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The high-frequency impact of macroeconomic news on jumps and co-jumps in the cryptocurrency markets

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Abstract

This study investigates the extent to which intraday price jumps and co-jumps in cryptocurrency markets stem from the release of macroeconomic news from the U.S., Germany and Japan. Using 5-min frequency prices for Bitcoin and Ethereum quoted against the U.S. dollar over the 2016–2019 period, we find that intraday price jumps are three times more frequent in Ethereum than in Bitcoin. More importantly, we show that jumps in Ethereum are more sensitive to macroeconomic news than jumps in Bitcoin, and that U.S. news releases exhibit higher influence on jumps in both cryptocurrencies than German and Japanese news announcements. We also find that co-jumps among Bitcoin and Ethereum are scarce and tend be associated only with a few U.S. news announcements, and in particular those related to the unemployment rate, new home sales, housing starts and Fed Beige Book.

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Notes

  1. See, for instance, Klein et al. (1991), Jensen et al. (1996), Andersen and Bollerslev (1998), Patelis (1997), Bomfim (2003), Green (2004), Erenburg, Kurov, and Lasser (2006), Wongswan (2006), Brandt and Kavajecz (2004), Bernanke and Kuttner (2005), Boyd, Hu, and Jagannathan (2005), Vega (2006), and Pasquariello and Vega (2007), Rigobon and Sack (2008), Love and Payne (2008), Evans and Lyons (2008), Hanousek et al. (2009), Brenner, Pasquariello and Subrahmanyam (2009), Chen and Gau (2010), Nowak et al. (2011), Hautsch, Hess and Veredas (2011), Rosa (2011), Hussain (2011), and Ben Omrane and Savaser (2016, 2017).

  2. Source: https://coinmarketcap.com/all/views/all/ (visited on October 15, 2020).

  3. See Goutte, Guesmi, and Saadi (2019a, 2019b, 2020) for a thorough review of the relevant literature.

  4. There is evidence that Bitcoin and other cryptocurrencies are interdependent, with a stronger relationship in the short-run than in the long-run (Ciaian et al., 2018). Bouri et al. (2019) also find that price bubbles exist for various cryptocurrencies and are contemporaneously related to each other.

  5. \({J}_{t,i}>{S}_{n}{\beta }^{*}+{C}_{n}\) with \({\beta }^{*}\,\text{such}\,\text{that}\,\mathrm{exp}\left({-\mathrm{e}}^{-{\beta }^{*}}\right)=1-{\upalpha }=0.9\) , consequently \({\beta }^{*}=-\mathrm{ln}\left(-\mathrm{ln}\left(0.9\right)\right)=2.25.\) .

  6. Based on Schwarz and Akaike criteria, we choose P = 4.

  7. The literature show that news has short-lived effects on volatility that would reach two hours (Andersen et al. 2003; Bauwens et al., 2005).

References

  • Al-Yahyaee, K. H., Mensi, W., & Yoon, S.-M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228–234.

    Google Scholar 

  • Andersen, T. G., & Bollerslev, T. (1998). Deutsche Mark-Dollar volatility: Intraday activity patterns, macroeconomic announcements, and longer run dependencies. The Journal of Finance, 53(1), 219–265.

    Google Scholar 

  • Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007a). Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility. Review of Economics and Statistics, 89(4), 701–720.

    Google Scholar 

  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2007b). Real-time price discovery in global stock, bond and foreign exchange markets. Journal of International Economics, 73(2), 251–277.

    Google Scholar 

  • Andersen, T. G., Bollerslev, T., & Dobrev, D. (2007c). No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications. Journal of Econometrics, 138(1), 125–180.

    Google Scholar 

  • Anderson, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2003). Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange. American Economic Review, 93(1), 38–62.

    Google Scholar 

  • Andreou, E., & Ghysels, E. (2002). Rolling-sample volatility estimators: Some new theoretical, simulation, and empirical results. Journal of Business & Economic Statistics, 20, 363–376.

    Google Scholar 

  • Ardia, D., Bluteau, K., & Rüede, M. (2019). Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266–271.

    Google Scholar 

  • Ayadi, M. A., Ben Omrane, W., Wang, J., & Welch, R. (2020). Macroeconomic news, public communications, and foreign exchange jumps around U.S. and European financial crises. International Journal of Finance & Economics, 25(2), 197–227.

    Google Scholar 

  • Balduzzi, P., Elton, E. J., & Green, T. C. (2001). Economic News and Bond Prices: Evidence from the U.S. Treasury Market. The Journal of Financial and Quantitative Analysis, 36(4), 523.

    Google Scholar 

  • Barndorff-Nielsen, O. E., & Shephard, N. (2002). Econometric Analysis of Realised Volatility and Its Use in Estimating Stochastic Volatility Models. Journal of the Royal Statistical Society, 64(B), 253–280.

    Google Scholar 

  • Bai, X., Russell, J. R., & Tiao, G. C. (2004). Effects of non-normality and dependence on the precision of variance estimates using high-frequency financial data. Working paper, University of Chicago.

  • Bandi, F. M., & Russell, J. R. (2006). Separating market microstructure noise from volatility. Journal of Financial Economics, 79, 655–692.

    Google Scholar 

  • 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, 177–189.

    Google Scholar 

  • Bauwens, L., Omrane, W. B., & Giot, P. (2005). News announcements, market activity and volatility in the euro/dollar foreign exchange market. Journal of International Money and Finance, 24, 1108–1125.

    Google Scholar 

  • Ben Omrane, W., & Savaşer, T. (2016). The sign switch effect of macroeconomic news in foreign exchange markets. Journal of International Financial Markets, Institutions and Money, 45, 96–114.

    Google Scholar 

  • Ben Omrane, W., & Savaşer, T. (2017). Exchange rate volatility response to macroeconomic news during the global financial crisis. International Review of Financial Analysis, 52, 130–143.

    Google Scholar 

  • Bernanke, B., & Kuttner, K. (2005). What explains the stock market’s reaction to federal reserve policy?”. Journal of Finance, 60, 1221–1257.

    Google Scholar 

  • Bollerslev, T., & Todorov, V. (2011a). Estimation of jump tails. Econometrica, 79(6), 1727–1783.

    Google Scholar 

  • Bollerslev, T., & Todorov, V. (2011b). Tails, Fears, and Risk Premia. The Journal of Finance, 66(6), 2165–2211.

    Google Scholar 

  • Bomfim, A. N. (2003). Pre-announcement effects, news effects, and volatility: Monetary policy and the stock market. Journal of Banking & Finance, 27(1), 133–151.

    Google Scholar 

  • Boudt, K., Croux, C., & Laurent, S. (2011). Robust estimation of intraweek periodicity in volatility and jump detection. Journal of Empirical Finance, 18(2), 353–367.

    Google Scholar 

  • Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216–221.

    Google Scholar 

  • Boyd, J., Hu, J., & Jagannathan, R. (2005). The stock market’s reaction to unemployment news: Why bad news is usually good for stocks? Journal of Finance, 60, 649–672.

    Google Scholar 

  • Brandt, M., & Kavajecz, K. (2004). Price discovery in the U.S. treasury market: The impact of order flow and liquidity on the yield curve. Journal of Finance, 59, 2623–2654.

    Google Scholar 

  • Brenner, M., Pasquariello, P., & Subrahmanyam, M. (2009). On the volatility and co-movement of US financial markets around macroeconomic news announcements. Journal of Financial and Quantitative Analysis, 44, 1265–1289.

    Google Scholar 

  • Carnero, M. A., Peña, D., & Ruiz, E. (2007). Effects of outliers on the identification and estimation of GARCH models. Journal of Time Series Analysis, 28(4), 471–497.

    Google Scholar 

  • Catania, L., Grassi, S., & Ravazzolo, F. (2019). Forecasting cryptocurrencies under model and parameter instability. International Journal of Forecasting, 35(2), 485–501.

    Google Scholar 

  • Chaim, P., & Laurini, M. P. (2018). Volatility and return jumps in bitcoin. Economics Letters, 173, 158–163.

    Google Scholar 

  • Charfeddine, L., & Maouchi, Y. (2019). Are shocks on the returns and volatility of cryptocurrencies really persistent? Finance Research Letters, 28, 423–430.

    Google Scholar 

  • Chatrath, A., Miao, H., Ramchander, S., & Villupuram, S. (2014). Currency jumps, cojumps and the role of macro news. Journal of International Money and Finance, 40, 42–62.

    Google Scholar 

  • Charles, A., & Darné, O. (2005). Outliers and GARCH models in financial data. Economics Letters, 86(3), 347–352.

    Google Scholar 

  • Chen, Y.-L., & Gau, Y.-F. (2010). News announcements and price discovery in foreign exchange spot and futures markets. Journal of Banking & Finance, 34(7), 1628–1636.

    Google Scholar 

  • Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2018). Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173–195.

    Google Scholar 

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

    Google Scholar 

  • Goutte, S., Guesmi, K., & Saadi, S. (2019a). Cryptofinance and Mechanisms of Exchange: The Making of Virtual Currency. Springer International Publishing.

    Google Scholar 

  • Goutte, S., Guesmi, K., & Saadi, S. (2020). Cryptofinance: A New Currency for A New Economy. World Scientific Publishing.

    Google Scholar 

  • Dewachter, H., Erdemlioglu, D., Gnabo, J.-Y., & Lecourt, C. (2014). The intra-day impact of communication on euro-dollar volatility and jumps. Journal of International Money and Finance, 43, 131–154.

    Google Scholar 

  • Duffie, D., Pan, J., & Singleton, K. (2000). Transform Analysis and Asset Pricing for Affine Jump-diffusions. Econometrica, 68(6), 1343–1376.

    Google Scholar 

  • Dungey, M., McKenzie, M., & Smith, L. V. (2009). Empirical evidence on jumps in the term structure of the US Treasury Market. Journal of Empirical Finance, 16(3), 430–445.

    Google Scholar 

  • Dyhrberg, A. H. (2016). Bitcoin, gold, and the dollar – A GARCH volatility analysis. Finance Research Letters, 16, 85–92.

    Google Scholar 

  • Eraker, B., Johannes, M., & Polson, N. (2003). The Impact of Jumps in Volatility and Returns. The Journal of Finance, 58(3), 1269–1300.

    Google Scholar 

  • Erenburg, G., Kurov, A., & Lasser, D. J. (2006). Trading around macroeconomic announcements: Are all traders created equal? Journal of Financial Intermediation, 15(4), 470–493.

    Google Scholar 

  • Evans, K. P. (2011). Intraday jumps and US macroeconomic news announcements. Journal of Banking & Finance, 35(10), 2511–2527.

    Google Scholar 

  • Evans, M. D. D., & Lyons, R. K. (2008). How is macro news transmitted to exchange rates? Journal of Financial Economics, 88(1), 26–50.

    Google Scholar 

  • Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1–9.

    Google Scholar 

  • French, K. R., & Roll, R. (1986). Stock return variances. Journal of Financial Economics, 17(1), 5–26.

    Google Scholar 

  • Fry, J. (2018). Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets? Economics Letters, 171, 225–229.

    Google Scholar 

  • Giudici, P., & Abu-Hashish, I. (2019). What determines bitcoin exchange prices? A network VAR approach. Finance Research Letters, 28, 309–318.

    Google Scholar 

  • Goutte, S., Guesmi, K., & Saadi, S. (2019b). Cryptofinance and Mechanisms of Exchange. Springer.

    Google Scholar 

  • Green, T. C. (2004). Economic News and the Impact of Trading on Bond Prices. The Journal of Finance, 59(3), 1201–1233.

    Google Scholar 

  • Grobys, K., & Sapkota, N. (2019). Cryptocurrencies and momentum. Economics Letters, 180, 6–10.

    Google Scholar 

  • Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis, 63, 431–437.

    Google Scholar 

  • Hanousek, J., Kočenda, E., & Kutan, A. M. (2009). The reaction of asset prices to macroeconomic announcements in new EU markets: Evidence from intraday data. Journal of Financial Stability, 5(2), 199–219.

    Google Scholar 

  • Hansen, P. R., & Lunde, A. (2006). Realized variance and market microstructure noise. Journal of Business & Economic Statistics, 24, 127–161.

    Google Scholar 

  • Hautsch, N., Hess, D., & Veredas, D. (2011). The impact of macroeconomic news on quote adjustments, noise, and informational volatility. Journal of Banking & Finance, 35(10), 2733–2746.

    Google Scholar 

  • Huang, X. (2005). The Relative Contribution of Jumps to Total Price Variance. Journal of Financial Econometrics, 3(4), 456–499.

    Google Scholar 

  • Huang, X., & Tauchen, G. (2018). Macroeconomic news announcements, systemic risk, financial market volatility, and jumps. Journal of Futures Markets, 38(5), 513–534.

    Google Scholar 

  • Hussain, S. M. (2011). Simultaneous monetary policy announcements and international stock markets response: An intraday analysis. Journal of Banking & Finance, 35(3), 752–764.

    Google Scholar 

  • Jensen, G. R., Mercer, J. M., & Johnson, R. R. (1996). Business conditions, monetary policy, and expected security returns. Journal of Financial Economics, 40(2), 213–237.

    Google Scholar 

  • Klein, M., Mizrach, B., & Murphy, R. G. (1991). Managing the Dollar: Has the Plaza Agreement Mattered? Journal of Money, Credit and Banking, 23(4), 742.

    Google Scholar 

  • Lahaye, J., Laurent, S., & Neely, C. J. (2011). Jumps, cojumps and macro announcements. Journal of Applied Econometrics, 26(6), 893–921.

    Google Scholar 

  • Lee, S. S., & Mykland, P. A. (2008). Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics. Review of Financial Studies, 21(6), 2535–2563.

    Google Scholar 

  • Liu, J., Longstaff, F. A., & Pan, J. (2003). Dynamic Asset Allocation with Event Risk. The Journal of Finance, 58(1), 231–259.

    Google Scholar 

  • Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. Journal of Econometrics, 187, 293–311.

    Google Scholar 

  • Love, R., & Payne, R. (2008). Macroeconomic News, Order Flows, and Exchange Rates. Journal of Financial and Quantitative Analysis, 43(2), 467–488.

    Google Scholar 

  • Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6–9.

    Google Scholar 

  • Nowak, S., Andritzky, J., Jobst, A., & Tamirisa, N. (2011). Macroeconomic fundamentals, price discovery, and volatility dynamics in emerging bond markets. Journal of Banking & Finance, 35(10), 2584–2597.

    Google Scholar 

  • Pasquariello, P., & Vega, C. (2007). Informed and strategic order flow in the bond markets. Review of Financial Studies, 20, 1975–2019.

    Google Scholar 

  • Patelis, A. D. (1997). Stock Return Predictability and The Role of Monetary Policy. The Journal of Finance, 52(5), 1951–1972.

    Google Scholar 

  • Philippas, D., Rjiba, H., Guesmi, K., & Goutte, S. (2019). Media attention and Bitcoin prices. Finance Research Letters, 30, 37–43.

    Google Scholar 

  • Piazzesi, M. (2005). Bond Yields and the Federal Reserve. Journal of Political Economy, 113(2), 311–344.

    Google Scholar 

  • Rigobon, R., & Sack, B. (2008). Noisy macroeconomic announcements, monetary policy, and asset prices. In J. Y. Campbell (Ed.), Asset prices and monetary policy (pp. 335–370). University of Chicago Press.

  • Rosa, C. (2011). The high-frequency response of exchange rates to monetary policy actions and statements. Journal of Banking & Finance, 35(2), 478–489.

    Google Scholar 

  • Rousseeuw, P. J., & Leroy, A. M. (1988). A robust scale estimator based on the shortest half. Statistica Neerlandica, 42(2), 103–116.

    Google Scholar 

  • Sun, X., Liu, M., & Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 101084.

    Google Scholar 

  • Tauchen, G., & Zhou, H. (2011). Realized jumps on financial markets and predicting credit spreads. Journal of Econometrics, 160(1), 102–118.

    Google Scholar 

  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82.

    Google Scholar 

  • Vega, C. (2006). Stock price reaction to public and private information. Journal of Financial Economics, 82, 103–133.

    Google Scholar 

  • Wongswan, J. (2006). Transmission of Information across International Equity Markets. Review of Financial Studies, 19(4), 1157–1189.

    Google Scholar 

  • Yermack, D. (2015). Is Bitcoin a Real Currency? An Economic Appraisal. In D. L. K. Chuen (Ed.), Handbook of Digital Currency. Elsevier, pp. 31–43

  • Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica a: Statistical Mechanics and Its Applications, 510, 658–670.

    Google Scholar 

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Acknowledgements

We are grateful to two anonymous reviewers for helpful comments and suggestions. We also benefited from comments offered by Lamia Chourou, Imed Chkir, Sara Ding, Rui Duan, Ligang Zhong, and seminar participants at the Telfer School of Management of the University of Ottawa and Paris School of Business. Samir Saadi gratefully acknowledges financial support from SSHRC Canada. Walid Ben Omrane acknowledge BUAF Explore grant financial support provided by the Office of Research Services at Brock University. We thank Omar Zidan for excellent research assistance. All errors are our own responsibility

Funding

Samir Saadi gratefully acknowledges financial support from SSHRC Canada. Walid Ben Omrane acknowledge BUAF Explore grant financial support from the Office of Research Services at Brock University.

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Authors and Affiliations

  1. Goodman School of Business, Brock University, St. Catharines, ON, Canada

    Walid Ben Omrane

  2. Center of Research for Energy and Climate Change, Paris School of Business, Paris, France

    Khaled Guesmi

  3. Telfer School of Management, University of Ottawa, Ottawa, ON, Canada

    Qi Qianru & Samir Saadi

  4. College of Business Administration, Gulf University for Science & Technology, West Mishref, Kuwait

    Samir Saadi

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  1. Walid Ben Omrane
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Ben Omrane, W., Guesmi, K., Qianru, Q. et al. The high-frequency impact of macroeconomic news on jumps and co-jumps in the cryptocurrency markets. Ann Oper Res 330, 177–209 (2023). https://doi.org/10.1007/s10479-021-04353-0

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