221 International Journal of Academic Research in Accounting, Finance and Management Sciences Vol. 9, No.3, July 2019, pp. 221–229 E-ISSN: 2225-8329, P-ISSN: 2308-0337 © 2019 HRMARS www.hrmars.com To cite this article: Hamida, H.B.H., Scalera, F. (2019). Threshold Mean Reversion and Regime Changes of Cryptocurrencies using SETAR-MSGARCH Models, International Journal of Academic Research in Accounting, Finance and Management Sciences 9 (3): 221-229 http://dx.doi.org/10.6007/IJARAFMS/v9-i3/6365 (DOI: 10.6007/IJARAFMS/v9-i3/6365) Threshold Mean Reversion and Regime Changes of Cryptocurrencies using SETAR-MSGARCH Models Hayet Ben Haj Hamida 1 , Francesco Scalera 2 1 Quantitative Department University, Carthage, Institute of Higher Commercial Studies of Carthage (IHEC), Tunisia University, Tunis El Manar, Faculty of Economics and Management of Tunis (FSEGT), Prospective, Strategies and Sustainable Development (PS2D), Tunis 2092, Tunisia, 1 E-mail: hayet.bhh@gmail.com 2 Department of Economics and Finance, University of Bari ‘Aldo Moro’, Bari, Italy, Administrative Director of the Research Center CEDIMES Paris, France, 2 E-mail: francesco.scalera@uniba.it Abstract In this paper we explores as to whether cryptocurrency returns exhibit asymmetric reverting patterns and we test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log–returns. For these reason, we uses non-linear autoregressive and Markov–switching GARCH (SETAR-MSGARCH) models. We finds strong evidence of regime changes in the mean and GARCH process. In addition, we conclude that bad news and good news of the same size have same impacts for investors. Key words Bitcoin, GARCH, Non-linear Autoregressive, Markov-Switching Received: 15 Sep 2019 © The Authors 2019 Revised: 24 Sep 2019 Published by Human Resource Management Academic Research Society (www.hrmars.com) This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode Accepted: 29 Sep 2019 Published Online: 09 Oct 2019 1. Introduction Since the global financial crisis of 2008, the international regulatory of the Basel III for bank has imposed more stringent capital requirements, and risk management systems have been developed. In fact, modelling volatility for risk management became crucial, and the international financial system has to face a new challenge evolving the introduction and development of decentralised cryptocurrencies. A cryptocurrency can be defined as “a digital asset designed to work as a medium of exchange using cryptography to secure the transactions and to control the creation of additional units of the currency” (Victor, 2017). Nakamoto (2008) designed the first decentralized cryptocurrency, Bitcoin, based on block chain technology. Bitcoin has received much attention in the media and by investors, given its innovative feature, simplicity, transparency and its increasing popularity. Bitcoin and its derivatives use decentralized control as opposed to centralized electronic money/centralized banking systems. The decentralized control is related to the use of bitcoin’s blockchain transaction database in the role of a distributed ledger. Nam, Pyun, and Arize (2002) indicated that stock market overreaction has been associated for a significant period with mean reversion of stock market price. Indeed, the overreaction of investors is based to new events and it can be determined from the assumption that a stock’price will tend to move to the average price overtime (Corbet and Katsiampa, 2018; Ahmed et al., 2018). The sharp appreciation in the price of cryptocurrency has been accurate on the presence of a substantial pricing bubble (Corbet et al., 2018a).