IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v88y2023icp1035-1051.html
   My bibliography  Save this article

Frequency domain causality and quantile connectedness between investor sentiment and cryptocurrency returns

Author

Listed:
  • Zhu, Huiming
  • Xing, Zhanming
  • Ren, Yinghua
  • Chen, Yiwen
  • Hau, Liya

Abstract

This study investigates the frequency-domain causality and quantile connectedness between online investors’ fear sentiment and cryptocurrency returns. We propose cross-quantile coherency and networks to examine the frequency-domain nonlinear interdependence. First, we find that investor fear sentiment and cryptocurrency returns exhibit bidirectional causality. Second, fear exhibits an asymmetric connectedness with cryptocurrency returns across quantiles and frequencies. Third, short-term cross-quantile connectedness is found to be more significant than long-term connectedness. These findings can help investors and policymakers make decisions regarding diversified hedging and controlling for potential risks.

Suggested Citation

  • Zhu, Huiming & Xing, Zhanming & Ren, Yinghua & Chen, Yiwen & Hau, Liya, 2023. "Frequency domain causality and quantile connectedness between investor sentiment and cryptocurrency returns," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1035-1051.
  • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:1035-1051
    DOI: 10.1016/j.iref.2023.07.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056023002484
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2023.07.038?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Madhumita Chakraborty & Sowmya Subramaniam, 2020. "Asymmetric relationship of investor sentiment with stock return and volatility: evidence from India," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 12(4), pages 435-454, May.
    2. Tiwari, Aviral Kumar & Trabelsi, Nader & Alqahtani, Faisal & Bachmeier, Lance, 2019. "Modelling systemic risk and dependence structure between the prices of crude oil and exchange rates in BRICS economies: Evidence using quantile coherency and NGCoVaR approaches," Energy Economics, Elsevier, vol. 81(C), pages 1011-1028.
    3. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    4. Stein, Jeremy C, 1996. "Rational Capital Budgeting in an Irrational World," The Journal of Business, University of Chicago Press, vol. 69(4), pages 429-455, October.
    5. Ahn, Yongkil & Kim, Dongyeon, 2021. "Emotional trading in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 42(C).
    6. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    7. Dai, Zhifeng & Zhu, Junxin & Zhang, Xinhua, 2022. "Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment," Energy Economics, Elsevier, vol. 114(C).
    8. Li, Xiao, 2021. "Does Chinese investor sentiment predict Asia-pacific stock markets? Evidence from a nonparametric causality-in-quantiles test," Finance Research Letters, Elsevier, vol. 38(C).
    9. Dimitrios Koutmos, 2023. "Investor sentiment and bitcoin prices," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 1-29, January.
    10. D. O. Olayungbo, 2019. "Effects of Global Oil Price on Exchange Rate, Trade Balance, and Reserves in Nigeria: A Frequency Domain Causality Approach," JRFM, MDPI, vol. 12(1), pages 1-14, March.
    11. Zhang, Jiahang & Zhang, Chi, 2022. "Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter," Research in International Business and Finance, Elsevier, vol. 61(C).
    12. Karamti, Chiraz & Belhassine, Olfa, 2022. "COVID-19 pandemic waves and global financial markets: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 45(C).
    13. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    14. Conghui Chen & Lanlan Liu & Ningru Zhao, 2020. "Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2298-2309, August.
    15. Westerhoff, Frank H., 2004. "Greed, fear and stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 635-642.
    16. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-102, December.
    17. Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2018. "Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 74-87, January.
    18. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    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. Ghaemi Asl, Mahdi & Ben Jabeur, Sami & Hosseini, Seyedeh Sana & Tajmir Riahi, Hamed, 2024. "Fintech's impact on conventional and Islamic sustainable equities: Short- and long-term contributions of the digital financial ecosystem," Global Finance Journal, Elsevier, vol. 62(C).
    2. Binlin Li & Nils Haneklaus & Mohammad Mafizur Rahman, 2024. "Dynamic connectedness and hedging opportunities of the commodity and stock markets in China: evidence from the TVP-VAR and cDCC-FIAPARCH," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
    3. Abakah, Emmanuel Joel Aikins & Brahim, Mariem & Carlotti, Jean-Etienne & Tiwari, Aviral Kumar & Mensi, Walid, 2024. "Extreme downside risk connectedness and portfolio hedging among the G10 currencies," International Economics, Elsevier, vol. 178(C).
    4. Zhu, Huiming & Zeng, Tian & Wang, Xinghui & Xia, Xiling, 2025. "Frequency domain cross-quantile coherency and connectedness network of exchange rates: Evidence from ASEAN+3 countries," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).

    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. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. Bampinas, Georgios & Panagiotidis, Theodore, 2024. "How would the war and the pandemic affect the stock and cryptocurrency cross-market linkages?," Research in International Business and Finance, Elsevier, vol. 70(PA).
    3. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. Jiang, Yonghong & Lie, Jiayi & Wang, Jieru & Mu, Jinqi, 2021. "Revisiting the roles of cryptocurrencies in stock markets: A quantile coherency perspective," Economic Modelling, Elsevier, vol. 95(C), pages 21-34.
    5. 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.
    6. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    7. Naeem, Muhammad Abubakr & Mbarki, Imen & Shahzad, Syed Jawad Hussain, 2021. "Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 496-514.
    8. Corbet, Shaen & Katsiampa, Paraskevi & Lau, Chi Keung Marco, 2020. "Measuring quantile dependence and testing directional predictability between Bitcoin, altcoins and traditional financial assets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. Fathin Faizah Said & Raja Solan Somasuntharam & Mohd Ridzwan Yaakub & Tamat Sarmidi, 2023. "Impact of Google searches and social media on digital assets’ volatility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    10. Fayssal Jamhamed & Franck Martin & Fabien Rondeau & Josué Thélissaint & Stéphane Tufféry, 2024. "Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-13, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    11. Maghyereh, Aktham & Abdoh, Hussein, 2021. "Time–frequency quantile dependence between Bitcoin and global equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    12. Shahzad, Syed Jawad Hussain & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav & Lucey, Brian, 2019. "Is Bitcoin a better safe-haven investment than gold and commodities?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 322-330.
    13. Jinsha Zhao, 2022. "Do economic crises cause trading in Bitcoin?," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 14(4), pages 465-490, April.
    14. A. V. Biju & Aparna Merin Mathew & P. P. Nithi Krishna & M. P. Akhil, 2022. "Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis," Digital Finance, Springer, vol. 4(4), pages 275-290, December.
    15. Kwon, Ji Ho, 2020. "Tail behavior of Bitcoin, the dollar, gold and the stock market index," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    16. Riza Demirer & Rangan Gupta & Hossein Hassani & Xu Huang, 2020. "Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram," Economies, MDPI, vol. 8(1), pages 1-12, March.
    17. Li, Dongxin & Hong, Yanran & Wang, Lu & Xu, Pengfei & Pan, Zhigang, 2022. "Extreme risk transmission among bitcoin and crude oil markets," Resources Policy, Elsevier, vol. 77(C).
    18. A. H. Nzokem, 2023. "Bitcoin versus S&P 500 Index: Return and Risk Analysis," Papers 2310.02436, arXiv.org.
    19. Cevik, Emrah Ismail & Gunay, Samet & Zafar, Muhammad Wasif & Destek, Mehmet Akif & Bugan, Mehmet Fatih & Tuna, Fatih, 2022. "The impact of digital finance on the natural resource market: Evidence from DeFi, oil, and gold," Resources Policy, Elsevier, vol. 79(C).
    20. Noman, Abu Hanifa Md & Karim, Muhammad Mahmudul & Hassan, Mohammad Kabir & Khan, Muhammad Asif & Pervin, Sajeda, 2023. "COVID-19 pandemic and the dynamics of major investable assets: What gives shelter to investors?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 14-30.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    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:eee:reveco:v:88:y:2023:i:c:p:1035-1051. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.