Zhou, Siwen (2018): Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach.
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Abstract
Bitcoin is a virtual currency scheme that is characterised by a decentralised network and cryptographic transfer verification which has been attracting much public attention due to its technological innovation and its high exchange rate volatility. In this paper, Bitcoin’s exchange rate movement from 2011 to 2018 and its relationship with the global financial markets are explored using an EGARCH framework. The results are as follows. First, fundamentals and Bitcoin-related events play a critical role in the exchange rate formation of Bitcoin. Second, the impact of regulation-related events on Bitcoin indicates that market sentiment is responding to market regulation statements. Third, news coverage is an essential factor in driving the volatility of Bitcoin. Fourth, Bitcoin may be a hedge in times of calm financial markets and a safe haven against uncertain economic policy but is likely to expose to flight-to-quality as global financial uncertainty increases. Lastly, the positive effect of the central bank’s announcements on Bitcoin is marginal enough to rule out the involvement of global expansionary monetary policy in inflating Bitcoin’s exchange rate over the past years, as it may have been the case with traditional asset prices after the great recession.
Item Type: | MPRA Paper |
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Original Title: | Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach |
Language: | English |
Keywords: | Bitcoin, EGARCH, event analysis, Reuters news, VIX, EPU, financial markets |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy F - International Economics > F3 - International Finance > F31 - Foreign Exchange G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates |
Item ID: | 89445 |
Depositing User: | Siwen Zhou |
Date Deposited: | 18 Oct 2018 13:41 |
Last Modified: | 27 Sep 2019 02:24 |
References: | Al-Khazali, O., Elie, B., Roubaud, D. (2018). The impact of positive and negative macroeconomic news surprises: Gold versus bitcoin. Economics Bulletin, 38(1):373–382. Ali, R., Barrdear, J., Clews, R., and Southgate, J. (2014). The economics of digital currencies. Bank of England Quarterly Bulletin, 54(3): 276–286. Allison, P. D. (2002). Missing data: Quantitative applications in the social sciences. British Journal of Mathematical and Statistical Psychology, 55(1): 193–196. Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1): 31–56. Andersen, T. G., Bollerslev, T., Diebold, F. X., and Vega, C. (2007). Micro effects of macro announcements: Real-time price discovery in foreign exchange. Journal of International Economics, 73(2): 251–277. Ané, T. and Geman, H. (2000). Order flow, transaction clock, and normality of asset returns. Journal of Finance, 55(5): 2259–2284. Aouadi, A., Arouri, M., and Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35(C): 674–681. Baek, C. and Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1): 30–34. Baker, S. R., Bloom, N., and Davis, S. J. (2015). Measuring economic policy uncertainty. NBER Working Paper No. 21633, National Bureau of Economic Research. Balcilar, M., Bouri, E., Gupta, R., and Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64:74–81. Balcilar, M., Gupta, R., and Segnon, M. (2016). The role of economic policy uncertainty in predicting U.S. recessions: A mixed-frequency Markov-switching vector autoregressive approach. Discussion Paper No. 2016-14, Kiel Institute for the World Economy. Bank, M., Larch, M., and Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3): 239–264. Bank of Canada (2014). Bank of Canada Review Spring 2014. Bank of England (2015). One Bank Research Agenda. Discussion Paper February 2015. Bariviera, A. F. (2017). The inefficiency of bitcoin revisited: A dynamic approach. Economics Letters, 161:1–4. Bartos, J. et al. (2015). Does Bitcoin follow the hypothesis of efficient market? International Journal of Economic Sciences, 4(2): 10–23. Baur, D. and Glover, K. (2016). The destruction of a safe haven asset? Applied Finance Letters, 1(1):8–15. Baur, D. G. and Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2):217–229. Baur, D. G. and McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking & Finance, 34(8):1886–1898. Berry, T. D. and Howe, K. M. (1994). Public information arrival. Journal of Finance, 49(4): 1331–1346. Bitpay (2017). BitPays Bitcoin Transactions Reach an All-Time High. Retrieved from https://www.ccn. com/bitpays-bitcoin-transactions-reach-time-high/. Last accessed on 16 Jul 2018. Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3): 623–685. Blundell Wignall, A. (2014). The Bitcoin Question: Currency versus Trust-less Transfer Technology. OECD Working Paper on Finance, Insurance and Private Pensions No. 37, OECD Publishing. Böhme, R., Christin, N., Edelman, B., and Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2): 213–238. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3): 307–327. Bouoiyour, J. and Selmi, R. (2015a). Bitcoin price: Is it really that new round of volatility can be on way? MPRA Paper No. 65580, University Library of Munich, Germany. Bouoiyour, J. and Selmi, R. (2015b). What Does Bitcoin Look Like? Annals of Economics and Finance, 16(2): 449–492. Bouoiyour, J., Selmi, R., et al. (2015). Greece withdraws from Euro and runs on Bitcoin; April Fools Prank or Serious Possibility? MPRA Paper No. 65317, University Library of Munich, Germany. Bouri, E., Azzi, G., and Dyhrberg, A. H. (2017a). On the return-volatility relationship in the Bitcoin market around the price crash of 2013. Economics - The Open-Access, Open-Assessment E-Journal, 11:1–16. Bouri, E., Das, M., Gupta, R., and Roubaud, D. (2018a). Spillovers between bitcoin and other assets during bear and bull markets. Working Papers 201812, University of Pretoria, Department of Economics. Bouri, E., Gupta, R., Lau, C. K. M., Roubaud, D., and Wang, S. (2018b). Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles. The Quarterly Review of Economics and Finance. Bouri, E., Gupta, R., Tiwari, A. K., and Roubaud, D. (2017b). Does bitcoin hedge global uncertainty? evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23:87–95. Brière, M., Oosterlinck, K., and Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with Bitcoin. Journal of Asset Management, 16(6): 365–373. Buchholz, M., Delaney, J., Warren, J., and Parker, J. (2012). Bits and Bets, Information, Price Volatility, and Demand for Bitcoin. Economics, 312. Buuren, S. v. and Groothuis-Oudshoorn, K. (2010). mice: Multivariate imputation by chained equations in r. Journal of statistical software, pages 1–68. Capie, F., Mills, T. C., and Wood, G. (2005). Gold as a hedge against the dollar. Journal of International Financial Markets, Institutions and Money, 15(4):343–352. Caporale, G. M., Spagnolo, F., and Spagnolo, N. (2017). Macro news and commodity returns. International Journal of Finance & Economics, 22(1):68–80. Cheah, E.-T. and Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130: 32–36. Cheah, E.-T., Mishra, T., Parhi, M., and Zhang, Z. (2018). Long memory interdependency and inefficiency in Bitcoin markets. Economics Letters, 167:18–25. Choi, H. and Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(S1): 2–9. Ciaian, P., Rajcaniova, M., and Kancs, d. (2016). The economics of BitCoin price formation. Applied Economics, 48(19): 1799–1815. Clark, P. K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41(1): 135–155. Corbet, S., McHugh, G., and Meegan, A. (2017). The influence of central bank monetary policy announcements on cryptocurrency return volatility. Investment Management and Financial Innovations, 14(4):60–72. Corbet, S., Meegan, A., Larkin, C., Lucey, B., and Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165:28–34. Da, Z., Engelberg, J., and Gao, P. (2011). In search of attention. Journal of Finance, 66(5): 1461–1499. Demir, E., Gozgor, G., Lau, C. K. M., and Vigne, S. A. (2018). Does economic policy uncertainty predict the Bitcoin returns? an empirical investigation. Finance Research Letters. Forthcoming. Ding, R. and Hou, W. (2015). Retail investor attention and stock liquidity. Journal of International Financial Markets, Institutions and Money, 37(C): 12–26. Doornik, J. A. and Ooms, M. (2008). Multimodality in GARCH regression models. International Journal of Forecasting, 24(3): 432–448. Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16: 85–92. Dyhrberg, A. H. (2016b). Hedging capabilities of bitcoin. is it the virtual gold? Finance Research Letters, 16:139–144. Easley, D. and O’hara, M. (1992). Time and the process of security price adjustment. Journal of Finance, 47(2): 577–605. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4): 987–1007. European Central Bank (2012). Virtual Currency Schemes. Feng, W., Wang, Y., and Zhang, Z. (2017). Informed trading in the bitcoin market. Finance Research Letters. Funke, M., Shu, C., Cheng, X., and Eraslan, S. (2015). Market Segmentation, Fundamentals or Contagion? Assessing Competing Explanations for CNH-CNY Pricing Differentials. Journal of International Money and Finance, 59(C): 245–262. Gallant, A. R., Rossi, P. E., and Tauchen, G. (1992). Stock prices and volume. Review of Financial Studies, 5(2): 199–242. Garcia, D. and Schweitzer, F. (2015). Social signals and algorithmic trading of bitcoin. Royal Society Open Science, 2(9). Ghalanos, A. (2018). Introduction to the rugarch package.(version 1.4-0). Technical report. Ghosh, D., Levin, E. J., Macmillan, P., and Wright, R. E. (2004). Gold as an inflation hedge? Studies in Economics and Finance, 22(1):1–25. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., and Siering, M. (2014). Bitcoin-Asset or Currency? Revealing Users’ Hidden Intentions. Revealing Users’ Hidden Intentions (April 15, 2014). ECIS. Glouderman, L. (2014). Bitcoin’s Uncertain Future in China. USCC Economic Issue Brief, (4). Goldman Sachs (2014). All about Bitcoin. Top of Mind. Gomez Gonzalez, J. E. and Parra Polania, J. A. (2014). Bitcoin: something seems to be ’fundamentally’ wrong. Working Paper No. 819, Banco de la Republica. Goodhart, C. A., Hall, S. G., Henry, S. B., and Pesaran, B. (1993). News effects in a high-frequency model of the sterling-dollar exchange rate. Journal of Applied Econometrics, 8(1): 1–13. Gronwald, M. (2014). The Economics of Bitcoins-Market Characteristics and Price Jumps. CESifo Working Paper No. 5121, CESifo Group Munich. Härdle, W. K. and Trimborn, S. (2015). CRIX or evaluating blockchain based currencies. Discussion Paper SFB649DP2015-048, Humboldt University, Collaborative Research Center 649. He, D., Habermeier, K. F., Leckow, R. B., Haksar, V., Almeida, Y., Kashima, M., Kyriakos-Saad, N., Oura, H., Saadi Sedik, T., Stetsenko, N., and Verdugo Yepes, C. (2016). Virtual Currencies and Beyond: Initial Considerations. IMF Staff Discussion Notes 16/3, International Monetary Fund. Jones, C. M., Kaul, G., and Lipson, M. L. (1994). Transactions, volume, and volatility. Review of Financial Studies, 7(4): 631–651. Jones, C. M. and Seguin, P. J. (1997). Transaction Costs and Price Volatility: Evidence from Commission Deregulation. American Economic Review, 87(4): 728–37. Joy, M. (2011). Gold and the US dollar: Hedge or haven? Finance Research Letters, 8(3):120–131. Karnizova, L. and Li, J. C. (2014). Economic policy uncertainty, financial markets and probability of U.S. recessions. Economics Letters, 125(2): 261–265. Karpoff, J. M. (1987). The Relation between Price Changes and Trading Volume: A Survey. Journal of Financial and Quantitative Analysis, 22(01): 109–126. Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of garch models. Economics Letters, 158:3–6. Khuntia, S. and Pattanayak, J. (2018). Adaptive market hypothesis and evolving predictability of Bitcoin. Economics Letters, 167:26–28. Koutmos, D. (2018). Bitcoin returns and transaction activity. Economics Letters, 167:81–85. Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific reports, 3(3415). Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one, 10(4): e0123923. Little, R. J. and Rubin, D. B. (1989). The analysis of social science data with missing values. Sociological Methods & Research, 18(2-3): 292–326. Lumsdaine, R. L. and Papell, D. H. (1997). Multiple trend breaks and the unit-root hypothesis. Review of Economics and Statistics, 79(2): 212–218. Luther, W. J. and Olson, J. (2013). Bitcoin is memory. Journal of Prices & Markets, 3(3): 22–33. MacDonell, A. (2014). Popping the Bitcoin bubble: An application of log-periodic power law modeling to digital currency. Working Paper, University of Notre Dame. Mainelli, M. and Milne, A. (2016). The Impact and Potential of Blockchain on the Securities Transaction Lifecycle. SWIFT Institute Working Paper No. 2015-007, SWIFT Institute. Mishkin, F. S. (2016). The economics of money, banking, and financial markets. Pearson Education, Harlow, England, Eleventh global edition. Mitchell, M. L. and Mulherin, J. H. (1994). The impact of public information on the stock market. Journal of Finance, 49(3): 923–950. Mody, A. (2009). From Bear Stearns to Anglo Irish; How Eurozone Sovereign Spreads Related to Financial Sector Vulnerability. IMF Working Papers No. 09/108, International Monetary Fund. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics, 80(2): 355–385. Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R., and Lightfoot, G. (2015). Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20(1): 9–49. Preis, T., Moat, H. S., and Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific reports, 3(1684). Roache, S. K. and Rossi, M. (2010). The effects of economic news on commodity prices. The Quarterly Review of Economics and Finance, 50(3):377–385. Rubin, D. B. (1987). The calculation of posterior distributions by data augmentation: Comment: A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: The sir algorithm. Journal of the American Statistical Association, 82(398): 543–546. Scott, S. L. and Varian, H. R. (2015). Bayesian Variable Selection for Nowcasting Economic Time Series. In Goldfarb, A., Greenstein, S., and Tucker, C., editors, Economic Analysis of the Digital Economy, 119-135. University of Chicago Press. Tauchen, G. E. and Pitts, M. (1983). The price variability-volume relationship on speculative markets. Econometrica, 51(2): 485–505. Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148:80–82. Van Wijk, D. (2013). What can be expected from the BitCoin. Working Paper No. 345986, Erasmus Rotterdam Universiteit. Vidal-Tomás, D. and Ibañez, A. (2018). Semi-strong efficiency of bitcoin. Finance Research Letters. Walther, T., Klein, T., Thu, H. P., et al. (2018). Bitcoin is not the new gold-a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59:105–116. Wang, L. and Liu, Y. (2015). Exploring Miner Evolution in Bitcoin Network. In Passive and Active Measurement, 290-302. Springer International Publishing. Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168:21–24. Yermack, D. (2013). Is Bitcoin a real currency? An economic appraisal. NBER Working Paper No. 19747, National Bureau of Economic Research. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/89445 |