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1.
European Journal of Finance ; 2023.
Article in English | Web of Science | ID: covidwho-20242863

ABSTRACT

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

2.
Applied Economics Letters ; 30(12):1685-1691, 2023.
Article in English | ProQuest Central | ID: covidwho-20238811

ABSTRACT

Bitcoin market had a significant momentum phenomenon before the launch of Futures, and then it turned into an insignificant reversal effect. After Covid-19 appeared, the momentum effect and reversal effect disappeared. The advent of bitcoin futures has increased how investors respond to information. With the outbreak of COVID-19, investor interest in Bitcoin as a safe-haven asset has increased the effectiveness of the price. We estimate the speed of signal diffusion in the bitcoin market, and the results support that effective response to information is the essential mechanism for the disappearance of momentum effect.

3.
Finance Research Letters ; 54, 2023.
Article in English | Scopus | ID: covidwho-2293074

ABSTRACT

This study examines the impact of two critical events, the introduction of Bitcoin futures and the COVID-19 pandemic, on Bitcoin's returns and volatility. We find that the inception of Bitcoin futures (positively) impacts its returns in the spot market while no significant interaction occurs for volatilities. The pandemic does not seem to influence Bitcoin's returns or volatility, which is consistent with the notion that Bitcoin is insulated from some global economic developments. Our tests also reveal that Bitcoin spot prices dominate its futures. This information might be useful for investors in capturing trend reversals considering the order of information disseminated. © 2023 Elsevier Inc.

4.
Res Int Bus Finance ; 59: 101519, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1392543

ABSTRACT

This paper contributes to the literature on the coronavirus (COVID-19) pandemic impacts on the Bitcoin futures (BTCF) market and to the ongoing consideration of the dynamic relationship between volatility (or returns) and trading behavior variables, such as volume and open interest as a proxy for belief dispersion. This paper focuses on the role of the unprecedented market stress induced by the COVID-19 pandemic in the interrelations among the variables. Accordingly, this paper proposes a structural change (SC)-VAR-MGARCH model and finds the COVID-19 pandemic has initiated a significant regime change. Furthermore, the relationship between the variables in the pre-pandemic regime is notably unclear, whereas an increase in belief dispersion in the pandemic regime due to market stress reduces BTCF returns but raises trading volume and volatility evidently. The outcomes in the pandemic regime are remarkably consistent with the difference of opinions model, though existing evidence on the dynamic relations is ambiguous. Moreover, the outcomes support our hypothesis that, in addition to information flows, market stress causing traders' behavioral biases should be considered as one of the crucial factors of tremendous price variability.

5.
Ann Oper Res ; : 1-32, 2021 Jul 22.
Article in English | MEDLINE | ID: covidwho-1321768

ABSTRACT

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

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