References contributed by 3598184_221115531256941
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. DOI: 10.1016/j.frl.2018.08.010.
Aganin A., Manevich V., Peresetsky A., Pogorelova P. (2023). Comparison of cryptocurrency and stock market volatility forecast models. HSE Economic Journal, 27 (1), 49–77. (in Russian). DOI: 10.17323/1813-8691-2023-27-1-49-77.
Agyei S. K., Adam A. M., Bossman A., Asiamah O., Owusu Junior P., Asafo-Adjei R., Asafo-Adjei E. (2022). Does volatility in cryptocurrencies drive the interconnectedness between the cryptocurrencies market? Insights from wavelets. Cogent Economics and Finance, 10 (1), DOI: 10.1080/23322039.2022.2061682.
Andersen T. G., Bollerslev T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 38 (4), 885–905. DOI: 10.2307/2527343.
Barndorff-Nielsen O. E., Shephard N. (2004). Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics. Econometrica, 72 (3), 885–925. DOI: 10.1111/j.1468-0262.2004.00515.x.
- Bergsli L. Ø., Lind A. F., Molnár P., Polasik M. (2022). Forecasting volatility of Bitcoin. Research in International Business and Finance, 59, 101540. DOI: 10.1016/j.ribaf.2021.101540.
Paper not yet in RePEc: Add citation now
Bouri E., Gkillas K., Gupta R., Pierdzioch C. (2021). Forecasting realized volatility of Bitcoin: The role of the trade war. Computational Economics, 57, 29–53. DOI: 10.1007/s10614-020-10022-4.
Brauneis A., Mestel R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58–61. DOI: 10.1016/j.econlet.2018.02.001.
Chen R. (2024). Forecasting Ethereum’s volatility: An expansive approach using HAR models and structural breaks. Cogent Economics and Finance, 12 (1), 2300925. DOI: 10.1080/23322039.2023.2300925.
Chiriac R., Voev V. (2011). Modelling and forecasting multivariate realized volatility. Journal of Applied Econometrics, 26 (6), 922–947. DOI: 10.1002/jae.1152.
Clements A., Preve D. P. (2021). A practical guide to harnessing the har volatility model. Journal of Banking and Finance, 133, 106285. DOI: 10.1016/j.jbankfin.2021.106285.
Corsi F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7 (2), 174–196. DOI: 10.1093/jjfinec/nbp001.
- Dudek G., Fiszeder P., Kubus P., Orzeszko W. (2024). Forecasting cryptocurrencies volatility using statistical and machine learning methods: A comparative study. Applied Soft Computing, 151 (2), 111132. DOI: 10.1016/j.asoc.2023.111132.
Paper not yet in RePEc: Add citation now
- Dyhrberg A. H. (2016). Bitcoin, gold and the dollar — A GARCH volatility analysis. Finance Research Letters, 16, 85–92. DOI: 10.1016/j.frl.2015.10.008.
Paper not yet in RePEc: Add citation now
Engle R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50 (4), 987–1007. DOI: 10.2307/1912773.
- Engle R. F., Bollerslev T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5 (1), 1–50. DOI: 10.1080/07474938608800095.
Paper not yet in RePEc: Add citation now
Fantazzini D., Kolodin N. (2020). Does the hashrate affect the bitcoin price? Journal of Risk and Financial Management, 13 (11), 263. DOI: 10.3390/jrfm13110263.
Fantazzini D., Nigmatullin E., Sukhanovskaya V., Ivliev S. (2016). Everything you always wanted to know about bitcoin modelling but were afraid to ask. Applied Econometrics, 44, 5–24. (In Russian).
- Gyamerah S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quantitative Finance and Economics, 3 (4), 739–753. DOI: 10.3934/QFE.2019.4.739.
Paper not yet in RePEc: Add citation now
Hansen P. R., Lunde A., Nason J. M. (2011). The model confidence set. Econometrica, 79 (2), 453–497. DOI: 10.3982/ECTA5771.
Harb E., Bassil C., Kassamany T., Baz R. (2024). Volatility interdependence between cryptocurrencies, equity, and bond markets. Computational Economics, 63 (3), 951–981. DOI: 10.1007/s10614-022-10318-7.
Liang C., Zhang Y., Li X., Ma F. (2022). Which predictor is more predictive for Bitcoin volatility? And why? International Journal of Finance and Economics, 27 (2), 1947–1961. DOI: 10.1002/ijfe.2252.
Liu C., Maheu J. M. (2008). Are there structural breaks in realized volatility? Journal of Econometrics, 187 (1), 293–311. DOI: 10.1016/j.jeconom.2015.02.008.
Liu J., Serletis A. (2019). Volatility in the cryptocurrency market. Open Economies Review, 30 (4), 779–811. DOI:10.1007/s11079-019-09547-5.
Liu L. Y., Patton A. J., Sheppard K. (2015). Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. Journal of Econometrics, 187 (1), 293–311. DOI: 10.1016/j.jeconom.2015.02.008.
- Ma F., Wei Y., Huang D., Chen Y. (2014). Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility. Physica A: Statistical Mechanics and Its Applications, 405, 171–180. DOI 10.1016/j.physa.2014.03.007.
Paper not yet in RePEc: Add citation now
- Manevich V., Ignatov D. (2023). Machine learning, neural networks and econometric models for prediction the realized volatility of Bitcoin and E-Mini S&P500. SSRN: 4334006. DOI: 10.2139/ssrn.4334006.
Paper not yet in RePEc: Add citation now
Manevich V., Peresetsky A., Pogorelova P. (2022). Stock market and cryptocurrency market volatility. Applied Econometrics, 65, 65–76. (in Russian). DOI: 10.22394/1993-7601-2022-65-65-76.
- Mastro D. (2014). Forecasting realized volatility: ARCH-type models vs. the HAR-RV model. SSRN 2519107. DOI: 10.2139/ssrn.2519107.
Paper not yet in RePEc: Add citation now
Mens W., Al-Yahyaee K. H., Kang S. H. (2019). Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum. Finance Research Letters, 29, 222–230. DOI: 10.1016/j.frl.2018.07.011.
Nelson D. B. (1994). Asymptotic filtering theory for multivariate ARCH models. Journal of Econometrics, 7 (1–2), 1–47. DOI: 10.1016/0304-4076(94)01679-8.
Rabemananjara R., Zakoian J.-M. (1993). Threshold ARCH models and asymmetries in volatility. Journal of Applied Econometrics, 8 (1), 31–49. 10.1002/jae.3950080104.
Teterin M. A., Peresetsky A. A. (2024). Google Trends and Bitcoin volatility forecast. Journal of the New Economic Association, 4 (65).