Forecasting Bitcoin volatility: The role of leverage effect and uncertainty

Yu, Miao

doi:10.1016/j.physa.2019.03.072

Cited by 50 publications

(29 citation statements)

References 36 publications

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“…More specifically, we showed that during the crisis period, only negative jumps had a predictive power concerning future realized volatility. This finding was important as it might explain the results of previous findings (Shen et al, 2020;Yu, 2019) that show the jumps do not have a predictive power for future volatility. In other words, these studies, by omitting the decomposition of jumps into positive and negative jumps, might have diluted the effect on jumps.…”

Section: The Out-of-sample Resultsmentioning

confidence: 77%

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Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

2021

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This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

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Section: The Out-of-sample Resultsmentioning

confidence: 77%

“…They also showed that jump and signed jumps improve volatility forecasting only in long horizons. Yu ( 2019 ) employed HFD to forecast the Bitcoin volatility by considering leverage effects and economic policy uncertainty (EPU). It was found that the leverage effect might impact future volatility significantly.…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

2021

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“…Return, as well as volatility, play important roles in numerous financial aspects, e.g., asset pricing, investment portfolio allocation and risk management, etc., and are the two most important characteristics of one certain asset. However, there are still many puzzles needed to be solved urgently in explaining and forecasting the Bitcoin market, which attracted numerous researchers in this field [ 3 , 6 , 11 ]. Investor attention, which may be represented by extreme return, abnormal trading volume, advertising expenditure, and media coverage [ 12 – 14 ], is a key resource constrained by limited processing capacity and time pressure, besides, it is a scarce resource for every asset, as investors can only concentrate on limited set information in reality since their time and effort constraint [ 13 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…As an alternative payment method accepted by merchants, i.e., Subway and Microsoft, Bitcoin is playing an increasingly important role in cryptocurrency exchanges around the world. The novelty of Bitcoin and other cryptocurrencies, as well as Bitcoin's unprecedented performance and volatility since its inception, have drawn attention from practitioners, regulators, and scholars [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Zhu

Zhang

et al. 2021

PLoS ONE

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This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.

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“…In the case of Bitcoin prices using high frequency data, in [38] it is shown that it exists a large degree of multi-fractality in all examined time intervals which can be attributed to the high kurtosis and the fat distributional tails of the series returns; in [39] there is evidence about the leverage effect as the most powerful effect in volatility forecasting; volatility is also analysed in [40] in terms of the property of the long memory parameter to be significant and quite stable for both unconditional and conditional volatilities at different time scales. Extending the study to several high frequency cryptocurrencies data, in [41] the investigation on stylized facts is developed in terms of the Hurst exponent of dependence between four different cryptocurrencies.…”

Section: Introductionmentioning

confidence: 98%