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Regime heteroskedasticity in Bitcoin: A comparison of Markov switching models

Daniel Chappell

MPRA Paper from University Library of Munich, Germany

Abstract: Markov regime-switching (MRS) models, also known as hidden Markov models (HMM), are used extensively to account for regime heteroskedasticity within the returns of financial assets. However, we believe this paper to be one of the first to apply such methodology to the time series of cryptocurrencies. In light of Molnar and Thies (2018) demonstrating that the price data of Bitcoin contained seven distinct volatility regimes, we will �fit a sample of Bitcoin returns with six m-state MRS estimations, with m between 2 and 7. Our aim is to identify the optimal number of states for modelling the regime heteroskedasticity in the price data of Bitcoin. Goodness-of-�fit will be judged using three information criteria, namely: Bayesian (BIC); Hannan-Quinn (HQ); and Akaike (AIC). We determined that the restricted 5-state model generated the optimal estimation for the sample. In addition, we found evidence of volatility clustering, volatility jumps and asymmetric volatility transitions whilst also inferring the persistence of shocks in the price data of Bitcoin.

Keywords: Bitcoin; Markov regime-switching; regime heteroskedasticity; volatility transitions. (search for similar items in EconPapers)
JEL-codes: C01 C22 C26 C50 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-ore and nep-pay
Date: 2018-09-28
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