IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v9y2023i1d10.1186_s40854-023-00464-8.html
   My bibliography  Save this article

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

Author

Listed:
  • Elie Bouri

    (Lebanese American University)

  • Afees A. Salisu

    (Centre for Econometrics and Applied Research
    University of Pretoria)

  • Rangan Gupta

    (University of Pretoria)

Abstract

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.

Suggested Citation

  • Elie Bouri & Afees A. Salisu & Rangan Gupta, 2023. "The predictive power of Bitcoin prices for the realized volatility of US stock sector returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00464-8
    DOI: 10.1186/s40854-023-00464-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-023-00464-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-023-00464-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    2. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 342-375.
    3. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    5. Elsayed, Ahmed H. & Gozgor, Giray & Lau, Chi Keung Marco, 2022. "Risk transmissions between bitcoin and traditional financial assets during the COVID-19 era: The role of global uncertainties," International Review of Financial Analysis, Elsevier, vol. 81(C).
    6. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    7. Aktham Maghyereh & Hussein Abdoh, 2022. "COVID-19 and the volatility interlinkage between bitcoin and financial assets," Empirical Economics, Springer, vol. 63(6), pages 2875-2901, December.
    8. Jiang, Shangrong & Li, Yuze & Lu, Quanying & Wang, Shouyang & Wei, Yunjie, 2022. "Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    9. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    10. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    11. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    12. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    13. Kumar, Ashish & Iqbal, Najaf & Mitra, Subrata Kumar & Kristoufek, Ladislav & Bouri, Elie, 2022. "Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    14. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
    15. Hussain Shahzad, Syed Jawad & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2020. "Safe haven, hedge and diversification for G7 stock markets: Gold versus bitcoin," Economic Modelling, Elsevier, vol. 87(C), pages 212-224.
    16. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    17. Elie Bouri & Rangan Gupta & Christian Pierdzioch & Afees A. Salisu, 2021. "El Nino and Forecastability of Oil-Price Realized Volatility," Working Papers 202105, University of Pretoria, Department of Economics.
    18. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    19. Symitsi, Efthymia & Chalvatzis, Konstantinos J., 2018. "Return, volatility and shock spillovers of Bitcoin with energy and technology companies," Economics Letters, Elsevier, vol. 170(C), pages 127-130.
    20. Abdallah, Abed AL-Nasser & Abdallah, Wissam, 2019. "Does cross-listing in the US improve investment efficiency? Evidence from UK firms," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 215-231.
    21. Shinji Kakinaka & Ken Umeno, 2021. "Exploring asymmetric multifractal cross-correlations of price-volatility and asymmetric volatility dynamics in cryptocurrency markets," Papers 2102.02865, arXiv.org, revised Jun 2021.
    22. Ben Sita, Bernard & Abdallah, Wissam, 2014. "Volatility links between the home and the host market for U.K. dual-listed stocks on U.S. markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 183-199.
    23. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    24. Xu, Fang & Bouri, Elie & Cepni, Oguzhan, 2022. "Blockchain and crypto-exposed US companies and major cryptocurrencies: The role of jumps and co-jumps," Finance Research Letters, Elsevier, vol. 50(C).
    25. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    26. Dimitrios Koutmos & Timothy King & Constantin Zopounidis, 2021. "Hedging uncertainty with cryptocurrencies: Is bitcoin your best bet?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(4), pages 815-837, December.
    27. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    28. Conlon, Thomas & McGee, Richard, 2020. "Safe haven or risky hazard? Bitcoin during the Covid-19 bear market," Finance Research Letters, Elsevier, vol. 35(C).
    29. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    30. Das, Debojyoti & Le Roux, Corlise Liesl & Jana, R.K. & Dutta, Anupam, 2020. "Does Bitcoin hedge crude oil implied volatility and structural shocks? A comparison with gold, commodity and the US Dollar," Finance Research Letters, Elsevier, vol. 36(C).
    31. Lin Peng & Turan G. Bali, 2006. "Is there a risk-return trade-off? Evidence from high-frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1169-1198.
    32. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Modeling energy price dynamics: GARCH versus stochastic volatility," Energy Economics, Elsevier, vol. 54(C), pages 182-189.
    33. Muhammad Abubakr Naeem & Mudassar Hasan & Muhammad Arif & Syed Jawad Hussain Shahzad, 2020. "Can Bitcoin Glitter More Than Gold for Investment Styles?," SAGE Open, , vol. 10(2), pages 21582440209, May.
    34. Min Xu & Xingtong Chen & Gang Kou, 2019. "A systematic review of blockchain," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-14, December.
    35. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    36. Abosedra, Salah & Laopodis, Nikiforos T. & Fakih, Ali, 2021. "Dynamics and asymmetries between consumer sentiment and consumption in pre- and during-COVID-19 time: Evidence from the US," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    37. Thomas C. Chiang & Yuanqing Zhang, 2018. "An Empirical Investigation of Risk-Return Relations in Chinese Equity Markets: Evidence from Aggregate and Sectoral Data," IJFS, MDPI, vol. 6(2), pages 1-22, March.
    38. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    39. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    40. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    41. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    42. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    43. Melisa Ozdamar & Levent Akdeniz & Ahmet Sensoy, 2021. "Lottery-like preferences and the MAX effect in the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    44. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    45. Ghassan Dibeh & Haidar Harmanani, 2012. "A Stochastic Chartist–Fundamentalist Model with Time Delays," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 105-113, August.
    46. Chan, Wing Hong & Le, Minh & Wu, Yan Wendy, 2019. "Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 107-113.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Business applications and state‐level stock market realized volatility: A forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 456-472, March.
    2. Bampinas, Georgios & Panagiotidis, Theodore, 2023. "How would the war and the pandemic affect the stock and cryptocurrency cross-market linkages?," MPRA Paper 117094, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elie Bouri & Afees A. Salisu & Rangan Gupta, 2022. "Bitcoin Prices and the Realized Volatility of US Sectoral Stock Returns," Working Papers 202224, University of Pretoria, Department of Economics.
    2. Rubaiyat Ahsan Bhuiyan & Afzol Husain & Changyong Zhang, 2023. "Diversification evidence of bitcoin and gold from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    3. Raifu, Isiaka Akande & Ogbonna, Ahamuefula E, 2021. "Safe-haven Effectiveness of Cryptocurrency: Evidence from Stock Markets of COVID-19 worst-hit African Countries," MPRA Paper 113139, University Library of Munich, Germany.
    4. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Tran, Vuong Thao, 2018. "Can economic policy uncertainty predict stock returns? Global evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 134-150.
    5. Waqas Hanif & Hee-Un Ko & Linh Pham & Sang Hoon Kang, 2023. "Dynamic connectedness and network in the high moments of cryptocurrency, stock, and commodity markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    6. Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, vol. 75(C).
    7. Salisu, Afees A. & Adediran, Idris & Omoke, Philip C. & Tchankam, Jean Paul, 2023. "Gold and tail risks," Resources Policy, Elsevier, vol. 80(C).
    8. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).
    9. Salisu, Afees A. & Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "Testing the predictability of commodity prices in stock returns of G7 countries: Evidence from a new approach," Resources Policy, Elsevier, vol. 64(C).
    10. Salisu, Afees A. & Swaray, Raymond & Oloko, Tirimisiyu F., 2019. "Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables," Economic Modelling, Elsevier, vol. 76(C), pages 153-171.
    11. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    12. Salisu, Afees & Ogbonna, Ahamuefula & Oloko, Tirimisiyu, 2020. "Pandemics and cryptocurrencies," MPRA Paper 109597, University Library of Munich, Germany.
    13. Afees A. Salisu & Juncal Cunado & Kazeem Isah & Rangan Gupta, 2020. "Oil Price and Exchange Rate Behaviour of the BRICS for Over a Century," Working Papers 202064, University of Pretoria, Department of Economics.
    14. Salisu, Afees A. & Olaniran, Abeeb & Lasisi, Lukman, 2023. "Climate risk and gold," Resources Policy, Elsevier, vol. 82(C).
    15. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    16. Salisu, Afees A. & Adekunle, Wasiu & Alimi, Wasiu A. & Emmanuel, Zachariah, 2019. "Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries," Resources Policy, Elsevier, vol. 62(C), pages 33-56.
    17. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    18. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2022. "Uncertainty and predictability of real housing returns in the United Kingdom: A regional analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1525-1556, November.
    19. Salisu, Afees A. & Isah, Kazeem & Akanni, Lateef O., 2019. "Improving the predictability of stock returns with Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 857-867.
    20. Afees A. Salisu & Rangan Gupta & Christian Pierdzioch, 2021. "Predictability of Tail Risks of Canada and the U.S. Over a Century: The Role of Spillovers and Oil Tail Risks," Working Papers 202127, University of Pretoria, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00464-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.