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1. Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S., & Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID‐19 pandemic. Technological Forecasting and Social Change, 161, 120261.
   Paper not yet in RePEc: Add citation now
   
2. Aslam, F., Ferreira, P., Ali, H., & Kauser, S. (2021). Herding behavior during the Covid‐19 pandemic: A comparison between Asian and European stock markets based on intraday multifractality. Eurasian Economic Review, 12, 333–359.
   Paper not yet in RePEc: Add citation now
   
3. Assaf, A., Mokni, K., Yousaf, I., & Bhandari, A. (2023). Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID‐19. Research in International Business and Finance, 64, 101821.
   Paper not yet in RePEc: Add citation now
   

4. Ben Mabrouk, A. (2008). A higher order multifractal formalism. Statistics and Probability Letters, 78(12), 1412–1421.

5. Bertrand, P. R., Fhima, M., & Guillin, A. (2011). Off‐line detection of multiple change points by the filtered derivative with p‐value method. Sequential Analysis, 30(2), 172–207.
   Paper not yet in RePEc: Add citation now
   
6. Bertrand, P. R., Fhima, M., & Guillin, A. (2013). Local estimation of the Hurst index of multifractional Brownian motion by increment ratio statistic method. ESAIM: Probability and Statistics, 17, 307–327.
   Paper not yet in RePEc: Add citation now
   
7. Brock, W. A., Dechert, W. D., & Scheinkman, J. A. (1987). A Test for independence based on the correlation dimension. Econometric Reviews, 6(1), 85–104.
   Paper not yet in RePEc: Add citation now
   
8. Cao, G., Cao, J., & Xu, L. (2013). Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF‐DFA. Physica A: Statistical Mechanics and its Applications, 392(4), 797–807.
   Paper not yet in RePEc: Add citation now
   

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

10. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.
    Paper not yet in RePEc: Add citation now
    
11. Elmi, M. (2014). Detection of multiple change points by the filtered derivative and false discovery rate. International Journal of Statistics and Probability, 3(1), 12–23.
    Paper not yet in RePEc: Add citation now
    

12. Geweke, J., & Porter‐Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4, 221–237.

13. Ghazani, M. M., & Khosravi, R. (2020). Multifractal detrended cross‐correlation analysis on benchmark cryptocurrencies and crude oil prices. Physica A: Statistical Mechanics and its Applications, 560, 125172.
    Paper not yet in RePEc: Add citation now
    

14. Gourieroux, C., Hencic, A., & Jasiak, J. (2021). Forecast performance and bubble analysis in noncausal MAR(1, 1) processes. Journal of Forecasting, 40(2), 301–326.

15. Huang, X., & Tang, H. (2022). Measuring multi‐volatility states of financial markets based on multifractal clustering model. Journal of Forecasting, 41(3), 422–434.

16. Ihlen, E. A. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3, 141–159.
    Paper not yet in RePEc: Add citation now
    
17. Kantelhardt, J. W., Zschiegner, S. A., Koscielny‐Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A Statistical Mechanics and Its Applications, 316(1–4), 87–114.
    Paper not yet in RePEc: Add citation now
    
18. Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.
    Paper not yet in RePEc: Add citation now
    

19. Kristjanpoller, W., & Bouri, E. (2019). Asymmetric multifractal cross‐correlations between the main world currencies and the main cryptocurrencies. Physica A: Statistical Mechanics and its Applications, 523, 1057–1071.

20. Lavielle, M. (2005). Using penalized contrasts for the change‐point problem. Signal Processing, 85, 1501–1510.
    Paper not yet in RePEc: Add citation now
    
21. Leigh, W., Purvis, R., & Ragusa, J. M. (2002). Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: A case study in romantic decision support. Decision Support Systems, 32(4), 361–377.
    Paper not yet in RePEc: Add citation now
    

22. Mandelbrot, B. (1967). The variation of some other speculative prices. The Journal of Business, 40(4), 393–413.

23. MathWorks, Inc. (2016). MATLAB, The language of technical computing. Natick, MA: The MathWorks. https://www.mathworks.com/.
    Paper not yet in RePEc: Add citation now
    
24. Menéndez, P., Ghosh, S., & Beran, J. (2010). On rapid change points under long memory. Journal of Statistical Planning and Inference, 140(11), 3343–3354.
    Paper not yet in RePEc: Add citation now
    

25. Mensi, W., Lee, Y.‐J., Al‐Yahyaee, K. H., Sensoy, A., & Yoon, S.‐M. (2019). Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis. Finance Research Letters, 31, 19–25.

26. Mensi, W., Sensoy, A., Vo, X. V., & Kang, S. H. (2022). Pricing efficiency and asymmetric multifractality of major asset classes before and during COVID‐19 crisis. The North American Journal of Economics and Finance, 62, 101773.

27. Oh, H., & Thomas, R. J. (2010). Nonlinear time series analysis on the offer behaviors observed in an electricity market. Decision Support Systems, 49(2), 132–137.
    Paper not yet in RePEc: Add citation now
    

28. Pec̆iulis, T., Ahmad, N., Menegaki, A. N., & Bibi, A. (2024). Forecasting of cryptocurrencies: Mapping trends, influential sources, and research themes. Journal of Forecasting. https://doi.org/10.1002/for.3114.

29. Rabbouch, H., Saâdaoui, F., & Mraihi, R. (2017). Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing. Neurocomputing, 26, 157–173.
    Paper not yet in RePEc: Add citation now
    

30. Saâdaoui, F. (2010). Acceleration of the EM algorithm via extrapolation methods: Review, comparison and new methods. Computational Statistics and Data Analysis, 54(3), 750–766.

31. Saâdaoui, F. (2012). A probabilistic clustering method for US interest rates analysis. Quantitative Finance, 12(1), 135–148.

32. Saâdaoui, F. (2018). Testing for multifractality of Islamic stock markets. Physica A: Statistical Mechanics and its Applications, 496, 263–273.
    Paper not yet in RePEc: Add citation now
    
33. Saâdaoui, F. (2023). Skewed multifractal scaling of stock markets over the COVID‐19 era. Chaos, Solitons & Fractals, 170, 113372.
    Paper not yet in RePEc: Add citation now
    
34. Saâdaoui, F. (2024a). Segmented multifractal detrended fluctuation analysis for assessing inefficiency in North African stock markets. Chaos, Solitons & Fractals, 181, 114652.
    Paper not yet in RePEc: Add citation now
    
35. Saâdaoui, F. (2024b). Segmented multifractal detrended fluctuation analysis in key economic sectors. Fluctuation and Noise Letters. https://doi.org/10.1142/S0219477524400261.
    Paper not yet in RePEc: Add citation now
    
36. Saâdaoui, F., & Rabbouch, H. (2014). A wavelet‐based multiscale vector‐ANN model to predict comovement of econophysical systems. Expert Systems with Applications, 41(13), 6017–6028.
    Paper not yet in RePEc: Add citation now
    

37. Shrestha, K. (2021). Multifractal detrended fluctuation analysis of return on Bitcoin. International Review of Finance, 21(1), 312–323.

38. 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.

39. Takaishi, T. (2018). Statistical properties and multifractality of Bitcoin. Physica A: Statistical Mechanics and its Applications, 506, 507–519.

40. Takala, K., & Virén, M. (1996). Chaos and nonlinear dynamics in financial and nonfinancial time series: Evidence from Finland. European Journal of Operational Research, 93(1), 155–172.

41. Tang, W. H., & Röllin, A. (2021). Model identification for ARMA time series through convolutional neural networks. Decision Support Systems, 146, 113544.
    Paper not yet in RePEc: Add citation now
    

42. Telli, S., & Chen, H. (2020). Multifractal behavior in return and volatility series of Bitcoin and gold in comparison. Chaos, Solitons and Fractals, 139, 109994.

43. Telli, S., & Chen, H. (2021). Multifractal behavior relationship between crypto markets and Wikipedia‐Reddit online platforms. Chaos, Solitons and Fractals, 152, 111331.

44. Velasco, C. (1999). Non‐stationary log‐periodogram regression. Journal of Econometrics, 91(2), 325–371.
    Paper not yet in RePEc: Add citation now
    
45. Walasek, R., & Gajda, J. (2021). Fractional differentiation and its use in machine learning. International Journal of Advances in Engineering Sciences and Applied Mathematics, 13, 270–277.
    Paper not yet in RePEc: Add citation now
    

46. Wu, L., Chen, L., Ding, Y., & Zhao, T. (2018). Testing for the source of multifractality in water level records. Physica A: Statistical Mechanics and its Applications, 508, 824–839.

47. Xu, N., Li, S., & Hui, X. (2021). Multifractal analysis of COVID‐19's impact on China's stock market. Fractals, 29(7), 2150.
    Paper not yet in RePEc: Add citation now
    
48. Yi, E., Ahn, K., & Choi, M. Y. (2022). Cryptocurrency: Not far from equilibrium. Technological Forecasting and Social Change, 177, 121424.
    Paper not yet in RePEc: Add citation now
    
49. Yolcu, U., Egrioglu, E., & Aladag, C. E. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 54(3), 1340–1347.
    Paper not yet in RePEc: Add citation now
    

50. Yousaf, I., Patel, R., & Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia‐Ukraine conflict “black‐swan” event: Evidence from event study approach. Journal of Behavioral and Experimental Finance, 35, 100723.

51. Zhang, X., Yang, L., & Zhu, Y. (2019). Analysis of multifractal characterization of Bitcoin market based on multifractal detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 523, 973–983.

1. Structured multifractal scaling of the principal cryptocurrencies: Examination using a self‐explainable machine learning. (2024). Rabbouch, Hana ; Saadaoui, Foued.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:43:y:2024:i:7:p:2917-2934.

   Full description at Econpapers || Download paper

2. Bayesian nonlinear expectation for time series modelling and its application to Bitcoin. (2023). Siu, Tak Kuen.
   In: Empirical Economics.
   RePEc:spr:empeco:v:64:y:2023:i:1:d:10.1007_s00181-022-02255-z.

   Full description at Econpapers || Download paper

3. Investor sentiment and bitcoin prices. (2023). Koutmos, Dimitrios.
   In: Review of Quantitative Finance and Accounting.
   RePEc:kap:rqfnac:v:60:y:2023:i:1:d:10.1007_s11156-022-01086-4.

   Full description at Econpapers || Download paper

4. Ex-post facto analysis of cryptocurrency literature over a decade using bibliometric technique. (2023). Tiwari, Aviral ; Hassan, M. Kabir ; Devji, Shridev ; Pattnaik, Debidutta ; Dsouza, Arun.
   In: Technological Forecasting and Social Change.
   RePEc:eee:tefoso:v:189:y:2023:i:c:s0040162523000240.

   Full description at Econpapers || Download paper

5. Structured Multifractal Scaling of the Principal Cryptocurrencies: Examination using a Self-Explainable Machine Learning. (2023). Saadaoui, Foued.
   In: Papers.
   RePEc:arx:papers:2304.08440.

   Full description at Econpapers || Download paper

6. Score-based calibration testing for multivariate forecast distributions. (2023). Knüppel, Malte ; Pohle, Marc-Oliver ; Knuppel, Malte ; Kruger, Fabian.
   In: Papers.
   RePEc:arx:papers:2211.16362.

   Full description at Econpapers || Download paper

7. Forecasting financial markets with semantic network analysis in the COVID-19 crisis. (2023). Violante, Francesco ; Ravazzolo, Francesco ; Grassi, S ; Colladon, Fronzetti A.
   In: Papers.
   RePEc:arx:papers:2009.04975.

   Full description at Econpapers || Download paper

8. Score-based calibration testing for multivariate forecast distributions. (2022). Knüppel, Malte ; Pohle, Marc-Oliver ; Knuppel, Malte ; Kruger, Fabian.
   In: Discussion Papers.
   RePEc:zbw:bubdps:502022.

   Full description at Econpapers || Download paper

9. Which predictor is more predictive for Bitcoin volatility? And why?. (2022). Zhang, Yaojie ; Li, Xiafei ; Ma, Feng ; Liang, Chao.
   In: International Journal of Finance & Economics.
   RePEc:wly:ijfiec:v:27:y:2022:i:2:p:1947-1961.

   Full description at Econpapers || Download paper

10. Robust estimation of time-dependent precision matrix with application to the cryptocurrency market. (2022). Stolfi, Paola ; Bernardi, Mauro ; Vergni, Davide.
    In: Financial Innovation.
    RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00355-4.

    Full description at Econpapers || Download paper

11. Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading. (2022). Li, Yuze ; Wang, Shouyang ; Jiang, Shangrong.
    In: Financial Innovation.
    RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00336-7.

    Full description at Econpapers || Download paper

12. Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models. (2022). GUPTA, RANGAN ; Bouri, Elie ; Christou, Christina.
    In: Working Papers.
    RePEc:pre:wpaper:202213.

    Full description at Econpapers || Download paper

13. Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle. (2022). Magner, Nicolas ; Hardy, Nicolas.
    In: Mathematics.
    RePEc:gam:jmathe:v:10:y:2022:i:13:p:2338-:d:855300.

    Full description at Econpapers || Download paper

14. Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns. (2022). Guidolin, Massimo ; Pedio, Manuela.
    In: Forecasting.
    RePEc:gam:jforec:v:4:y:2022:i:1:p:16-306:d:752601.

    Full description at Econpapers || Download paper

15. 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

16. Investors sentiments and the dynamic connectedness between cryptocurrency and precious metals markets. (2022). Oyewole, Oluwatomisin ; Oliyide, Johnson A ; Fasanya, Ismail O.
    In: The Quarterly Review of Economics and Finance.
    RePEc:eee:quaeco:v:86:y:2022:i:c:p:347-364.

    Full description at Econpapers || Download paper

17. Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models. (2022). GUPTA, RANGAN ; Bouri, Elie ; Christou, Christina.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322003993.

    Full description at Econpapers || Download paper

18. Environmental and economic assessment of the efficiency of heat exchanger network retrofit options based on the experience of society and energy price records. (2022). Gil, Tatyana ; Boldyryev, Stanislav ; Ilchenko, Mariia.
    In: Energy.
    RePEc:eee:energy:v:260:y:2022:i:c:s0360544222020497.

    Full description at Econpapers || Download paper

19. PREDICTIVE MODELLING OF SELECT CRYPTOCURRENCIES AND IDENTIFYING THE BEST SUITABLE MODEL - WITH REFERENCE TO ARIMA AND ANNS. (2022). Sharma, Sudhi ; Reepu, Prof ; Dhyani, Prof Bijesh ; Ayushi, MS ; Kumar, Manish.
    In: Annals - Economy Series.
    RePEc:cbu:jrnlec:y:2022:v:6:p:11-19.

    Full description at Econpapers || Download paper

20. Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach. (2022). Ciganovic, Milos ; D'Amario, Federico.
    In: Papers.
    RePEc:arx:papers:2210.00883.

    Full description at Econpapers || Download paper

21. Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. (2021). Neslihanoglu, Serdar.
    In: Financial Innovation.
    RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00247-z.

    Full description at Econpapers || Download paper

22. Forecasting and trading cryptocurrencies with machine learning under changing market conditions. (2021). Sebastião, Helder ; Godinho, Pedro ; Sebastio, Helder.
    In: Financial Innovation.
    RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-020-00217-x.

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23. 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

24. Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis. (2021). el Zein, Samer Ajour ; Lansdowne, Nicola Jackman ; Rudolf, Karl Oton.
    In: Risks.
    RePEc:gam:jrisks:v:9:y:2021:i:9:p:154-:d:622085.

    Full description at Econpapers || Download paper

25. Interplay between Cryptocurrency Transactions and Online Financial Forums. (2021). Diaz, Rebeca P ; Cancela, Daniel Couto ; Vilas, Ana Fernandez ; Pazos, Alejandro Torrado.
    In: Mathematics.
    RePEc:gam:jmathe:v:9:y:2021:i:4:p:411-:d:502476.

    Full description at Econpapers || Download paper

26. Bitcoin and Fiat Currency Interactions: Surprising Results from Asian Giants. (2021). Muhammed, Shahnawaz ; Gunay, Samet ; Kaskaloglu, Kerem.
    In: Mathematics.
    RePEc:gam:jmathe:v:9:y:2021:i:12:p:1395-:d:575985.

    Full description at Econpapers || Download paper

27. Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China. (2021). Wong, Wing-Keung ; van Hoang, Thi Hong ; Ly, Sel ; Lu, Richard ; Pho, Kim Hung.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:74:y:2021:i:c:s105752192100017x.

    Full description at Econpapers || Download paper

28. The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets. (2021). Urquhart, Andrew ; Manahov, Viktor.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:73:y:2021:i:c:s1057521920302726.

    Full description at Econpapers || Download paper

29. 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

30. Returns, volatility and the cryptocurrency bubble of 2017–18. (2021). Cross, Jamie ; Trinh, Kelly ; Hou, Chenghan.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:104:y:2021:i:c:s0264999321002327.

    Full description at Econpapers || Download paper

31. Dynamic Ordering Learning in Multivariate Forecasting. (2021). Lopes, Hedibert F.
    In: Papers.
    RePEc:arx:papers:2101.04164.

    Full description at Econpapers || Download paper

32. Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach. (2020). Ma, Yuanhui ; M. I. M. Wahab, ; Liang, Chao.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:8:p:1277-1290.

    Full description at Econpapers || Download paper

33. Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach. (2020). Kassim, Salina ; Rosman, Romzie Bin ; Binti, Nur Harena ; Abdullah, Anwar Hasan.
    In: Journal of Revenue and Pricing Management.
    RePEc:pal:jorapm:v:19:y:2020:i:5:d:10.1057_s41272-020-00229-3.

    Full description at Econpapers || Download paper

34. Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations. (2020). Younas, Zahid Irshad ; Jeleskovic, Vahidin ; Meloni, Mirko.
    In: MAGKS Papers on Economics.
    RePEc:mar:magkse:202034.

    Full description at Econpapers || Download paper

35. Lead Behaviour in Bitcoin Markets. (2020). Trimborn, Simon ; Giudici, Paolo ; Chen, Ying ; Misheva, Branka Hadji.
    In: Risks.
    RePEc:gam:jrisks:v:8:y:2020:i:1:p:4-:d:305277.

    Full description at Econpapers || Download paper

36. Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables. (2020). Kashef, Rasha ; Li, Menglu ; Valencia, Esteban ; Huang, Eric ; Ibrahim, Ahmed.
    In: JRFM.
    RePEc:gam:jjrfmx:v:13:y:2020:i:9:p:189-:d:401211.

    Full description at Econpapers || Download paper

37. Cryptocurrency Trading Using Machine Learning. (2020). Koker, Thomas E ; Koutmos, Dimitrios.
    In: JRFM.
    RePEc:gam:jjrfmx:v:13:y:2020:i:8:p:178-:d:396989.

    Full description at Econpapers || Download paper

38. Does Bitcoin Hedge Commodity Uncertainty?. (2020). Nguyen, Thang ; Hoang, Khanh ; Poch, Kongchheng.
    In: JRFM.
    RePEc:gam:jjrfmx:v:13:y:2020:i:6:p:119-:d:369078.

    Full description at Econpapers || Download paper

39. Gold, platinum, and expected Bitcoin returns. (2020). Burggraf, Tobias ; Duc, Toan Luu ; Wang, Mei.
    In: Journal of Multinational Financial Management.
    RePEc:eee:mulfin:v:56:y:2020:i:c:s1042444x20300177.

    Full description at Econpapers || Download paper

40. A novel two-stage approach for cryptocurrency analysis. (2020). Sun, Yuying ; Wang, Shouyang ; Yang, Boyu.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:72:y:2020:i:c:s1057521920302118.

    Full description at Econpapers || Download paper

41. Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market. (2020). Bekiros, Stelios ; Lahmiri, Salim.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:133:y:2020:i:c:s0960077920300400.

    Full description at Econpapers || Download paper

42. Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks. (2020). Urquhart, Andrew ; Shen, Dehua ; Wang, Pengfei.
    In: European Financial Management.
    RePEc:bla:eufman:v:26:y:2020:i:5:p:1294-1323.

    Full description at Econpapers || Download paper

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

    Full description at Econpapers || Download paper

44. Blockchain adoption at German logistics service providers. (2019). Kuhn, Oliver ; Schuller, Michael ; Jacob, Axel.
    In: Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL).
    RePEc:zbw:hiclch:209379.

    Full description at Econpapers || Download paper

45. Co-movement in crypto-currency markets: evidences from wavelet analysis. (2019). S Kumar, Anoop ; Ajaz, Taufeeq.
    In: Financial Innovation.
    RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-019-0143-3.

    Full description at Econpapers || Download paper

46. Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models. (2019). Rossini, Luca ; Bohte, Rick.
    In: JRFM.
    RePEc:gam:jjrfmx:v:12:y:2019:i:3:p:150-:d:268406.

    Full description at Econpapers || Download paper

47. Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?. (2019). Grassi, Stefano ; Santabarbara, Luca ; Muglia, Camilla.
    In: JRFM.
    RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:93-:d:235917.

    Full description at Econpapers || Download paper

48. Bitcoin at High Frequency. (2019). Sandholdt, Mads ; Catania, Leopoldo.
    In: JRFM.
    RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:36-:d:206409.

    Full description at Econpapers || Download paper

49. Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. (2019). Bekiros, Stelios ; Karasu, Sekin ; Altan, Ayta.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:126:y:2019:i:c:p:325-336.

    Full description at Econpapers || Download paper

50. A joint multifractal analysis of vector valued non Gibbs measures. (2019). Menceur, Mohamed ; ben Mabrouk, Anouar.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:126:y:2019:i:c:p:203-217.

    Full description at Econpapers || Download paper

51. A New Economic Framework: A DSGE Model with Cryptocurrency. (2019). Ravazzolo, Francesco ; Lorusso, Marco ; Asimakopoulos, Stylianos.
    In: Working Papers.
    RePEc:bny:wpaper:0079.

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52. Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models. (2019). Rossini, Luca ; Bohte, Rick.
    In: Papers.
    RePEc:arx:papers:1909.06599.

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