Bitcoin, gold and the dollar – A GARCH volatility analysis

By A. H. Dyhrberg. 2016

1,431 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Information Systems

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Citations in the corpus

Statistical Arbitrage in Cryptocurrency Markets (2019, background): “Despite these heights, the market has remained fairly unregulated by governmental institutions (Dyhrberg 2016).”
Return, volatility and shock spillovers of Bitcoin with energy and technology companies (2018, result): “Our study expands previous efforts in cryptocurrencies’ literature that analyze the diversification benefits and interdependencies with financial assets (Dyhrberg, 2016; Ciaian et al., 2018; Corbet et al., 2018) and explore Bitcoin returns and volatility (Balcilar et al.”
Cryptocurrencies as a financial asset: A systematic analysis (2019, background): “Feld et al. [2014] present novel insights about Bitcoin’s peer-to-peer (P2P) network with a special focus on its distribution among distinct autonomous systems.”
The economic value of Bitcoin: A portfolio analysis of currencies, gold, oil and stocks (2019, neutral): “We select more than one assets in each asset class portfolio, even in cases they are highly correlated, to reduce the 5Employing a price index that blends trading prices of the top Bitcoin exchanges is a standard practice in literature (e.g., see Dyhrberg, 2016; Katsiampa, 2017; Baur et al., 2018).”
Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions (2016, background): “…on the diversification and hedging benefits of Bitcoin against equities (Halaburda and Gandal, 2014; Eisl et al., 2015; Bouri, Azzi et al., 2016; Bouri, Molnár et al., 2016; Dyhrberg, 2016) and against currencies or commodities (Bouri, Azzi et al., 2016; Bouri, Molnár et al., 2016; Dyhrberg, 2016).”
Statistical properties and multifractality of Bitcoin (2017, methodology): “or nancial institutions[2, 3]. Bitcoin price properties have also received attention and various related issues have been analyzed: time series analysis using economic models such as GARCH-type models[4, 5], statistical properties of Bitcoin prices[6, 7], ineciency of Bitcoin[8, 9], hedging capabili”
Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis (2019, neutral): “For the conditional variance-covariance equations, we employ the unrestricted BEKKMGARCH methodology of Engle and Kroner [1995]. The BEKK model permits the interaction of the conditional variances and covariances of several time series.”
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).”