1. Quantile connectivity between cryptocurrency, commodities, gold and BRICS index: what is the best investment strategy?. (2025). Jarboui, Anis ; Bouzguenda, Mariem.
   In: Eurasian Economic Review.
   RePEc:spr:eurase:v:15:y:2025:i:1:d:10.1007_s40822-024-00290-y.

   Full description at Econpapers || Download paper

1. Aalborg, H. A., Molnár, P., & de Vries, J. E. (2019). What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters, 29, 255–265.

2. Ahn, Y., & Kim, D. (2021). Emotional trading in the cryptocurrency market. Finance Research Letters, 42, 101912.

3. Al-Shboul, M., & Anwar, S. (2017). Long memory behavior in Singapore’s tourism market. International Journal of Tourism Research, 19, 524–534.
   Paper not yet in RePEc: Add citation now
   

4. Andrews, D. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 59, 817–858.

5. Ante, L., Fiedler, I., & Strehle, E. (2021a). The influence of stablecoin issuances on cryptocurrency markets. Finance Research Letters, 41(July), 101867.

6. Ante, L., Fiedler, I., & Strehle, E. (2021b). The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of Bitcoin. Technological Forecasting & Social Change, 170, 120851.

7. Arouri, M. E., Hammoudeh, S., Lahiani, A., & Nguyen, D. K. (2012). Long memory and structural breaks in modeling the return and volatility dynamics of precious metals. The Quarterly Review of Economics and Finance, 52(2), 207–218.
   Paper not yet in RePEc: Add citation now
   

8. Assaf, A. (2015). Long memory and level shifts in REITs returns and volatility. International Review of Financial Analysis, 42, 172–182.

9. Assaf, A., Gil-Alana, L. A., & Mokni, K. (2022). True or spurious long memory in the cryptocurrency markets: Evidence from a multivariate test and other Whittle estimation methods. Empirical Economics, 63, 1543–1570.

10. Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.

11. Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22.

12. Bai, J., & Perron, P. (2006). Multiple structural change models: A simulation analysis. In D. Corbae, S. N. Durlauf, & B. E. Hansen (Eds.), Econometric Theory and Practice: Frontiers of Analysis and Applied Research (pp. 212–238). Cambridge University Press.

13. Baillie, R. T., & Morana, C. (2009). Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach. Journal of Economic Dynamics and Control, 33(8), 1577–1592.

14. Balcilara, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74–81.

15. Bariviera, A. F., Basgall, M. J., Hasperué, W., & Naiouf, M. (2017). Some stylized facts of the Bitcoin market. Physica A: Statistical Mechanics and its Applications, 484, 82–90.
    Paper not yet in RePEc: Add citation now
    

16. Baur, D. G., Cahill, D., Godfrey, K., & Liu, Z. (2019). Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume. Finance Research Letters, 31, 78–92.

17. Bianchi, D., Babiak, M., & Dickerson, A. (2022). Trading volume and liquidity provision in cryptocurrency markets. Journal of Banking and Finance, 142, 106547.

18. Blau, B. M. (2017). Price dynamics and speculative trading in Bitcoin. Research in International Business and Finance, 41, 15–21.
    Paper not yet in RePEc: Add citation now
    

19. Bouraoui, T. (2020). The drivers of Bitcoin trading volume in selected emerging countries. The Quarterly Review of Economics and Finance, 76, 218–229.

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

21. Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 32(November), 87–95.

22. Bouri, E., Lau, C. K., Lucey, B., & Roubaud, D. (2019b). Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Research Letters, 29, 340–346.

23. Bradshaw, M., Graaf, T. V., & Connolly, R. (2019). Preparing for the new oil order? Saudi Arabia and Russia. Energy Strategy Reviews, 26, 100374.
    Paper not yet in RePEc: Add citation now
    

24. Briola, A., Vidal-Tomás, D., Wang, Y., & Aste, T. (2023). Anatomy of a Stablecoin’s failure: The Terra-Luna case. Finance Research Letters, 51(January), 103358.

25. Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics, 108(4), 905–939.

26. Caporale, G. M., Karanasos, M., Yfanti, S., & Kartsaklas, A. (2021). Investors’ trading behaviour and stock market volatility during crisis periods: A dual long-memory model for the Korean Stock Exchange. International Journal of Finance & Economics, 26(3), 4441–4461.

27. Charfeddine, L. (2016). Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis. Economic Modelling, 53(February), 354–374.

28. Charfeddine, L., & Guégan, D. (2012). Breaks or long memory behavior: An empirical investigation. Physica a: Statistical Mechanics and Its Applications, 391(22), 5712–5726.
    Paper not yet in RePEc: Add citation now
    

29. Charfeddine, L., Benlagha, N., & Maouchi, Y. (2020). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling, 85(February), 198–217.

30. Charles, A., & Darné, O. (2019). Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks. Economics Bulletin, 39(2), 954–968.

31. Coakley, J., Dollery, J., & Kellard, N. (2011). Long memory and structural breaks in commodity futures markets. The Journal of Futures Markets, 31(11), 1076–1113.

32. Copeland, T. E. (1976). A model of asset trading under the assumption of sequential information arrival. The Journal of Finance, 31(4), 1149–1168.

33. Copeland, T. E., & Friedman, D. (1987). The effect of sequential information arrival on asset prices: An experimental study. The Journal of Finance, 42(3), 763–797.

34. Dasgupta, A., Prat, A., & Verardo, M. (2011). The price impact of institutional herding. The Review of Financial Studies, 24(3), 892–925.

35. El Alaoui, M., Bouri, E., & Roubaud, D. (2019). Bitcoin price–volume: A multifractal cross-correlation approach. Finance Research Letters, 31, 374–381.
    Paper not yet in RePEc: Add citation now
    
36. Fakhfekh, M., & Jeribi, A. (2020). Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075.
    Paper not yet in RePEc: Add citation now
    

37. Fleming, J., & Kirby, C. (2011). Long memory in volatility and trading volume. Journal of Banking & Finance, 35(7), 1714–1726.

38. Fousekis, P., & Tzaferi, D. (2021). Returns and volume: Frequency connectedness in cryptocurrency markets. Economic Modelling, 95, 13–20.

39. Gallant, A. R., Rossi, P. E., & Tauchen, G. (1992). Stock prices and volume. The Review of Financial Studies, 5(2), 199–242.

40. Gandal, N., Hamrick, J., Moore, T., & Oberman, T. (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95(May), 86–96.

41. Gebka, B., & Wohar, M. E. (2013). Causality between trading volume and returns: Evidence from quantile regressions. International Review of Economics & Finance, 27(June), 144–159.

42. Gemici, E., & Polat, M. (2019). Relationship between price and volume in the Bitcoin market. Journal of Risk Finance, 20(5), 435–444.
    Paper not yet in RePEc: Add citation now
    
43. Granger, C. W., & Hyung, N. (2004). Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance, 11(3), 399–421.
    Paper not yet in RePEc: Add citation now
    

44. Granger, C. W., & Joyeux, R. (1980). An Introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29.

45. Han, J., Li, X., Ma, G., & Kennedy, A. (2022). Long memory in retail trading activity. Retrieved from SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4242634 .
    Paper not yet in RePEc: Add citation now
    

46. He, H., & Wang, J. (1995). Differential information and dynamic behavior of stock trading volume. The Review of Financial Studies, 8(4), 919–972.

47. Hurst, H. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770–799.
    Paper not yet in RePEc: Add citation now
    
48. James, N. (2021). Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19. Physica a: Statistical Mechanics and Its Applications, 570(May), 125831.
    Paper not yet in RePEc: Add citation now
    
49. James, N., Menzies, M., & Chan, J. (2021). Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19. Physica a: Statistical Mechanics and Its Applications, 565(1), 125581.
    Paper not yet in RePEc: Add citation now
    

50. Karanasos, M., & Kartsaklas, A. (2009). Dual long-memory, structural breaks and the link between turnover and the range-based volatility. Journal of Empirical Finance, 16(5), 838–851.

51. Kellard, N. M., Jiang, Y., & Wohar, M. (2015). Spurious long memory, uncommon breaks and the implied-realized volatility puzzle. Journal of International Money and Finance, 56(September), 36–54.

52. Khuntia, S., & Pattanayak, J. (2020). Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume. Finance Research Letters, 32, 101077.

53. Kim, C.-S., & Phillips, P. C. (2000). Modified log periodogram regression. Yale University.
    Paper not yet in RePEc: Add citation now
    

54. King, T., & Koutmos, D. (2021). Herding and feedback trading in cryptocurrency markets. Annals of Operations Research, 300, 79–96.

55. Kumar, A. (2004). Long memory in stock trading volume: Evidence from Indian stock market. Retrieved from SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=557681 .
    Paper not yet in RePEc: Add citation now
    
56. Künsch, H. (1987). Statistical aspects of self-similar processes. In Proceedings of the 1st world congress of the Bernoulli society (pp. 67–74). VNU Science Press.
    Paper not yet in RePEc: Add citation now
    

57. Lahmiri, S., & Bekiros, S. (2018). Chaos, randomness and multi-fractality in Bitcoin market. Chaos, Solitons & Fractals, 106, 28–34.

58. Lanouar, C. (2016). Breaks or long Range dependence in the futures energy volatility: Out-of-sample forecasting and VaR analysis. Economic modelling, 53, 354–374.
    Paper not yet in RePEc: Add citation now
    

59. Leccadito, A., Rachedi, O., & Urga, G. (2015). True versus spurious long memory: Some theoretical results and a monte carlo comparison. Econometric Reviews, 34(4), 452–479.

60. Lo, A. W. (1991). Long-term memory in stock market prices. Econometrica, 59(5), 1279–1313.

61. Lo, A. W., & Wang, J. (2000). Trading volume: Definitions, data analysis, and implications of portfolio theory. The Review of Financial Studies, 13(2), 257–300.

62. Lobato, I., & Velasco, C. (2000). Long memory in stock-market trading volume. Journal of Business & Economic Statistics, 18(4), 410–427.

63. Mandelbrot, B. (1972). Statistical methodology for nonperiodic cycles: From the covariance To R/S analysis. Annals of Economic and Social Measurement, 1(3), 259–290.

64. McCloskey, A., & Hill, J. B. (2017). Parameter estimation robust to low-frequency contamination. Journal of Business & Economic Statistics, 35(4), 598–610.

65. Mensi , W., Al-Yahyaee, K. H., & Kang, S. H. (2016). Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum. Finance Research Letters, 29(June), 222–230.
    Paper not yet in RePEc: Add citation now
    

66. Mohamad, A., & Stavroyiannis, S. (2022). Do birds of a feather flock together? Evidence from time-varying herding behaviour of bitcoin and foreign exchange majors during Covid-19. Journal of International Financial Markets, Institutions and Money, 80(September), 101646.

67. Nasir, M. A., Huynh, T. L., Nguyen, S. P., & Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(2), 1–13.

68. Nofsinger, J. R., & Sias, R. W. (1999). Herding and feedback trading by institutional and individual investors. The Journal of Finance, 54(6), 2263–2295.

69. Olmo, J., Pilbeam, K., & Pouliot, W. (2011). Detecting the presence of insider trading via structural break tests. Journal of Banking & Finance, 35(11), 2820–2828.

70. Partz, H. (2022). A brief history of Bitcoin crashes and bear markets: 2009–2022. Retrieved from Cointelegraph: the future of money: https://cointelegraph.com/news/a-brief-history-of-bitcoin-crashes-and-bear-markets-2009-2022 .
    Paper not yet in RePEc: Add citation now
    

71. Philippas, D., Philippas, N., Tziogkidis, P., & Rjiba, H. (2020). Signal-herding in cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 65(March), 101191.

72. Phillip, A., Chan, J. S., & Peiris, S. (2018). A new look at cryptocurrencies. Economics Letters, 163(February), 6–9.

73. Phillips, P. C. (1999). Unit root log periodogram regression. Retrieved from SSRN Working Papers: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=216931 . Accessed 26 Aug 2022.
    Paper not yet in RePEc: Add citation now
    

74. Phillips, P. C. (2007). Unit root log periodogram regression. Journal of Econometrics, 138(1), 104–124.

75. Phillips, P. C., & Shimotsu, K. (2004). Local whittle estimation in nonstationary and unit root cases. The Annals of Statistics, 32(2), 656–692.
    Paper not yet in RePEc: Add citation now
    
76. Phillips, P. J., & Pohl, G. (2022). Bitcoin’s place in a mean-variance efficient portfolio of seven cryptocurrencies. Retrieved from SSRN : https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4234823 . Accessed 26 Aug 2022.
    Paper not yet in RePEc: Add citation now
    

77. Qu, Z. (2011). A test against spurious long memory. Journal of Business & Economic Statistics, 29(3), 423–438.

78. Robinson, P. (1995a). Log-periodogram regression of time Series with long range dependence. The Annals of Statistics, 23(3), 1048–1072.
    Paper not yet in RePEc: Add citation now
    
79. Robinson, P. (1995b). Gaussian semiparametric estimation of long range dependence. The Annals of Statistics, 23(5), 1630–1661.
    Paper not yet in RePEc: Add citation now
    
80. Rosenfeld, E. (2015). Bitcoin is one of 2015′s biggest winners. Retrieved from CNBC: https://www.cnbc.com/2015/12/29/bitcoin-is-one-of-2015s-biggest-winners.html . Accessed 28 Aug 2022.
    Paper not yet in RePEc: Add citation now
    

81. Rossi, E., & Magistris, P. S. (2013). Long memory and tail dependence in trading volume and volatility. Journal of Empirical Finance, 22(June), 94–112.

82. Sahoo, P. K. (2021). COVID-19 pandemic and cryptocurrency markets: An empirical analysis from a linear and nonlinear causal relationship. Studies in Economics and Finance, 38(2), 454–468.
    Paper not yet in RePEc: Add citation now
    

83. Shimotsu, K. (2006). Simple (but Effective) tests of long memory versus structural breaks. Queen's Economics Department Working Paper No. 1101: https://www.econ.queensu.ca/research/working-papers/1101 .

84. Shimotsu, K. (2010). Exact local Whittle estimation of fractional integration with unknown mean and time trend. Econometric Theory, 26(2), 501–540.

85. Shimotsu, K., & Phillips, P. C. (2005). Exact local whittle estimation of fractional integration. The Annals of Statistics, 33(4), 1890–1933.
    Paper not yet in RePEc: Add citation now
    

86. Sias, R. W. (2004). Institutional herding. The Review of Financial Studies, 17(1), 165–206.

87. Sidorenko, E. (2019). Stablecoin as a new financial instrument. In S. I. Ashmarina, M. Vochozka, & V. V. Mantulenko (Eds.), Digital age: Chances, challenges and future (pp. 630–638). Springer Nature.
    Paper not yet in RePEc: Add citation now
    

88. Silva, P. V., Klotzle, M. C., Pinto, A. C., & Gomes, L. L. (2019). Herding behavior and contagion in the cryptocurrency market. Journal of Behavioral and Experimental Finance, 22(June), 41–50.

89. Smith, A. (2005). Level shifts and the illusion of long memory in economic time series. Journal of Business & Economic Statistics, 23(3), 321–335.

90. Soylu, P. K., Okur, M., Çatıkkaş, O., & Altintig, Z. A. (2020). Long memory in the volatility of selected cryptocurrencies: Bitcoin, Ethereum and Ripple. Journal of Risk and Financial Management, 13(6), 1–20.

91. Stosic, D., Stosic, D., Ludermir, T. B., & Stosic, T. (2019). Multifractal behavior of price and volume changes in the cryptocurrency market. Physica a: Statistical Mechanics and Its Applications, 520, 54–61.
    Paper not yet in RePEc: Add citation now
    
92. Telli, Ş, & Chen, H. (2020). Structural breaks and trend awareness-based interaction in crypto markets. Physica a: Statistical Mechanics and Its Applications, 558(15), 124913.
    Paper not yet in RePEc: Add citation now
    
93. Thompson, P. (2019). Cryptocurrency news: What happened in 2019? Retrieved from Coingeek: https://coingeek.com/cryptocurrency-news-what-happened-in-2019/ . Accessed 2 Sep 2022.
    Paper not yet in RePEc: Add citation now
    
94. Tsuji, C. (2002). Long-term memory and applying the multi-factor ARFIMA models in financial markets. Asia-Pacific Financial Markets, 9(3), 283–304.
    Paper not yet in RePEc: Add citation now
    

95. Varneskov, R. T., & Perron, P. (2018). Combining long memory and level shifts in modelling and forecasting the volatility of asset returns. Quantitative Finance, 18(3), 371–393.

96. Vidal-Tomás, D. (2022). Which cryptocurrency data sources should scholars use? International Review of Financial Analysis, 81(May), 102061.
    Paper not yet in RePEc: Add citation now
    

97. Wang, J. (1994). A model of competitive stock trading volume. Journal of Political Economy, 102(1), 127–168.

98. Wang, J.-N., Liu, H.-C., & Hsu, Y.-T. (2020). Time-of-day periodicities of trading volume and volatility in Bitcoin exchange: Does the stock market matter? Finance Research Letters, 34, 101243.

99. Wang, P., Zhang, W., Li, X., & Shen, D. (2019). Trading volume and return volatility of Bitcoin market: Evidence for the sequential information arrival hypothesis. Journal of Economic Interaction and Coordination, 14, 377–418.

100. Wei, Y., Wang, Y., & Huang, D. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), 1477–1484.

101. Zhao, Y., Liu, N., & Li, W. (2022). Industry herding in crypto assets. International Review of Financial Analysis, 84(November), 102335.

1. The mutual predictability of Bitcoin and web search dynamics. (2022). Sussmuth, Bernd.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:41:y:2022:i:3:p:435-454.

   Full description at Econpapers || Download paper

2. The cryptocurrency uncertainties and investment transitions: Evidence from high and low carbon energy funds in China. (2022). Mirza, Nawazish ; Umar, Muhammad ; Yan, Lei.
   In: Technological Forecasting and Social Change.
   RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521007575.

   Full description at Econpapers || Download paper

3. Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models. (2022). Leonardos, Stefanos ; Piliouras, Georgios ; Koki, Constandina.
   In: Research in International Business and Finance.
   RePEc:eee:riibaf:v:59:y:2022:i:c:s0275531921001756.

   Full description at Econpapers || Download paper

4. Investor attention in cryptocurrency markets. (2022). Smales, Lee.
   In: International Review of Financial Analysis.
   RePEc:eee:finana:v:79:y:2022:i:c:s105752192100288x.

   Full description at Econpapers || Download paper

5. Bitcoin in the economics and finance literature: a survey. (2021). Rohilla, Purnima ; Kayal, Parthajit.
   In: SN Business & Economics.
   RePEc:spr:snbeco:v:1:y:2021:i:7:d:10.1007_s43546-021-00090-5.

   Full description at Econpapers || Download paper

6. The time-varying causal relationship between the Bitcoin market and internet attention. (2021). Zhang, Xun ; Wang, Shouyang ; Tao, Rui ; Lu, Fengbin.
   In: Financial Innovation.
   RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00275-9.

   Full description at Econpapers || Download paper

7. Say anything you want about me if you spell my name right: the effect of Internet searches on financial market. (2021). Kliber, Agata ; Rutkowska, Aleksandra.
   In: Central European Journal of Operations Research.
   RePEc:spr:cejnor:v:29:y:2021:i:2:d:10.1007_s10100-019-00665-6.

   Full description at Econpapers || Download paper

8. Bitcoin Mining Activity and Volatility Dynamics in the Power Market. (2021). GUPTA, RANGAN ; Demirer, Riza ; Karmakar, Sayar.
   In: Working Papers.
   RePEc:pre:wpaper:202166.

   Full description at Econpapers || Download paper

9. Intertemporal asset pricing with bitcoin. (2021). Payne, James ; Koutmos, Dimitrios.
   In: Review of Quantitative Finance and Accounting.
   RePEc:kap:rqfnac:v:56:y:2021:i:2:d:10.1007_s11156-020-00904-x.

   Full description at Econpapers || Download paper

10. On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. (2021). ÖZÜTLER, Hatice ; Benzekri, Mohamed Khalil ; Ztler, Hatice Ehime.
    In: Journal of Economic Policy Researches.
    RePEc:ist:iujepr:v:8:y:2021:i:2:p:293-309.

    Full description at Econpapers || Download paper

11. Cryptocurrencies and blockchain. Overview and future perspectives. (2021). Sebastião, Helder ; Corteso, Pedro Manuel ; Osrio, Paulo Jos ; Osorio, Paulo Jose ; Correia, Helder Miguel.
    In: International Journal of Economics and Business Research.
    RePEc:ids:ijecbr:v:21:y:2021:i:3:p:305-342.

    Full description at Econpapers || Download paper

12. Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework. (2021). Khan, Muhammed Asif ; Alnemer, Hashem A ; Hkiri, Besma.
    In: JRFM.
    RePEc:gam:jjrfmx:v:14:y:2021:i:6:p:275-:d:577199.

    Full description at Econpapers || Download paper

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    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521004704.

    Full description at Econpapers || Download paper

14. The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of Bitcoin. (2021). Ante, Lennart ; Fiedler, Ingo ; Strehle, Elias .
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521002833.

    Full description at Econpapers || Download paper

15. Cryptocurrency price volatility and investor attention. (2021). al Guindy, Mohamed.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:76:y:2021:i:c:p:556-570.

    Full description at Econpapers || Download paper

16. Do higher-order realized moments matter for cryptocurrency returns?. (2021). Ahmed, Walid ; al Mafrachi, Mustafa.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:72:y:2021:i:c:p:483-499.

    Full description at Econpapers || Download paper

17. Structural vector error correction modelling of Bitcoin price. (2021). le Fur, Eric ; Haffar, Adlane.
    In: The Quarterly Review of Economics and Finance.
    RePEc:eee:quaeco:v:80:y:2021:i:c:p:170-178.

    Full description at Econpapers || Download paper

18. Stock market volatility forecasting: Do we need high-frequency data?. (2021). Výrost, Tomáš ; Molnár, Peter ; Lyócsa, Štefan ; Vrost, Toma ; Lyocsa, Tefan ; Molnar, Peter.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:37:y:2021:i:3:p:1092-1110.

    Full description at Econpapers || Download paper

19. Market reaction to large transfers on the Bitcoin blockchain - Do size and motive matter?. (2021). Ante, Lennart ; Fiedler, Ingo.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:39:y:2021:i:c:s1544612320304438.

    Full description at Econpapers || Download paper

20. Does investor sentiment on social media provide robust information for Bitcoin returns predictability?. (2021). Renault, Thomas ; Guegan, Dominique.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319314199.

    Full description at Econpapers || Download paper

21. Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty. (2021). Xie, Tian ; Qiu, Yue ; Wang, Zongrun ; Zhang, Xinyu.
    In: Journal of Empirical Finance.
    RePEc:eee:empfin:v:62:y:2021:i:c:p:179-201.

    Full description at Econpapers || Download paper

22. Bitcoin mining activity and volatility dynamics in the power market. (2021). GUPTA, RANGAN ; Demirer, Riza ; Karmakar, Sayar.
    In: Economics Letters.
    RePEc:eee:ecolet:v:209:y:2021:i:c:s0165176521003888.

    Full description at Econpapers || Download paper

23. Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies. (2021). Xie, Tian ; Qiu, Yue ; Wang, Yifan.
    In: Economics Letters.
    RePEc:eee:ecolet:v:208:y:2021:i:c:s0165176521003694.

    Full description at Econpapers || Download paper

24. Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model. (2021). NG, KOK HAUR ; Koh, You-Beng ; Tan, Chia-Yen.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:56:y:2021:i:c:s1062940821000164.

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25. Does Bitcoin React to Trump’s Tweets?. (2021). Duc, Toan Luu.
    In: Journal of Behavioral and Experimental Finance.
    RePEc:eee:beexfi:v:31:y:2021:i:c:s2214635021000903.

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26. WHERE DO WE STAND IN CRYPTOCURRENCIES ECONOMIC RESEARCH? A SURVEY BASED ON HYBRID ANALYSIS. (2021). Fernandez Bariviera, Aurelio ; Meredizsola, Ignasi.
    In: Journal of Economic Surveys.
    RePEc:bla:jecsur:v:35:y:2021:i:2:p:377-407.

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27. Cryptocurrency Market Consolidation in 2020--2021. (2021). Zd, Stanislaw Dro ; Wkatorek, Marcin ; Kwapie, Jaroslaw.
    In: Papers.
    RePEc:arx:papers:2112.06552.

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28. Causal effect of regulated Bitcoin futures on volatility and volume. (2021). Menchetti, Fiammetta ; Cipollini, Fabrizio ; Mealli, Fabrizia.
    In: Papers.
    RePEc:arx:papers:2109.15052.

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29. Exploring the short-term momentum effect in the cryptocurrency market. (2020). Nguyen, Thi Thanh Ha ; Ha, Nguyen ; Parikh, Nirav Y ; Liu, Bin.
    In: Evolutionary and Institutional Economics Review.
    RePEc:spr:eaiere:v:17:y:2020:i:2:d:10.1007_s40844-020-00176-z.

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30. Momentum effects in the cryptocurrency market after one-day abnormal returns. (2020). Plastun, Alex ; Caporale, Guglielmo Maria.
    In: Financial Markets and Portfolio Management.
    RePEc:kap:fmktpm:v:34:y:2020:i:3:d:10.1007_s11408-020-00357-1.

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31. Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?. (2020). Pagnottoni, Paolo ; Resta, Marina ; de Giuli, Maria Elena.
    In: Risks.
    RePEc:gam:jrisks:v:8:y:2020:i:2:p:44-:d:354452.

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32. Bitcoin Price Risk—A Durations Perspective. (2020). Dimpfl, Thomas ; Odelli, Stefania.
    In: JRFM.
    RePEc:gam:jjrfmx:v:13:y:2020:i:7:p:157-:d:386045.

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33. Cryptocurrency Returns before and after the Introduction of Bitcoin Futures. (2020). Stengos, Thanasis ; Deniz, Pinar.
    In: JRFM.
    RePEc:gam:jjrfmx:v:13:y:2020:i:6:p:116-:d:367403.

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34. A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns. (2020). Vravosinos, Orestis ; Stengos, Thanasis ; Panagiotidis, Theodore.
    In: JRFM.
    RePEc:gam:jjrfmx:v:13:y:2020:i:2:p:33-:d:319970.

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35. Volatility persistence in cryptocurrency markets under structural breaks. (2020). Madigu, Godfrey ; Gil-Alana, Luis ; Abakah, Emmanuel ; Romero-Rojo, Fatima ; Aikins, Emmanuel Joel.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:69:y:2020:i:c:p:680-691.

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36. The drivers of Bitcoin trading volume in selected emerging countries. (2020). BOURAOUI, Taoufik.
    In: The Quarterly Review of Economics and Finance.
    RePEc:eee:quaeco:v:76:y:2020:i:c:p:218-229.

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37. Bitcoin and gold price returns: A quantile regression and NARDL analysis. (2020). Jareño, Francisco ; Tolentino, Marta ; De, Maria ; Sierra, Karen ; Jareo, Francisco.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:67:y:2020:i:c:s0301420719309985.

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38. Cryptocurrency accepting venues, investor attention, and volatility. (2020). Sabah, Nasim.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:36:y:2020:i:c:s154461231930649x.

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39. Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility. (2020). Yin, Libo ; Su, Zhi ; Fang, Tong.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:71:y:2020:i:c:s1057521920302106.

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40. More heat than light: Investor attention and bitcoin price discovery. (2020). Rzayev, Khaladdin ; Ibikunle, Gbenga ; McGroarty, Frank.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:69:y:2020:i:c:s1057521919306301.

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41. Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators. (2020). Hung, Jui-Cheng ; Yang, Jimmy J ; Liu, Hung-Chun.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:52:y:2020:i:c:s1062940820300620.

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42. Limited attention, salience of information and stock market activity. (2020). Veiga, Helena ; Ramos, Sofia ; Latoeiro, Pedro.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:87:y:2020:i:c:p:92-108.

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43. Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin. (2020). Širaňová, Mária ; Plíhal, Tomáš ; Molnár, Peter ; Lyócsa, Štefan ; Plihal, Toma ; Molnar, Peter ; Iraova, Maria.
    In: Journal of Economic Dynamics and Control.
    RePEc:eee:dyncon:v:119:y:2020:i:c:s0165188920301482.

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44. High- and low-level chaos in the time and frequency market returns of leading cryptocurrencies and emerging assets. (2020). ALAGIDEDE, IMHOTEP ; Omane-Adjepong, Maurice.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:132:y:2020:i:c:s096007791930520x.

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45. Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models. (2020). Leonardos, Stefanos ; Piliouras, Georgios ; Koki, Constandina.
    In: Papers.
    RePEc:arx:papers:2011.03741.

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46. Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis. (2020). Fernandez Bariviera, Aurelio ; Merediz-Sola, Ignasi.
    In: Papers.
    RePEc:arx:papers:2003.09723.

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47. Can Economic Policy Uncertainty, Volume, Transaction Activity and Twitter Predict Bitcoin? Evidence from Time-Varying Granger Causality Tests. (2019). Oxley, Les ; Hu, Yang ; Lang, Chunlin.
    In: Working Papers in Economics.
    RePEc:wai:econwp:19/12.

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48. Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets. (2019). Škrinjarić, Tihana.
    In: IJFS.
    RePEc:gam:jijfss:v:7:y:2019:i:4:p:59-:d:275379.

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49. Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting. (2019). Walther, Thomas ; Bouri, Elie ; Klein, Tony.
    In: Journal of International Financial Markets, Institutions and Money.
    RePEc:eee:intfin:v:63:y:2019:i:c:s1042443119302446.

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50. Momentum Effects in the Cryptocurrency Market After One-Day Abnormal Returns. (2019). Plastun, Alex ; Caporale, Guglielmo Maria.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_7917.

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