Forecasting volatility in bitcoin market

Aiolfi, M., Timmermann, A.: Persistence in forecasting performance and conditional combination strategies. J Econ 135, 31–53 (2006). https://doi.org/10.1016/j.jeconom.2005.07.015

Ardia, D., Bluteau, K., Rüede, M.: Regime changes in Bitcoin GARCH volatility dynamics. Finance Res Lett 29, 266–271 (2019)

Balcombe, K., Fraser, I.: Do bubbles have an explosive signature in markov switching models? Econ Model 66, 81–100 (2017)

Bariviera, A.F.: The inefficiency of Bitcoin revisited: a dynamic approach. Econ Lett 161, 1–4 (2017)

Bariviera, A.F., Basgall, M.J., Hasperué, W., Naiouf, M.: Some stylized facts of the Bitcoin market. Phys A Stat Mech Appl 484, 82–90 (2017)

Barndorff-Nielsen, O.E., Prause, K.: Apparent scaling. Finance Stoch 5, 103–113 (2001)

Bau, D.G., Dimpfl, T., Kuck, K.: Bitcoin, gold and the dollar–a replication and extension. Finance Res Lett 25, 103–110 (2018)

Begušić, S., Kostanjčar, Z., Stanley, H.E., Podobnik, B.: Scaling properties of extreme price fluctuations in Bitcoin markets. Phys A Stat Mech Appl 510, 400–406 (2018)

Beran J.: Statistics for Long-memory Processes: New York: Chapman and Hall (1994)

Berkowitz, J.: Testing density forecasts, with application to risk management. J Bus Econ Stat 12, 465–474 (2001)

Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J Econ 31, 307–327 (1986)

Bollerslev, T., Engle, R.F., Nelson, D.: Handbook of Econometrics. In: ARCH models, vol. 4, pp. 2961–3038, Amsterdam: Elsevier Science BV (1994)

Bougerol, P., Picard, N.: Stationarity of GARCH processes and of some nonnegative time series. J Econ 52, 115–127 (1992)

Bouri, E., Azzi, G., Dyhrberg, A.H.: On the return-volatility relationship in the Bitcoin market around price crash of 2013. Economics 11, 1–17 (2017)

Calvet, L., Fisher, A.: Forecasting multifractal volatility. J Econ 105, 27–58 (2001)

Calvet, L., Fisher, A.: Regime-switching and the estimation of multifractal processes. J Financ Econ 2, 44–83 (2004)

Catania, L., Grassi, S.: Modelling crypto-currencies financial time-series. CEIS Research Paper 417, Tor Vergata University, CEIS (2017). https://ideas.repec.org/p/rtv/ceisrp/417.html

Chu, J., Chan, S., Nadarajah, S., Osterrieder, J.: GARCH modelling of cryptocurrencies. J Risk Financ Manage 10, 1–15 (2017)

Conrad, C.: Non-negativity conditions for the hyperbolic GARCH model. J Econ 157, 441–457 (2010)

Conrad, C., Haag, B.R.: Inequality constraints in the fractionally integrated GARCH model. J Financ Econ 4, 413–449 (2006)

Davidson, J.: Moment and memory properties of linear conditional heteroscedasticity models, and a new model. J Bus Econ Stat 22, 16–29 (2004)

Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74, 427–431 (1979)

Diebold, F., Gunther, T., Tay, A.: Evaluating density forecasts with application to financial risk management. Int Econ Rev 39, 863–883 (1998)

Ding, Z., Granger, C., Engle, R.: A long memory property of stock market returns and a new model. J Empir Finance 1, 83–106 (1993)

Dowd, K.: A modified Berkowitz backtest. Risk 17, 86–87 (2004)

Glosten, L., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and volatility of the nominal excess return on stocks. J Finance 46, 1779–1801 (1993)

Granger, C.W., Teräsvirta, T.: A simple nonlinear time series model with missleading linear properties. Econ Lett 62, 161–165 (1999)

Granger, C.W.J., Joyeux, R.: An introduction to long-memory time series models and fractional differencing. J Time Series Anal 1, 15–29 (1980)

Haas, M., Mittnik, S., Paolella, M.S.: A new approach to Markov-switching GARCH models. J Financ Econ 2, 493–530 (2004)

Hansen, P.R., Lunde, A., Nason, J.M.: The model confidence set. Econometrica 79, 453–497 (2011)

Harvey, D.I., Leybourne, S.J., Newbold, P.: Tests for forecast encompassing. J Bus Econ Stat 16, 254–259 (1998)

Hentschel, L.: All in the family nesting symmetric and asymmetric GARCH models. J Financ Econ 39, 71–104 (1995)

Hosking, J.R.: Fractional differencing. Biometrika 68, 165–176 (1981)

Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., Stanley, H.E.: Multifractal detrented fluctuation analysis of nonstationary time series. Phys A 316, 87–114 (2002)

Katsiampa, P.: Volatility estimation for Bitcoin: a comparison of GARCH models. Econ Lett 158, 3–6 (2017)

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

Lahmiri, S., Bekiros, S., Salvi, A.: Long-range memory, distribution variation and randomness of bitcoin voaltility. Chaos, Solitions Fractals 107, 43–48 (2018)

Ling, S.: On the probabilistic properties of a double threshold ARMA conditional heteroskedasticity model. J Appl Prob 36, 1–18 (1999)

Ling, S., Li, W.: On fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity. J Am Stat Assoc 92, 1184–1194 (1997)

Ling, S., McAleer, M.: Necessary and sufficient moment conditions for the GARCH(r, s) and asymmetric power GARCH(r, s) models. Econ Theory 18, 722–729 (2002)

Ling, S., McAleer, M.: Stationary and the existence of moments of a GARCH processes. J Econ 106, 109–117 (2002)

Liu, J.C.: Stationarity of a markov-switching GARCH model. J Financ Econ 4, 573–593 (2006)

Liu, R., di Matteo, T., Lux, T.: True and apparent scaling:the proximity of the Markov-switching multifractal model to long-range dependence. Phys A 383, 35–42 (2007)

Lux, T.: Detecting multi-fractal properties in asset returns. Int J Mod Phys 15, 481–491 (2004)

Lux, T.: The Markov-switching multifractal model of asset returns: GMM estimation and linear forecasting of volatility. J Bus Econ Stat 26, 194–210 (2008)

Lux, T., Morales-Arias, L.: Forecasting volatility under fractality, regime-switching, long memory and Student-$$t$$ innovations. Comput Stat Data Anal 54, 2676–2692 (2010)

Lux, T., Segnon, M., Gupta, R.: Forecasting crude oil price volatility and value-at-risk: evidence from historical and recent data. Energy Econ 56, 117–133 (2016)

Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system. Prequel Books (2011). https://bitcoin.org/bitcoin.pdf

Nelson, D.B.: Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59, 347–370 (1991)

Osterrieder, J., Lorenz, J.: A statistical risk assessment of Bitcoin and its extreme tail behaviour. Ann Financ Econ 12, 175000 (2017)

Peng, C., Buldyrev, S., Havlin, S., Simons, M., Stanley, H., Goldberger, A.: Mosaic organization of DNA nucleotides. Phys. Rev. E 49, 1685–1689 (1994)

Pesaran, M.H., Timmermann, A.: Selection of estimation window in the presence of breaks. J Econ 137, 134–161 (2007)

Phillip, A., Chan, J.S.K., Shelton, P.: A new look at cryptocurrencies. Econ Lett 163, 6–9 (2018)

Pichl, L., Kaizoji, T.: Volatility analysis of bitcoin time series. Quant Finance Econ 1, 474–485 (2017)

Rosenblatt, R.F.: Remarks on a multivariate transformation. Ann Math Stat 23, 470–472 (1952)

Sansó, A., Arragó, V., Carrion, J.L.: Testing for change in the unconditional variance of financial time series. Rev de Econ Financ 4, 32–53 (2004)

Shiryaev, A.: Probability (Graduate Texts in Mathematics), 2nd edn. Springer, Verlag (1995)

Stavroyiannis, S.: Value-at-risk and related measures for the Bitcoin. J Risk Finance 19, 127–136 (2018)

Thies, S., Molnár, P.: Bayesian change point analysis of Bitcoin returns. Finance Res Lett 27, 223–227 (2018)