Determinants of &lt;i&gt;Bitcoin&lt;/i&gt; Expected Returns

Adjei, Frederick

doi:10.12691/jfe-7-1-5

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Previous studies have extensively examined the main drivers of changes in Bitcoin prices. It is widely acknowledged that a fundamental supply and demand factor (Kristoufek 2015); investors' interest (Ciaian, Rajcaniova, and Kancs 2016); macroeconomic and financial developments (Panagiotidis, Stengos, and Vravosinos 2019); and technological factors (Adjei 2019) are the main determinants of bitcoin prices. In relation to macroeconomic and financial developments, the expanding literature has examined the relationship between some macroeconomic indicators and Bitcoin prices.…”

Section: Introductionmentioning

confidence: 99%

Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19

Chen

Liu

Zhao

2020

Emerging Markets Finance and Trade

257

155

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This paper studies the impact of fear sentiment caused by the coronavirus pandemic on Bitcoin price dynamics. We construct a new proxy for coronavirus fear sentiment using hourly Google search queries on coronavirus-related words. The results show that market volatility has been exacerbated by fear sentiment as the result of an increase in search interest in coronavirus. Moreover, we find that negative Bitcoin returns and high trading volume can be explained by fear sentiment regarding the coronavirus. Our results also show that Bitcoin fails to act as a safe haven during the pandemic.

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Section: Introductionmentioning

confidence: 99%

Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19

Chen

Liu

Zhao

2020

Emerging Markets Finance and Trade

257

155

View full text Add to dashboard Cite

show abstract

“…The main decisive factors of bitcoin prices are a fundamental supply and demand factor in [29]; investors' interest in [30]; financial developments and macroeconomics in [31]; together with technological elements in [32]. But all these factors above are heavily affected by black swan events.…”

Section: Literature and Theorymentioning

confidence: 99%

Insights into Cryptocurrency Market Prices Fluctuation Correlated to COVID-19 Pandemic spillovers: Whether Bitcoin Can Act as a Safe Haven

2023

AEMPS

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In this article, I examine the relationship between COVID-19 daily additional cases and Bitcoin daily closing prices by applying Quantile-on-Quantile Regression (QQR) approach. The results reveal that the changing intensity level of COVID-19 does have an impact on Bitcoin prices. Whats more, my research captures the dependence between the distribution of Bitcoin prices and the severity of the COVID-19 pandemic. Levels of COVID-19 cause a rise in Bitcoin prices. I give a fresh look at combining every day confirmed number of new infections of COVID-19 and behavioral economics with fluctuating daily Bitcoin prices. And as the confirmed case has stabilized, although it is still at a relatively high position, investors would develop speculative psychology and take abnormal and seemingly inexplicable actions, e.g., they prefer to invest in Cryptocurrency rather than hold US dollars cash. Bitcoin offers a relatively steady performance in enduring the pandemic shocks; thus, it can be considered a safe-haven asset.

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“…Cryptocurrencies have very high unconditional volatility and are subject to sudden, massive price swings. As the search for determinants of cryptocurrency returns goes on, researchers have found broad themes, such as market forces of supply and demand, the arrival of additional information, trust, speculators (Ciaian, Rajcaniova, & Kancs, 2016), price clustering at round numbers (Urquhart, 2017), persistence (Caporale, Gil-Alana, & Plastun, 2017), mining difficulty and block size (Adjei, 2019), market stress and herding (Raimundo Júnior, Palazzi, Tavares, & Klotzle, 2020; Youssef, 2020) and investor attention (Subramaniam & Chakraborty, 2020).…”

Section: The Literature On Cryptocurrenciesmentioning

confidence: 99%

Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics

Malladi

2022

CAFR

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PurposeCritics say cryptocurrencies are hard to predict, lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.Design/methodology/approachWe use daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top-six cryptocurrencies that constitute 80% of the market. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effects model (FEM), random-effects model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.FindingsThe seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) Cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are: emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.Practical implicationsOne of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.Originality/valueThis paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices. Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods. Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

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Determinants of <i>Bitcoin</i> Expected Returns

Cited by 10 publications

References 8 publications

Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19

Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19

Insights into Cryptocurrency Market Prices Fluctuation Correlated to COVID-19 Pandemic spillovers: Whether Bitcoin Can Act as a Safe Haven

Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics

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