Skip to main content

Volatility in the Cryptocurrency Market

Abstract

How do cryptocurrency prices evolve? Is there any interdependence among cryptocurrency returns and/or volatilities? Are there any return spillovers and volatility spillovers between the cryptocurrency market and other financial markets? To answer these questions, we use GARCH-in-mean models to examine the relationship between volatility and returns of leading cryptocurrencies, to investigate spillovers within the cryptocurrency market, and also from the cryptocurrency market to other financial markets. Overall, we find statistically significant transmission of shocks and volatilities among the leading cryptocurrencies. We also find statistically significant spillover effects from the cryptocurrency market to other financial markets in the United States, as well as in other leading economies (Germany, the United Kingdom, and Japan).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  • Andrews D, Ploberger W (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62:1383–1414

    Article  Google Scholar 

  • Baur DG, Dimpfl T (2018) Asymmetric volatility in cryptocurrencies. Econ Lett 173:148–151

    Article  Google Scholar 

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31:307–327

    Article  Google Scholar 

  • Bollerslev T, Engle R, Wooldridge JM (1988) A capital asset pricing model with time-varying covariances. J Polit Econ 96:116–131

    Article  Google Scholar 

  • Bollerslev T, Wooldridge JM (2007) Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econ Rev 11:143–172

    Article  Google Scholar 

  • Bouri E, Azzi G, Dyhrberg AH (2017) On the return-volatility relationship in the Bitcoin market around the price crash of 2013. Economics - The Open-Access 11:1–16

    Google Scholar 

  • Caporale GM, Plastun A (2017) The day of the week effect in the cryptocurrency market, Discussion Papers of DIW Berlin: 1694. https://reader.elsevier.com/reader/sd/pii/S1544612318304240?token=FD7AB723B30EBE30374B2066721EFC3E7CCF431ED6AE5811A865F8B148444AD5B34CC4A2A0BE60E11DFD2408AFC8E757. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3082117

  • Ciaian P, Rajcaniova M, d’Artis K (2016) The economics of Bitcoin price formation. Appl Econ 19:1799–1815

    Article  Google Scholar 

  • Corbet S, Meegan A, Larkin C, Lucey B, Yarovaya L (2018) Exploring the dynamic relationships between cryptocurrencies and other financial assets. Econ Lett 165:28–34

    Article  Google Scholar 

  • Dickey D, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–72

    Article  Google Scholar 

  • Diebold FX, Nerlove M (1989) The dynamics of exchange rate volatility: a multivariate Latent Factor Arch Model. J Appl Econom 4:1–21

    Article  Google Scholar 

  • Elliott G, Rothenberg TJ, Stock J (1996) Efficient tests for an autoregressive unit root. Econometrica 64:813–36

    Article  Google Scholar 

  • Engle R (2001) GARCH 101: the use of ARCH/GARCH models in applied econometrics. J Econ Perspect 15:157–168

    Article  Google Scholar 

  • Engle R, Kroner KF (1995) Multivariate simultaneous generalized ARCH. Economet Theor 11:122–150

    Article  Google Scholar 

  • Engle R (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation. Econometrica 50:987–1007

    Article  Google Scholar 

  • Engle R, Ito T, Lin W.-L. (1990) Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica 58:525–542

    Article  Google Scholar 

  • Fry J, Cheah E.-T. (2016) Negative bubbles and shocks in cryptocurrency markets. Int Rev Financ Anal 47:343–352

    Article  Google Scholar 

  • Gallant A, Hsieh D, Tauchen G (1991) On fitting a recalcitrant series: the pound/dollar exchange rate. Nonparametric and Semiparametric Methods in Econometrics and and Statistics. In: Proceedings of the fifth international symposium in econmic theory and econometrics, p 8

  • Glosten LR, Jagannathan R, Runkle DE (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. J Financ 48:1779–1801

    Article  Google Scholar 

  • Grier KB, Henry OT, Olekalns N, Shields K (2004) The asymmetric effects of uncertainty on inflation and output growth. J Appl Econom 19:551–565

    Article  Google Scholar 

  • Jagannathan R, Wang Y (2007) Lazy investors, discretionary consumption, and the cross-section of stock returns. J Financ 62:1623–1661

    Article  Google Scholar 

  • Kwiatkowski D, Phillips P, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J Econ 54:159–178

    Article  Google Scholar 

  • Nelson CR, Plosser CI (1982) Trends random walks in macroeconmic time series: some evidence and implications. J Monet Econ 10:139–162

    Article  Google Scholar 

  • Pindyck RS, Rotemberg JJ (1993) The comovement of stock prices. Q J Econ 4:1073–1104

    Article  Google Scholar 

  • Schilling L, Uhlig H (2018) Some simple Bitcoin economics. National Bureau of Economic Research Working Paper, 24483

  • Stavroyiannis S (2018) A note on the Nelson-Cao inequality constraints in the GJR- GARCH model: is there a leverage effect? Int J Econ Bus Res 16:442–452

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Apostolos Serletis.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Forthcoming in: Open Economies Review

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Serletis, A. Volatility in the Cryptocurrency Market. Open Econ Rev 30, 779–811 (2019). https://doi.org/10.1007/s11079-019-09547-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11079-019-09547-5

Keywords

  • Cryptocurrency
  • Financial markets
  • Spillover effects
  • GARCH-in-mean model
  • Asymmetric BEKK model
  • Volatility transmission

JEL Classification

  • C32
  • G15
  • G32