Bitcoin Returns Research Articles

Mutual coupling between stock market and cryptocurrencies

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns––and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.

THE EFFECT OF BITCOIN NEWS ON BITCOIN PRICE AND RETURN

Yapılan bu çalışmanın amacı, 1.1.2016-4.12.2022 dönemini kapsayan günlük veriler yardımıyla Bitcoin ile alakalı çıkan haberler ile hem Bitcoin fiyatı hem de getirisi arasındaki ilişkiyi zamanla değişen nedensellik analizi kapsamında incelemektir. Söz konusu ilişkinin varlığı, Hacker ve Hatemi-J (2006)’nin Boostrapt Temelli Toda-Yamamoto Nedensellik Testi ve zamanla değişen nedensellik analizi kullanılarak araştırılmıştır. Elde edilen nedensellik testi bulguları, Bitcoin ile ilgili çıkan haberler ile Bitcoin fiyatı arasında karşılıklı bir nedensellik ilişkisi olduğu yönündedir. Diğer taraftan, Bitcoin getirisi ile Bitcoin ile ilgili çıkan haberler arasındaki nedensellik bulguları incelendiğinde, Bitcoin ile alakalı çıkan haberlerden Bitcoin getirisine doğru nedensellik ilişkisinin söz konusu olmadığı, buna karşın Bitcoin getirilerinden Bitcoin ile alakalı çıkan haberlere doğru bir nedensellik olduğu söylenebilir. Ayrıca, söz konusu nedensellik ilişkilerinin zamanla nasıl bir seyir izlediğine bakıldığında özellikle Bitcoin fiyatlarının arttığı dönemlerde Bitcoin ile ilgili haber sayılarının arttığı görülmüştür. Bu çerçevede hem Bitcoin hem de altcoin piyasasına yatırım yapacak bireylerin, Bitcoin ve altcoin ile alakalı çıkmış olan haberleri dikkate alarak işlem yapmaları yatırımın sağlıklı olması adına önem teşkil etmektedir.

THE EFFECT OF TECHNICAL ASSET, FINANCIAL ASSET AND MACRO-ECONOMIC ON RETURN BITCOIN

This study uses research variables such as Trading Volume, Market Capitalization, S&P 500 Index, World Oil Price, Gold Price on Bitcoin Returns. The data source in this study is through internet media with the sites https://coinmarketcap.com/, https://finance.yahoo.com/, and other sites that support this research. The data used in this study are time series, and the target population for the data sample is 1,096 (3 years x 365) daily report data. The data were collected from January 1, 2020, to December 31, 2022, through the release of daily bitcoin transaction reports. Time series data analysis using Engel Granger's Error Correction Model is the method employed (ECM). The analysis tool used is the Econometric Views (Eviews) which is one of the computer econometric program. The results of this study where trading volume has a positive and insignificant effect on bitcoin returns. The impact of market capitalisation on bitcoin returns is both favorable and significant. Returns on bitcoin are positively and significantly impacted by S&P 500. The returns on bitcoin are negatively and insignificantly impacted by the price of world oil. Returns on bitcoin are positively and negligibly impacted by the gold price.

Dynamic effect of Bitcoin, fintech and artificial intelligence stocks on eco-friendly assets, Islamic stocks and conventional financial markets: Another look using quantile-based approaches

Against the milieu of rapidly growing investment in technologically induced assets, this study examines the investment role of Bitcoin, fintech, and artificial intelligence (AI) stocks in relation to major environmentally friendly assets (green bonds, sustainable investments, and clean energy), Islamic stocks, and conventional financial markets using quantile-based approaches. To this end, we specifically examine the distributional and directional predictability between the returns of fintech, Bitcoin, and AI and various markets using the nonparametric causality-in-quantiles method and the cross-quantilogram correlation method respectively. We use daily data spanning March 9, 2018 to January 27, 2021. In terms of the distributional predictability of fintech, Bitcoin, and AI in relation to the traditional markets, Islamic stocks, clean energy stocks, and sustainable investments, we find strong evidence of causal asymmetry across quantiles and strong variations across markets. Likewise, findings in terms of directional predictability between the returns of fintech, Bitcoin, and AI and various markets infer that Islamic stocks act as a good hedge against Bitcoin. The S&P Treasury Bond and S&P Green Bond are also perfect hedges for fintech stocks, while S&P Global Clean Energy is a perfect hedge for AI stocks in terms of long-term dynamics.

Quantile prediction for Bitcoin returns using financial assets’ realized measures

This paper explores which properties of financial asset prices drive Bitcoin’s return distributions, using quantile regressions with lagged realized moment measures of various financial assets. The result shows that Bitcoin’s lagged realized volatility predicts its return distributions very well, revealing Bitcoin’s aspect as a risk asset. Moreover, its lagged realized kurtosis plays some role in prediction in recent periods. In contrast, other financial assets’ realized measures have limited predictive power, which implies the relative uniqueness of Bitcoin’s price movements. Finally, out-of-sample predictions using lasso quantile regressions confirm the robust predictive power of lagged Bitcoin variables even in the Covid-19 period.

The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model

This study employs a non-linear framework to investigate the impacts of central bank digital currency (CBDC) news on the financial and cryptocurrency markets. The time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005) is estimated based on weekly data from the first week of January 2015 to the last week of December 2021. The vector of endogenous variables in the VAR estimation contains the Central Bank Digital Currency uncertainty index (CBDCU), cryptocurrency policy uncertainty index, S&P 500 index, VIX, and Bitcoin price. The TVP-VAR model’s time-varying responses demonstrated that the reactions of the cryptocurrency market to central bank digital currency announcements vary remarkably over time. The impacts of the CBDC shocks on the financial market have been increasingly visible during the COVID-19 pandemic. According to the time-varying forecast error decompositions, CBDCU and VIX shocks have accounted for most of the variance in cryptocurrency uncertainty and Bitcoin return shocks, notably during the COVID-19 period.

What Information Variables Predict Bitcoin Returns? A Dimension-Reduction Approach

This article investigates the determinants of Bitcoin returns. The authors consider a comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other assets, stress level, and investor sentiment. Their approach toward this large dataset is built upon dimension-reduction models such as Backward Elimination, least absolute shrinkage and selection operator (LASSO), principal component regression (PCR), and three-pass regression filter (3PRF). The empirical results show that blockchain technology, stress level, and investor sentiment have positive, negative, and positive predicting power on Bitcoin returns, respectively. Macroeconomic variables exhibit insignificant impacts on Bitcoin returns. Other asset variables show little predicting power until 2019, but some become a significant predictor during the COVID-19 pandemic. Overall, the authors caution against using Bitcoin as a risk-hedging device in financial portfolios. They also find that, consistent with other financial assets such as equities, Bitcoin shows increased predictability with a longer return horizon. Due to their empirical results, they also advocate the use of 3PRF; relative to other dimension-reduction methods under consideration, they observe superior performance of 3PRF in predicting both the level and the direction of future Bitcoin returns across all return horizons.

Asymmetric impact of economic policy uncertainty on cryptocurrency market: Evidence from NARDL approach

The study examines the relationship between cryptocurrency market and economic policy uncertainty (EPU) by concentrating on the top five cryptocurrencies assessed by market capitalization. We investigate long and short-run effects of global EPU on the returns of Bitcoin, Ethereum, Tether, Binance coin and Ripple, along with ascertaining asymmetries, through the NARDL (Non-Linear Autoregressive Distributed Lag) model, for the period ranging from September 2017 to August 2022. In the long run, except for Tether, all other cryptocurrencies are negatively affected by EPU, challenging the safe hedge properties of cryptocurrencies. However, Bitcoin, Ethereum, Binance coin and Ripple display positive relations with growing EPU in short run, reflecting their safe haven attributes over a shorter horizon. Additionally, these cryptocurrencies exhibit asymmetries in the short run. Further, we analyse the effect of US, Chinese and Indian EPU on bitcoin returns to understand the effect of EPU shocks of advanced and emerging economies. US EPU has long-run negative effects on bitcoin market, whereas Indian and Chinese EPU have no significant impact in long term. Moreover, our analysis also establishes the detrimental impact of the Indian government's crypto tax policy on bitcoin. The study has relevance for current and potential investors of the cryptocurrency market apart from the policymakers and regulatory bodies.

Asset Allocation with Crypto: Application of Preferences for Positive Skewness

Bitcoin (BTC) returns exhibit pronounced positive skewness with a third central moment of approximately 150% per year. They are well characterized by a mixture of Normals distribution with one “normal” regime and a small probability of a “bliss” regime where the price appreciation is more than 100 times at the annual horizon. The large right-tail skew induces investors who prefer positive skewness to add significant BTC holdings to equity-bond portfolios. Even when BTC is forecasted to lose half its value in the normal regime, investors with power utility optimally add 3% allocations to BTC when the probability of the bliss regime is around 1%. Cumulative Prospect Theory investors are even more sensitive to positive skewness. They hold BTC allocations of about 3% when the probability of the bliss regime is 0.0006, and the mean of BTC in the normal regime corresponds to a loss of 90%.

Contribution of Nonlinear Dynamics to the Informational Efficiency of the Bitcoin Market

The recent decade has witnessed a surge of cryptocurrency markets as innovative financial systems based strongly on digital emission, interchange and coding. The main characteristic is that cryptocurrencies are not subjected to the regulation of governments and financial institutions (e.g., central banks), such that their dynamics are determined solely by non-centralized mechanisms. Informational efficiency is a key issue for cryptocurrency markets since its fulfillment guarantees that all participants have access to the same information quality and that arbitrage conditions are discarded. This study evaluated the contribution of nonlinearities to the informational efficiency of the Bitcoin market for the period 2014–2022. Singular value decomposition (SVD) entropy together with shuffled and phase-randomized data in a rolling-window framework was used to capture randomness and nonlinear dynamics in Bitcoin returns. It was found that the contribution of nonlinearities to informational efficiency increases with the time scale, with a mean contribution of about 7.25% for long-time scales. This means that the Bitcoin market is only affected by weak nonlinearities, although these effects should be considered for forecasting and valuation.

The impact of expected and unexpected events on Bitcoin price development: Introduction of futures market and COVID-19

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.

Oil price and the Bitcoin market

Motivated by the significant role oil plays in the production of Bitcoin, we test whether its price can influence the realized volatility of Bitcoin returns. Using data over the period of January 27, 2017 (coinciding with the emergence of Bitcoin bubbles) to June 3, 2022, we conduct some predictability analyses and establish the following outcomes. First, we find that higher oil prices tend to raise the cost of producing Bitcoins, therefore lowering its returns and by extension its trading and volatility. Second, we find improved forecast performance of oil price for the realized volatility of Bitcoin as our proposed model that accounts for oil price consistently outperforms the benchmark (random walk) model, regardless of the oil price variant and forecast horizon. Third, investors in the Bitcoin market that observe oil price movements when making investment decisions are more likely to derive higher economic gains than their counterparts that ignore it.

Bitcoin Return Prediction based on OLS, Random Forest, LightGBM, and LSTM

Since Bitcoin was proposed in 2008, it has become a very valuable asset and an important part of many investors’ portfolios. It’s important to both understand Bitcoin mechanics and predict its valuation with the help of the state-of-art machine learning tools. The study develops four different models, including Ordinary Least Squares (OLS) regression model, Random Forest, Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM), to predict the return of Bitcoin and compare the performance of these models. According to the analysis, the daily changes in the high, low, close price of Bitcoin, and close price of Tesla stock, and gold price between yesterday and today are all strongly correlated to the Bitcoin return on tomorrow. The statistical approach, or OLS modeling, has the simplest algorithm whereas the highest accuracy rate. The LightGBM model and LSTM model have lower accuracy rates in order, but still exceed the 50% (random benchmark). The Random Forest model, as another type of decision tree algorithm, has similar prediction results with the LightGBM model but a lower accuracy rate that fails to reach the benchmark. Based on the analysis, multiple factors affect the Bitcoin return, and these results provide an insight for investors to the cryptocurrency market and the macroeconomic environment. It validates the effectiveness of several machine learning algorithms in Bitcoin return forecasting and supports future developments in related fields.

CORRELATION BETWEEN BITCOIN, STOCKS AND GOLD: A MARKOV REGIME-SWITCHING APPROACH

ABSTRACT The following article explores the correlation between bitcoin and both stocks and gold. A Markov regime-switching approach was used to identify and date two regimes in each of these financial assets. Stock returns are characterized by short-lived episodes of elevated volatility and negative returns whereas bitcoin returns are characterized by a persistent high volatility state with positive returns. Gold stayed in the low volatility period most of the time and only a few short-lived episodes of high volatility were identified during the first year of the pandemic. A concordance measure was computed to assess the synchronicity and correlation between the regimes. The regimes of bitcoin and gold are uncorrelated suggesting that bitcoin is not yet perceived as a safe haven like gold. The regimes of bitcoin and stocks were also uncorrelated suggesting that bitcoin may be used as a hedge against stocks. Keywords Bitcoin, correlation between bitcoin and stock market, correlation between bitcoin and gold, Markov regime-switching.

Currency exchange rate predictability: The new power of Bitcoin prices

We show that Bitcoin prices have surprisingly predictive power for nominal currency exchange rates, both in-sample and out-of-sample. The predictability follows from the fact that Bitcoin prices are forward-looking: Bitcoin efficiently incorporates expectations of currency exchange rates and their drivers, as exchange rates serve as a fundamental of Bitcoin. We examine the Bitcoin-based exchange rate prediction model in the autoregressive distributed lag (ADL) specification and the error correction specification. Forecasts based on both specifications outperform different benchmarks for some of the exchange rates. The outperformance is most pronounced at the daily horizon using the ADL model. Bitcoin-based forex trading strategies generate Sharpe ratio gains relative to the US risk-free rate and the carry trade. Bitcoin returns incorporate extra knowledge of future interest rate differentials after controlling for lagged exchange rate movements. Our result is inspiring for currency market participants, given the well-documented difficulty in exchange rate prediction.

Risk Spillovers between Bitcoin and ASEAN+6 Stock Markets before and after COVID-19 Outbreak: A Comparative Analysis with Gold

This paper applies the multivariate GARCH models to investigate the role of Bitcoin as a hedge and safe haven for ASEAN+6 stock markets compared to gold. We used daily data for the dates 2 January 2017–20 January 2023, covering the recent COVID-19 pandemic. The empirical findings provide compelling evidence of cross-market shock and volatility transmission between stock returns and Bitcoin returns in both directions. Therefore, the dynamics of Bitcoin returns significantly influence the volatility of stock returns, and the relationship also holds in reverse. All diagonal element estimations are statistically significant for both periods, as shown by the findings of the return and volatility spillovers between the returns of gold and the ASEAN+6 stock market. For most ASEAN+6 equity markets evaluated, Bitcoin and gold are not safe havens, and their inclusion increases the portfolio downside risk.

Asymmetric impact of investor sentiment and media coverage news on bitcoin returns

PurposeThis paper investigates the impact of global sentiment and various coronavirus disease 2019 (COVID-19)-related media coverage news (Media-Hype index; Panic Index; Media Coverage Index, infodemic index and coronavirus statistics) on the dynamics of bitcoin returns during the COVID-19 pandemic using an asymmetric framework.Design/methodology/approachThe authors use an asymmetric framework based on quantile regression (QR) and quantile-on-quantile regression.FindingsQR results show that COVID-19 panic news negatively affects bitcoin market returns at times of extreme bearish. However, COVID-19 bullish sentiment negatively impacts bitcoin market returns during bullish market conditions. Quantile-on-quantile approach's (QQA) empirical results show that the effects of COVID-19-related news on bitcoin returns were heterogeneous, mainly negative and varied across quantiles.Research limitations/implicationsThe authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic.Practical implicationsThe authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic.Originality/valueThis study contributes to understanding the dynamics of bitcoin returns using various COVID-19 media news.

Testing for parameter change epochs in GARCH time series

Summary We develop a uniform test for detecting and dating the integrated or mildly explosive behaviour of a strictly stationary generalized autoregressive conditional heteroskedasticity (GARCH) process. Namely, we test the null hypothesis of a globally stable GARCH process with constant parameters against the alternative that there is an ‘abnormal’ period with changed parameter values. During this period, the parameter-value change may lead to an integrated or mildly explosive behaviour of the volatility process. It is assumed that both the magnitude and the timing of the breaks are unknown. We develop a double-supreme test for the existence of breaks, and then provide an algorithm to identify the periods of changes. Our theoretical results hold under mild moment assumptions on the innovations of the GARCH process. Technically, the existing properties for the quasi-maximum likelihood estimation in the GARCH model need to be reinvestigated to hold uniformly over all possible periods of change. The key results involve a uniform weak Bahadur representation for the estimated parameters, which leads to weak convergence of the test statistic to the supreme of a Gaussian process. Simulations in the Appendix show that the test has good size and power for reasonably long time series. We apply the test to the conventional early-warning indicators of both the financial market and a representative of the emerging Fintech market, i.e., the Bitcoin returns.

Good and bad self-excitation: Asymmetric self-exciting jumps in Bitcoin returns

Good and bad self-excitation: Asymmetric self-exciting jumps in Bitcoin returns

The Impact of the Russia-Ukraine War on the Cryptocurrency Market

This research provides the very first empirical investigation of the impact of the Russia-Ukraine war on the cryptocurrency market (Bitcoin trading volume, and returns). The findings indicate that the Russia-Ukraine war impedes Bitcoin trading volume. A 1% increase in the Russia-Ukraine war leads to a 0.2% reduction in Bitcoin trading volume. The findings also indicate that the impact is more pronounced during the post-invasion period, especially after one week of the invasion. Finally, the Russia-Ukraine war predicts Bitcoin returns in both the short and long run.