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Open AccessJournal ArticleDOI

On the return-volatility relationship in the Bitcoin market around the price crash of 2013

TLDR
In this paper, the authors examined the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in US dollar and found that positive shocks increased the conditional volatility more than negative shocks.
Abstract
The authors examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in US dollar. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. The authors 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 one observes in equities. As leverage effect and volatility feedback do not adequately explain this reaction, the authors propose the safe-haven effect (Baur, Asymmetric volatility in the gold market, 2012). They 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 the 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.

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Citations
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Journal ArticleDOI

Volatility estimation for Bitcoin: A comparison of GARCH models

TL;DR: In this article, the authors explore the optimal conditional heteroskedasticity model with regards to goodness-of-fit to Bitcoin price data and find that the best model is the AR-CGARCH model, highlighting the significance of including both a short run and a long run component of the conditional variance.
Journal ArticleDOI

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

TL;DR: In this article, a non-parametric causality-in-quantiles test was employed to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions.
Journal ArticleDOI

Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions

TL;DR: In this article, the authors examine whether Bitcoin can hedge global uncertainty, measured by the first principal component of the VIXs of 14 developed and developing equity markets, by decomposing Bitcoin returns into various frequencies, i.e., investment horizons, and given evidence of heavy-tails.
Journal ArticleDOI

The inefficiency of Bitcoin revisited: A dynamic approach

TL;DR: In this paper, the authors revisited the informational efficiency of the Bitcoin market and analyzed the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent.
Posted Content

Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions

TL;DR: In this article, the authors analyse whether Bitcoin can hedge uncertainty using daily data for the period of 17th March, 2011, to 7th October, 2016, and find that Bitcoin does act as a hedge against uncertainty, that is, it reacts positively to uncertainty at both higher quantiles and shorter frequency movements of Bitcoin returns.
References
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Journal ArticleDOI

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks

TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
Journal ArticleDOI

Computation and analysis of multiple structural change models

TL;DR: In this paper, the problem of estimating the break dates and the number of breaks in a linear model with multiple structural changes has been considered and an efficient algorithm based on the principle of dynamic programming has been proposed.
Posted Content

Computation and Analysis of Multiple Structural-Change Models

TL;DR: In this paper, the problem of estimating the number of break dates in a linear model with multiple structural changes has been studied and an efficient algorithm to obtain global minimizers of the sum of squared residuals has been proposed.
Posted Content

No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns

TL;DR: In this paper, the generalized autoregressive conditionally heteroskedastic (GARCH) model of returns is modified to allow for volatility feedback effect, which amplifies large negative stock returns and dampens large positive returns, making stock returns negatively skewed and increasing the potential for large crashes.
Journal ArticleDOI

No news is good news: An asymmetric model of changing volatility in stock returns

TL;DR: This article developed a formal model of the volatility feedback effect using a simple model of changing variance (a quadratic generalized autoregressive conditionally heteroskedastic, or QGARCH, model).
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