Q2. What are the future works in "Volatility estimation for bitcoin: a comparison of garch models" ?
Bitcoin is different from any other asset on the financial market and thereby creates new possibilities for stakeholders with regards to risk management, portfolio analysis and consumer sentiment analysis ( Dyhrberg 2016b ). Hence, it can be a useful tool for portfolio and risk management, and their results can help investors make more informed decisions.
Q3. What is the purpose of this article?
In this research their interest lies particularly in low-order models, since low orders of GARCH-type models can catch most of the nonlinearity of the conditional variance, and hence only the first order models are presented for simplicity.
Q4. What are the two types of models used in this research?
The models used in this research consist of an Autoregressive model for the conditional mean and a first-order GARCH-type model for the conditional variance2, as follows2
Q5. What is the current status of the Bitcoin market?
2014; Baek and Elbeck 2015; Dyhrberg 2016a), the Bitcoin market is currently highly speculative, and more volatile and susceptible to speculative bubbles than other currencies (Grinberg 2011; Cheah and Fry 2015).
Q6. What criteria are used to choose the optimal model?
The optimal model is chosen according to three information criteria, namely Akaike (AIC), Bayesian (BIC) and Hannan-Quinn (HQ), all of which consider both how good the fitting of the model is and the number of parameters in the model, rewarding a better fitting and penalising an increased number of parameters for given data sets.
Q7. What is the way to describe the volatility of the Bitcoin price?
The authors found evidence that the optimal model in terms of goodness-of-fit to the data is the AR-CGARCH, a result which suggests the importance of having both a short-run and a long-run component of conditional variance.
Q8. What is the name of the author?
Paraskevi Katsiampa E-mail address: p.katsiampa@shu.ac.ukMoreover, the presence of long memory and persistent volatility (Bariviera et al. 2017) justifies the application of GARCH-type models.
Q9. What is the way to estimate the volatility of the Bitcoin price?
In addition, according to the results of both the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit-root tests the authors fail to accept the null hypothesis of a unit root for the returns, and, hence, stationarity is guaranteed.
Q10. Why has Bitcoin received so much attention lately?
This can be attributed to its innovative features, simplicity, transparency and its increasing popularity (Urquhart 2016), while since its introduction it has posed great challenges and opportunities for policy makers, economists, entrepreneurs, and consumers (Dyhrberg 2016b).
Q11. What is the significance of the Jarque-Bera test?
even though theresiduals of the AR(1)-CGARCH(1,1) model still depart from normality, the value of the Jarque-Bera test has considerably decreased compared with the corresponding value for the returns.
Q12. How many observations are used in this research?
The data used are the daily closing prices for the Bitcoin Coindesk Index from 18th July 2010 (as the earliest date available) to 1st October 2016, which corresponds to a total of 2267 observations.