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Alternative distribution based GARCH models for Bitcoin volatility estimation

Mattera, Raffaele; Giacalone, Massimiliano - In: The empirical economics letters : a monthly … 17 (2018) 11, pp. 1283-1288

Persistent link: https://www.econbiz.de/10012006849

Volatility estimation for cryptocurrencies using Markov-switching GARCH models

Silva, Paulo Vitor Jordão da Gama; Klotzle, Marcelo Cabus - In: International Journal of Financial Markets and … 7 (2019) 1, pp. 1-14

Persistent link: https://www.econbiz.de/10012253524

Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models

Phung Duy Quang; Oanh Nguyen Thi; Phuong Hao Le Thi; … - In: Business analyst journal : BAJ 45 (2024) 1, pp. 11-23

Persistent link: https://www.econbiz.de/10015188090

Volatility estimation for Bitcoin : a comparison of GARCH models

Katsiampa, Paraskevi - In: Economics letters 158 (2017), pp. 3-6

Persistent link: https://www.econbiz.de/10011849728

Exogenous drivers of Bitcoin and Cryptocurrency volatility : a mixed data sampling approach to forecasting

Walther, Thomas; Klein, Tony; Bouri, Elie - In: Journal of international financial markets, … 63 (2019), pp. 1-13

Persistent link: https://www.econbiz.de/10012263290

Forecasting Bitcoin risk measures : a robust approach

Trucíos, Carlos - In: International journal of forecasting 35 (2019) 3, pp. 836-847

Persistent link: https://www.econbiz.de/10012305182

Volatility forecasting accuracy for Bitcoin

Köchling, Gerrit; Schmidtke, Philipp; Posch, Peter N. - In: Economics letters 191 (2020), pp. 1-5

Persistent link: https://www.econbiz.de/10012507996

Modelling volatility dynamics of cryptocurrencies using GARCH models

Ngunyi, Anthony; Mundia, Simon; Omari, Cyprian Ondieki - In: Journal of mathematical finance 9 (2019) 4, pp. 591-615

Persistent link: https://www.econbiz.de/10012433128

Regime changes in Bitcoin GARCH volatility dynamics

Ardia, David; Bluteau, Keven; Rüede, Maxime - In: Finance research letters 29 (2019), pp. 266-271

Persistent link: https://www.econbiz.de/10012419095

Exogenous drivers of cryptocurrency volatility : a mixed data sampling approach to forecasting

Walther, Thomas; Klein, Tony - 2018

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized … Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction … quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global …

Persistent link: https://www.econbiz.de/10011906495