Bitcoin, gold and the dollar – A GARCH volatility analysis

Dyhrberg, Anne Haubo

doi:10.1016/j.frl.2015.10.008

Cited by 1,284 publications

(814 citation statements)

References 5 publications

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“…Accordingly, the volatility of Bitcoin decreases less as the price of Bitcoin increases, leading to an inverted asymmetry phenomenon. This interesting finding concurs with that reported for gold (Baur, 2012), and adds further evidence to the similarities between gold and Bitcoin (Dyhrberg, 2015b). Another plausible explanation of the findings relates to investors' quest for a safe-haven asset in an environment of weak trust, such as during the global financial crisis (GFC) and post-GFC periods, in particular during the ESDC.…”

Section: Table 2 Coefficient Estimates Of the Asymmetric-garch Modelsupporting

confidence: 87%

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On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe authors examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in various currencies. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. They test if there is a difference in the return-volatility relation before and after the price crash of 2013 and show a significant inverse relation between past shocks and volatility before the crash and no significant relation after. This finding shows that, prior to the price crash of December 2013, positive shocks increased the conditional volatility more than negative shocks. This inverted asymmetric reaction of Bitcoin to positive and negative shocks is contrary to what the authors observe in equities. As leverage effect and volatility feedback don't adequately explain this reaction, they propose the safe-haven effect (Baur, Asymmetric volatility in the gold market, 2012). The authors highlight the benefits of adding Bitcoin to a US equity portfolio, especially in the pre-crash period. Robustness analyses show, among others, a negative relation between the US implied volatility index (VIX) and Bitcoin volatility. Those additional analyses further support their findings and provide useful information for economic actors who are interested in adding Bitcoin to their equity portfolios or are curious about the capabilities of Bitcoin as a financial asset. JEL G11 G15

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Section: Table 2 Coefficient Estimates Of the Asymmetric-garch Modelsupporting

confidence: 87%

“…Bouri et al (2016) examine the volatility persistence in the Bitcoin market. However, Dyhrberg (2015a) highlights the hedging ability of Bitcoin against the USD/EUR and USD/GBP exchange rates and UK equities, whereas Dyhrberg (2015b) situates the hedging capability of Bitcoin somewhere between gold and the US dollar.…”

Section: Introductionmentioning

confidence: 99%

On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013

2016

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“…Dyhrberg (2016a) investigated the hedging capabilities of Bitcoin by applying the asymmetric GARCH methodology and showed that Bitcoin can clearly be used as a hedge against stocks in the Financial Times Stock Exchange Index and against the American dollar in the shortterm. 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.…”

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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“…It was shown that Bitcoin can be used as a hedge against stocks in the Financial Times Stock Exchange Index and against the American dollar in the short term. Dyhrberg (2016b) used GARCH models to explore the financial asset capabilities of Bitcoin. It was shown that Bitcoin has a place on the financial markets and in portfolio management as it can be classified as something in between gold and the American dollar, on a scale from pure medium of exchange advantages to pure store of value advantages.…”

Section: Introductionmentioning

confidence: 99%

GARCH Modelling of Cryptocurrencies

Chu

Chan

Nadarajah

et al. 2017

JRFM

278

165

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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.

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Bitcoin, gold and the dollar – A GARCH volatility analysis

Cited by 1,284 publications

References 5 publications

On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013

On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013

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

GARCH Modelling of Cryptocurrencies

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