Modelling the volatility of Bitcoin returns using GARCH models

Gyamerah, Samuel Asante

doi:10.3934/qfe.2019.4.739

Cited by 48 publications

(18 citation statements)

References 5 publications

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“…Our results evidenced the superiority of the IGARCH model in forecasting the volatility of world currencies, and revealed that the volatilities of cryptocurrencies are better vindicated by advanced models mainly the CGARCH, GJR-GARCH, APARCH, and TGARCH. This conforms to the findings of Gyamerah [27], who concluded that the TGARCH alternative is the best model to forecast time-varying volatility in Bitcoin, and of Katsiampa [7], who found that the best conditional heteroscedasticity model for Bitcoin is the AR-CGARCH. On the other hand, our results contradict those of Holtappels [11], Abdalla [28] and Naimy & Hayek [9], who highlighted the superiority of EGARCH in modelling the Bitcoin's volatility.…”

Section: Discussionsupporting

confidence: 90%

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

2021

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This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

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Section: Discussionsupporting

confidence: 90%

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

2021

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“…The empirical literature related to cryptocurrency volatility modelling and forecasting is abundant, with a strand of literature adopting the classical time series models, particularly the generalized autoregressive conditional heteroscedasticity (GARCH) family of models. In this literature, some studies investigated the cryptocurrency volatility modelling based on the in-sample forecasting strategy, (Balcilar et al, 2017;Charles & Darné 2019;Cheikh et al, 2020;Chu et al, 2017;Conrad et al, 2018;Dyhrberg, 2016;Katsiampa, 2017;Naimy & Hayek, 2018;Pichl & Kaizoji, 2017;Gyamerah, 2019;Tiwari et al, 2019, among others), and some assessed volatility forecasting based on out-of-sample strategy for a specific forecasting horizon (Bezerra & Albuquerque, 2017;Catani et al, 2019;Naimy & Hayek, 2018;Peng et al, 2018;Xiao & Sun, 2020, among others). This corpus uses the conventional time series models like the GARCH family models, which were extended recently in light of outliers that characterize cryptocurrency markets (Aslan & Sensoy, 2020;Charles & Darné, 2019;Catani et al, 2019;Trucíos, 2019, among others).…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

2021

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This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

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“…On the other hand, the price of Bitcoin, which is one of speculative assets, can be influenced by information acquisition and propagation rate. Considering the instantaneity of information, highly frequent data may better reflect the effects of information on the Bitcoin price volatility [33,57]. Accordingly, we use the realized volatility, proposed by Ref.…”

Section: Network Connectedness Of Common and Idiosyncratic Volatilitymentioning

confidence: 99%

Dynamic Network Connectedness of Bitcoin Markets: Evidence from Realized Volatility

Chen

Dong

2020

Front. Phys.

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In this paper, we explore the volatility spillovers across different Bitcoin markets. We decompose the realized volatility into common and idiosyncratic volatilities, as well as the good and bad volatilities. Then the asymmetry in volatility spillovers between Bitcoin markets is measured by the DY (Diebold and Yilmaz) index. In addition, we construct statistics to test the asymmetry in volatility spillovers between different Bitcoin markets. The results are achieved as follows. The spillovers of systematic and idiosyncratic volatilities dominate the connectedness among different Bitcoin markets. In addition, the idiosyncratic volatility spillovers are more easily influenced by policies. Good volatility spillovers dominate the Bitcoin markets and change over time. The further results suggest that there is significant asymmetry between systematic and idiosyncratic volatility spillovers in the Bitcoin markets, while the asymmetries between good and bad volatility spillovers are heterogeneous in different markets. The findings in this paper can provide some suggestions for regulators controlling market stability and investors generating investment strategies.

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Modelling the volatility of Bitcoin returns using GARCH models

Cited by 48 publications

References 5 publications

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

Dynamic Network Connectedness of Bitcoin Markets: Evidence from Realized Volatility

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