Heteroskedasticity in Excess Bitcoin Return Data: Google Trend vs. Garch Effects

Open access

Abstract

This paper examines the mixture of distribution properties associated with heteroskedastic excess Bitcoin return data, using the volume of Google search queries as a proxy for the information arrival time, from a monthly data sampling period of June 2010 to May 2019. The results show that the volatility coefficients become highly statistically insignificant when the lagged volume of search queries is included in the conditional variance equation of the GJR-GARCH-M model. This clearly suggests that the volume of search queries is shown to provide significant explanatory power regarding the variance of heteroskedastic excess Bitcoin return, which can be traced from the ARCH process defined in the GJR-GARCH-M specification. A significant negative relationship between the conditional volatility and the volume of search queries indicates that Internet (online) information arrival reduces the risk premium in the Bitcoin market, which may improve market stability.

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  • Baur D.G. Hong K. Lee A.D. 2018 Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets Institutions and Money 54 pp. 177-189.

  • Bollerslev T. Sizova N. Tauchen G. 2011 Volatility in equilibrium: Asymmetries and dynamic dependencies Review of Finance 16(1) pp. 31-80.

  • Bouoiyour J. Selmi R. Tiwari A.K. Olayeni O.R. 2016 What drives Bitcoin price? Economics Bulletin 36(2) pp. 843-850.

  • Brauneis A. Mestel R. 2019 Cryptocurrency-portfolios in a mean-variance framework Finance Research Letters 28 pp. 259-264.

  • Chan W.H. Le M. Wu Y.W. 2019 Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin The Quarterly Review of Economics and Finance 71 pp. 107-113.

  • Clark P.K. 1973 A subordinated stochastic process model with finite variance for speculative prices Econometrica 40(1) pp. 135-155.

  • Engle R.F. Lilien D.M. Robins R.P. 1987 Estimating time varying risk premia in the term structure: The ARCH-M model Econometrica 55(2) pp. 391-407.

  • Garcia D. Tessone C.J. Mavrodiev P. Perony N. 2014 The digital traces of bubbles: Feedback cycles between socio-economic signals in the Bitcoin economy Journal of the Royal Society Interface 11(99) 20140623.

  • Georgoula I. Pournarakis D. Bilanakos C. Sotiropoulos D. N. Giaglis G.M. 2015 Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices 9th Mediterranean Conference on Information Systems MCIS 2015 Samos Greece October 2-5.

  • Glosten L.R. Jagannathan R. Runkle D.E. 1993 On the relation between the expected value and the volatility of the nominal excess return on stocks The Journal of Finance 48(5) pp. 1779-1801.

  • Kjærland F. Khazal A. Krogstad E. Nordstrøm F. Oust A. 2018 An analysis of bitcoin’s price dynamics Journal of Risk and Financial Management 11(4) pp. 2-18.

  • Kristoufek L. 2013 Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era Scientific Reports 3 p. 3415.

  • Kristoufek L. 2015 What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis PLoS ONE 10: e0123923.

  • Kurihara Y. Fukushima A. 2018 How does price of bitcoin volatility change? International Research in Economics and Finance 2(1) pp. 8-14.

  • Lamoureux C.G. Lastrapes W.D. 1990 Heteroskedasticity in stock return data: Volume versus GARCH effects The Journal of Finance 45(1) pp. 221-229.

  • Liu W. 2018 Portfolio diversification across cryptocurrencies Finance Research Letters 29 pp. 200-205.

  • Mandelbrot B.B. 1963 The Variation of Certain Speculative Prices The Journal of Business 36(4) pp. 394-419.

  • Pavel C. Rajcaniova M. Kancs A. 2016 The economics of Bitcoin price formation Applied Economics 48 pp. 1799-815.

  • Roll R. 1988 R2. Journal of Finance 43(3) pp. 541-566.

  • Senarathne C.W. 2019a The leverage effect and information flow interpretation for speculative bitcoin prices: Bitcoin volume vs ARCH effect European Journal of Economic Studies 8(1) pp. 77-84.

  • Senarathne C.W. 2019b The information flow interpretation of margin debt value data: Evidence from the New York Stock Exchange Applied Economics Journal 26(1) pp. 31-46.

  • Senarathne C.W. 2019c The impact of internet information flow regarding ‘innovation’on common stock returns: Volume vs Google search querries Management of Sustainable Development 11(1) pp. 43-49.

  • Senarathne C.W. Jayasinghe P. 2017 Information flow interpretation of heteroskedasticity for capital asset pricing: An expectation-based view of risk Economic Issues 22(1) pp. 1-24.

  • Senarathne C.W. Wei J. 2018 The impact of patent citation information flow regarding economic innovation on common stock returns: Volume vs. patent citations International Journal of Innovation Studies 2(4) pp. 137-152.

  • Shen D. Li X. Zhang W. 2018 Baidu news information flow and return volatility: Evidence for the sequential information arrival hypothesis Economic Modelling 69 pp. 127-133.

  • Shen D. Zhang W. Xiong X. Li X. Zhang Y. 2016 Trading and non-trading period Internet information flow and intraday return volatility Physica A: Statistical Mechanics and its Applications 451 pp. 519-524.

  • Trautman L.J. Dorman T. 2018 Bitcoin as Asset Classhttps://ssrn.com/abstract=3218007 or http://dx.doi.org/10.2139/ssrn.3218007.

  • Urquhart A. 2017 Price clustering in Bitcoin Economics Letters 159 pp. 145-148.

  • Urquhart A. 2018 What causes the attention of Bitcoin? Economics Letters 166 pp. 40-44.

  • Zhang Y. Feng L. Jin X. Shen D. Xiong X. Zhang W. 2014 Internet information arrival and volatility of SME price index Physica A: Statistical Mechanics and Its Applications 399 pp. 70-74.