Volatility estimation for Bitcoin: A comparison of GARCH models

Katsiampa, Paraskevi

doi:10.1016/j.econlet.2017.06.023

Cited by 878 publications

(539 citation statements)

References 7 publications

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“…The results are interesting to note. There are not large differences among the GARCH models, however, CGARCH model is the most appropriate model among the GARCH models judging from the Akaike Information Criteria (AIC) and Hanna-Quinn Criteria (HQ), which are similar to results discussed in Katsimpa (2017) and Bouoiyour and Selmi (2015). When examining the Bitcoin's prices, traders should see the volatilities, the short-run changes in volatility, and the long-run changes of volatility.…”

Section: Garch Modelsupporting

confidence: 72%

How Does Price of Bitcoin Volatility Change?

Kurihara¹,

Fukushima²

2018

IREF

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The trading volume of Bitcoin has increased immensely since its conception. Bitcoin is a cryptocurrency, it is not a legal currency but rather a private monetary system that manages by itself and does not depend on governments or central banks. It is an autonomous currency system that is not liable to any governing body. Some fear that the increase of Bitcoin usage, as it is quite different from traditional currencies and is free from control or regulations by monetary authorities. Although its popularity has grown worldwide, fluctuations of the prices are sometimes erratic. Hence, such large and sudden movements would dampen the sound development of Bitcoin. This paper examines how the volatile price of Bitcoin changes empirically. The empirical results show that there is a difference between short-term volatility and long-term volatility. Traders should see not only the short-term movements in volatile Bitcoin pricing but also long-term developments.

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Section: Garch Modelsupporting

confidence: 72%

How Does Price of Bitcoin Volatility Change?

Kurihara¹,

Fukushima²

2018

IREF

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“…Unlike Urquhart (2016) and Nadarajah and Chu (2017) use transformed Bitcoin returns and report evidence of market efficiency. Katsiampa (2017) focuses on the price volatility of Bitcoin and explores the optimal conditional variance model. Furthermore, Urquhart (2017) finds evidence of price clustering at round numbers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, Urquhart (2017) finds evidence of price clustering at round numbers. Katsiampa (2017) focuses on the price volatility of Bitcoin and explores the optimal conditional variance model. Bouri, Azzi, and Dyhrberg (2017) report evidence of a negative relation between the U.S. implied volatility index and Bitcoin volatility.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

et al. 2018

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Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors.

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“…Dyhrberg (2016b) explored the financial asset capabilities of Bitcoin using GARCH models and showed several similarities to gold and the dollar indicating hedging capabilities and advantages as a medium of exchange. Katsiampa (2017) explored the optimal conditional heteroscedasticity model with regards to goodness-of-fit to Bitcoin price data, and found that the best model is the AR-CGARCH model, indicating significance of including both a short-run and a long-run component of the conditional variance. Urquhart (2017) examined the volatility of Bitcoin as well as shedding light on the forecasting ability of GARCH models and heterogeneous auto-regressive (HAR) models in the Bitcoin market.…”

Section: Literature Reviewmentioning

confidence: 99%

GARCH Model With Fat-Tailed Distributions and Bitcoin Exchange Rate Returns

Liu¹,

Shao²,

Wei³

et al. 2017

Journal of Accounting, Business and Finance Research

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In the era of diminishing power from US dollar and increasing competition among world currencies, Bitcoin, as a completely new concept as a medium of exchange, has received increasing attentions over the world. Nowadays, Bitcoin also becomes an investment vehicle, which carries attractive opportunities but also significant risks for the investment community. In this paper, we have compared the empirical performance of a newly-developed heavy-tailed distribution, the normal reciprocal inverse Gaussian (NRIG), with the most popular heavy-tailed distribution, the Student's t distribution, under the GARCH framework in fitting the daily Bitcoin exchange rate returns. Our results indicate the heavy-tailed distribution has better performance in capture the daily Bitcoin exchange rate returns dynamics than the standard normal distribution. Our results also show the older fashioned Student's t distribution still performs better than the new heavy-tailed distribution.

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Volatility estimation for Bitcoin: A comparison of GARCH models

Cited by 878 publications

References 7 publications

How Does Price of Bitcoin Volatility Change?

How Does Price of Bitcoin Volatility Change?

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

GARCH Model With Fat-Tailed Distributions and Bitcoin Exchange Rate Returns

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