Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 715

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Open access
Balcilar, Mehmet, Bouri, Elie, Gupta, Rangan, Roubaud, David·2017·Economic Modelling
DOI 10.1016/j.econmod.2017.03.019
Open this paper in CoChat — free account

Abstract

Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.econmod.2017.03.019

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 47

  1. Balcilar, 2016, Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test, Open Econ. Rev., 27, 229, 10.1007/s11079-016-9388-x · DOI
  2. Balcilar, 2016, Terror attacks and stock market fluctuations: evidence based on a nonparametric causality-in-quantiles test for the G7 countries, Eur. J. Financ., 10.1080/1351847X.2016.1239586 · DOI
  3. Balduzzi, 2001, Economic news and bond prices: evidence from the U.S. Treasury market, J. Financ. Quant. Anal., 36, 523, 10.2307/2676223 · DOI
  4. Bampinas, 2015, On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing, Stud. Nonlinear Dyn. Econ., 19, 657
  5. Bekiros, 2016, Incorporating economic policy uncertainty in US equity premium models: a nonlinear predictability analysis, Financ. Res. Lett., 18, 291, 10.1016/j.frl.2016.01.012 · DOI
  6. Bouoiyour, 2015, What Bitcoin looks like?, Ann. Econ. Financ., 16, 449
  7. Bouoiyour, 2015, Is Bitcoin business income or speculative foolery? New ideas through an improved frequency domain analysis, Ann. Financ. Econ., 10, 1550002, 10.1142/S2010495215500025 · DOI
  8. Bouri, 2017, On the return-volatility relationship in the Bitcoin market around the price crash of 2013, Econ.: Open-Access Open-Assess. E-J., 11, 1
  9. Bouri, 2017, On the hedge and safe haven properties of Bitcoin: is it really more than a diversifier?, Financ. Res. Lett., 20, 192, 10.1016/j.frl.2016.09.025 · DOI · Paper page
  10. Brandvold, 2015, Price discovery on Bitcoin exchanges, J. Int. Financ. Mark. Inst. Money, 36, 18, 10.1016/j.intfin.2015.02.010 · DOI · Paper page
  11. Brock, 1996, A test for independence based on the correlation dimension, Econ. Rev., 15, 197, 10.1080/07474939608800353 · DOI
  12. Cheah, 2015, Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin, Econ. Lett., 130, 32, 10.1016/j.econlet.2015.02.029 · DOI
  13. Chen, 2001, The dynamic relation between stock returns, trading volume and volatility, Financ. Rev., 36, 153, 10.1111/j.1540-6288.2001.tb00024.x · DOI
  14. Chen, 2016, Evidence of stock returns and abnormal trading volume: a threshold quantile regression approach, Jpn. Econ. Rev., 67, 96, 10.1111/jere.12074 · DOI
  15. Chiang, 2012, Stock returns and risk: evidence from quantile regression analysis, J. Risk Financ. Manag., 5, 20, 10.3390/jrfm5010020 · DOI
  16. Chiarella, 2016, The return-volatility relation in commodity futures markets, J. Futur. Mark., 36, 127, 10.1002/fut.21717 · DOI
  17. Chuang, 2009, Causality in quantiles and dynamic stock return-volume relations, J. Bank. Financ., 33, 1351, 10.1016/j.jbankfin.2009.02.013 · DOI
  18. Ciaian, 2016, The economics of BitCoin price formation, ‎Appl. Econ., 48, 1799, 10.1080/00036846.2015.1109038 · DOI · Paper page
  19. Clark, 1973, A subordinated stochastic process model with finite variance for speculative prices, Economics, 41, 135, 10.2307/1913889 · DOI · Paper page
  20. Copeland, 1976, A model of asset trading under the assumption of sequential information arrival, J. Financ., 31, 1149, 10.2307/2326280 · DOI · Paper page
  21. Diks, 2005, A note on the Hiemstra-Jones test for Granger non-causality, Stud. Nonlinear Dyn. Econ., 9, 1
  22. Dwyer, 2015, The economics of Bitcoin and similar private digital currencies, J. Financ. Stab., 17, 81, 10.1016/j.jfs.2014.11.006 · DOI
  23. Fry, 2016, Negative bubbles and shocks in cryptocurrency markets, Int. Rev. Financ. Anal., 47, 343, 10.1016/j.irfa.2016.02.008 · DOI
  24. Gallant, 1992, Stock prices and volume, Rev. Financ. Stud., 5, 199, 10.1093/rfs/5.2.199 · DOI · Paper page
  25. Garcia, 2014, The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy, J. R. Soc. Interface, 11, 1, 10.1098/rsif.2014.0623 · DOI · Paper page
  26. Gebka, 2012, The dynamic relation between returns, trading volume, and volatility: lessons from spillovers between Asia and the United States, Bull. Econ. Res., 64, 65, 10.1111/j.1467-8586.2010.00371.x · DOI
  27. Gebka, 2013, Causality between trading volume and returns: evidence from quantile regressions, Int. Rev. Econ. Financ., 27, 144, 10.1016/j.iref.2012.09.009 · DOI
  28. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M.C., Siering, M., 2014. Bitcoin-Asset or Currency? Revealing Users' Hidden Intentions. Revealing Users' Hidden Intentions. ECIS. Available at SSRN: 2425247.
  29. Hanley, B.P., 2013. The False Premises and Promises of Bitcoin. arXiv preprint arXiv:1312.2048. · arXiv
  30. Hayes, 2016, Cryptocurrency value formation: an empirical study leading to a cost of production model for valuing Bitcoin, Telemat. Inform.
  31. Hiemstra, 1994, Testing for linear and nonlinear Granger causality in the stock price-volume relation, J. Financ., 49, 1639
  32. Hill, 2007, Efficient tests of long-run causation in trivariate VAR processes with a rolling window study of the money-income relationship, J. Appl. Econ., 22, 747, 10.1002/jae.925 · DOI
  33. Hurvich, 1989, Regression and time series model selection in small samples, Biometrika, 76, 297, 10.1093/biomet/76.2.297 · DOI · Paper page
  34. Jeong, 2012, A consistent nonparametric test for causality in quantile, Econ. Theor., 28, 861, 10.1017/S0266466611000685 · DOI · Paper page
  35. Karpoff, 1987, The relation between price changes and trading volume: a survey, J. Financ. Quant. Anal., 22, 109, 10.2307/2330874 · DOI · Paper page
  36. Kristoufek, L., 2013. BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship between Phenomena of the Internet Era. Scientific Reports 3, Article number: 3415. http://dx.doi.org/10.1038/srep03415. · DOI
  37. Kristoufek, 2014, What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis, PLoS One, 10, e0123923, 10.1371/journal.pone.0123923 · DOI · Paper page
  38. Li, 2004, Cross-validated local linear nonparametric regression, Stat. Sin., 14, 485
  39. Li, 2016, Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations, Econ. Model., 52, 661, 10.1016/j.econmod.2015.09.037 · DOI
  40. Lin, 2013, Dynamic stock return-volume relation: evidence from emerging Asian markets, Bull. Econ. Res., 65, 178, 10.1111/j.1467-8586.2011.00428.x · DOI
  41. Nishiyama, 2011, A consistent nonparametric Test for nonlinear causality – specification in time series regression, J. Econ., 165, 112, 10.1016/j.jeconom.2011.05.010 · DOI
  42. Polasik, 2015, Price fluctuations and the use of Bitcoin: an empirical inquiry, Int. J. Electron. Commun., 20, 9, 10.1080/10864415.2016.1061413 · DOI · Paper page
  43. Puri, 2008, Asymmetric volume-return relation and concentrated trading in LIFFE futures, Eur. Financ. Manag., 14, 528, 10.1111/j.1468-036X.2007.00396.x · DOI
  44. Racine, 2004, Nonparametric estimation of regression functions with both categorical and continuous data, J. Econ., 119, 99, 10.1016/S0304-4076(03)00157-X · DOI · Paper page
  45. Todorova, 2014, The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range, Econ. Model., 36, 332, 10.1016/j.econmod.2013.10.003 · DOI
  46. Tsai, 2014, Ripple effect in house prices and trading volume in the UK housing market: new viewpoint and evidence, Econ. Model., 40, 68, 10.1016/j.econmod.2014.03.026 · DOI
  47. Yermack, D., 2013. Is Bitcoin A Real Currency? An Economic Appraisal (No. w19747). National Bureau of Economic Research. URL: 〈http://www.nber.org/papers/w19747〉. · DOI

Cited by

See all 715

Metadata

Authors
Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D.
Venue
Economic Modelling
Publisher
Elsevier BV
Published
2017-08
Volume
64
Pages
74-81
ISSN
0264-9993 (ISSN-L)
DOI
10.1016/j.econmod.2017.03.019
OpenAlex
W2601386739
Cited by
715
References
1
FWCI
106.13
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.