and Finance in Emerging Market Economies, 13
A. Mamun, G. Uddin, M. Suleman, S. Kang (2020)
Geopolitical risk, uncertainty and Bitcoin investmentPhysica A-statistical Mechanics and Its Applications, 540
S. Corbet, G. McHugh, Andrew Meegan (2017)
The influence of central bank monetary policy announcements on cryptocurrency return volatilityInvestment management & financial innovations, 14
Zhi Da, Joseph Engelberg, P. Gao (2013)
The Sum of All FEARS: Investor Sentiment and Asset PricesMicroeconomics: General Equilibrium & Disequilibrium eJournal
Hui-Pei Cheng, Kuang-Chieh Yen (2020)
The relationship between the economic policy uncertainty and the cryptocurrency marketFinance Research Letters, 35
Vasilios Plakandaras, Elie Bouri, Rangan Gupta (2020)
Forecasting Bitcoin Returns: Is There a Role for the US–China Trade War?Journal of Risk
Florin Aliu, Ujkan Bajra, Naim Preniqi (2021)
Analysis of diversification benefits for cryptocurrency portfolios before and during the COVID-19 pandemicStudies in Economics and Finance
G. Elliott, T. Rothenberg, J. Stock (1992)
Efficient Tests for an Autoregressive Unit RootEconometrics eJournal
Giray Gozgor, A. Tiwari, Ender Demir, Sagi Akron (2019)
The relationship between Bitcoin returns and trade policy uncertaintyFinance Research Letters
B. Podobnik, H. Stanley (2007)
Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series.Physical review letters, 100 8
M. Naeem, Elie Bouri, Zhe Peng, S. Shahzad, X. Vo (2020)
Asymmetric efficiency of cryptocurrencies during COVID19Physica a, 565
D. Aharon, Mahmoud Qadan (2019)
Bitcoin and the day-of-the-week effectFinance Research Letters, 31
Decision Support Systems, 95
Dimitrios Koutmos (2018)
Bitcoin returns and transaction activityEconomics Letters
A. Tiwari, R. Jana, Debojyoti Das, David Roubaud (2018)
Informational efficiency of Bitcoin—An extensionEconomics Letters, 163
Ender Demir, Giray Gozgor, Chi Lau, S. Vigne (2018)
Does economic policy uncertainty predict the Bitcoin returns? An empirical investigationFinance Research Letters
E. Zivot, D. Andrews (1992)
Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root HypothesisJournal of Business & Economic Statistics, 20
C. Granger (1969)
Investigating causal relations by econometric models and cross-spectral methods
Olivier Kraaijeveld, J. Smedt (2020)
The predictive power of public Twitter sentiment for forecasting cryptocurrency pricesJournal of International Financial Markets, Institutions and Money
Walid Mensi, Yun-Jung Lee, K. Al-Yahyaee, A. Sensoy, Seong‐Min Yoon (2019)
Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysisFinance Research Letters
Francisco Colon, Chaehyun Kim, Hana Kim, Wonjoon Kim (2020)
The effect of political and economic uncertainty on the cryptocurrency marketFinance Research Letters
Harald Kinateder, V. Papavassiliou (2019)
Calendar effects in Bitcoin returns and volatilityFinance Research Letters
L. Kristoufek (2014)
What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence AnalysisPLoS ONE, 10
Khaled Mokni, Elie Bouri, A. Ajmi, X. Vo (2021)
Does Bitcoin Hedge Categorical Economic Uncertainty? A Quantile AnalysisSAGE Open, 11
R. Koenker, Zhijie Xiao (2004)
Unit Root Quantile Autoregression InferenceJournal of the American Statistical Association, 99
S. Hansson (2009)
Measuring UncertaintyStudia Logica, 93
M. Resta, Paolo Pagnottoni, M. Giuli (2020)
Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?Risks
A. Cretarola, Gianna Figá-Talamanca (2019)
Detecting bubbles in Bitcoin price dynamics via market exuberanceAnnals of Operations Research, 299
Elie Bouri, Rangan Gupta, A. Tiwari, David Roubaud (2016)
Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressionsFinance Research Letters, 23
P. Saikkonen (1991)
Asymptotically Efficient Estimation of Cointegration RegressionsEconometric Theory, 7
D. Dickey, W. Fuller (1979)
Distribution of the Estimators for Autoregressive Time Series with a Unit RootJournal of the American Statistical Association, 74
Zhijie Xiao (2009)
Quantile cointegrating regressionJournal of Econometrics, 150
Economics Letters, 148
Elie Bouri, Rangan Gupta (2019)
Predicting Bitcoin returns: Comparing the roles of newspaper- and internet search-based measures of uncertaintyFinance Research Letters
Feng Mai, Qing Bai, Jay Shan, X. Wang, R. Chiang (2016)
The Impacts of Social Media on Bitcoin Performance
A. Baig, Benjamin Blau, Nasim Sabah (2019)
Price clustering and sentiment in bitcoinFinance Research Letters
BitCoin's Roller Coaster: Systemic Risk and Market Sentiment. Unpublished
S. Nakamoto, A. Bitcoin (2008)
A peer-to-peer electronic cash system
Pengfei Wang, Xiao Li, Dehua Shen, Wei Zhang (2020)
How does economic policy uncertainty affect the bitcoin market?Research in International Business and Finance, 53
S. Bartolucci, Giuseppe Destefanis, Marco Ortu, Nicola Uras, M. Marchesi, R. Tonelli (2020)
The Butterfly “Affect”: impact of development practices on cryptocurrency pricesEPJ Data Science, 9
R. Koenker, G. Bassett (2007)
Regression Quantiles
Xin Li, C. Wang (2014)
The Technology and Economic Determinants of Cryptocurrency Exchange Rates: The Case of BitcoinIRPN: Innovation & Finance (Topic)
Julian Geuder, Harald Kinateder, N. Wagner (2019)
Cryptocurrencies as financial bubbles: The case of BitcoinFinance Research Letters, 31
A. Galvao (2009)
Unit root quantile autoregression testing using covariatesJournal of Econometrics, 152
J. Cromwell, M. Hannan, W. Labys, M. Terraza (1994)
Testing for Causality
Faruk Balli, A. Bruin, Md Chowdhury, M. Naeem (2020)
Connectedness of cryptocurrencies and prevailing uncertaintiesApplied Economics Letters, 27
S. Johansen (1996)
Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
P. Ciaian, M. Rajcaniova, d'Artis Kancs (2014)
The economics of BitCoin price formationApplied Economics, 48
American Economic Review, 105
This paper aims to examine the predictive power of the volume of Economic Uncertainty Related Queries and the Macroeconomic Uncertainty Index on the Bitcoin returns.Design/methodology/approachData consists of 118 monthly observations from September 2010 to June 2020. Due to the departure of series from Gaussian distribution and the existence of outliers, the authors use the quantile analysis framework to investigate the persistency of the shocks, the long-run relationships and Granger causality among the variables.FindingsThis research provides several important findings. First, the substantial differences between conventional and quantile test results stress the importance of the method selection. Second, throughout the conditional distribution of the series, stochastic properties of the variables, long-run and the causal relationships between the variables might be significantly different. Third, rich information provided by the quantile framework might help the investors design better investment strategies.Originality/valueThis study differs from the previous research in terms of variable selection and econometric methodology. Therefore, it presents a more comprehensive framework that suggests implications for empirical researchers and Bitcoin investors.
Studies in Economics and Finance – Emerald Publishing
Published: Jan 3, 2023
Keywords: Bitcoin; Macroeconomic uncertainty; Internet search volumes; Economic uncertainty related queries; Quantile estimation; G12; G15; C01; C21; C58
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.