GARCH Modelling of Cryptocurrencies

By Jeffrey Chu, Stephen Chan, Saralees Nadarajah, Joerg Osterrieder. 2017

330 citations recorded by the source corpus.

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3dd6f57e263802d4d62f502b4fddaff55b64cb01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

In Management Science and Operations Research

Citations in the corpus

Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance (2018, positive): “Conrad et al. (2018), Catania & Grassi (2017), Dyhrberg (2016), Katsiampa (2017), and Chu et al. (2017) use univariate volatility models to analyze the volatility structure of Bitcoin and other cryptocurrencies.”
Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, background): “…study, Baek & Elbeck (2015) find no evidence of predictability, whereas later studies report contradicting evidence (Urquhart, 2016, Nadarajah & Chu, 2017, Bariviera, 2017, Kristoufek, 2018, Sensoy, 2019), which suggest the possibility to predict Bitcoin volatility.6 Lastly, our work is…”
High frequency volatility co-movements in cryptocurrency markets (2019, methodology): “[2018], and Chuen et al. [2018], the findings of which suggest that cryptocurrencies are isolated from other markets. More specifically, using a dynamic conditional correlation model, Chuen et al. [2018] studied co-movements between the Cryptocurrency Index (CRIX) and mainstream assets, such as SP 5”
An empirical investigation of volatility dynamics in the cryptocurrency market (2019, neutral): “Later, Nadarajah and Chu (2017) considered a simple power transformation of the Bitcoin returns and showed that the transformed Bitcoin returns are actually market efficient.”
An application of extreme value theory to cryptocurrencies (2018, neutral): “For this reason, a lot of academic research has been conducted on Bitcoin (see, e.g., Cheah and Fry, 2015; Dyhrberg, 2016; Urquhart, 2016, 2017; Bariviera, 2017; Katsiampa, 2017; Nadarajah and Chu, 2017).”
Testing Stylized Facts of Bitcoin Limit Order Books (2019, background): “Other works focus on stylized facts of price time series (Bariviera et al. 2017; Brandvold et al. 2015; Chan et al. 2017; Chu et al. 2017; Easwaran et al. 2015; Zargar and Kumar 2019; Zhang et al. 2018) or the liquidity at the best bid and best ask—see Dimpfl (2017) and Dyhrberg et al. (2018).”
Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero (2018, neutral): “…there have been plenty of studies examining the future of Bitcoin and its volatility (Polasik et al. 2015; Letra, 2016; Bouoiyour and Selmi, 2016; Katsiampa, 2017; Chiu and Koeppl, 2017; Chu et al. 2017), there have been few that explore the broader cryptocurrency market and how it is evolving.”