Bitcoin Volatility Research Articles

Bitcoin volatility and the introduction of bitcoin futures: A portfolio construction approach

We evaluate the introduction of Bitcoin futures on Bitcoin return and volatility using realized volatility and GARCH model pre-and post-futures. We also assess the portfolio construction implications by building two portfolios containing the top 25 S&P stocks, one without futures and one with. GARCH and realized volatility show mixed results. We provide that futures make Bitcoin riskier and more vulnerable to fluctuations over time. However, Bitcoin futures improve the portfolio's volatility and returns profile. Our findings offer implications regarding portfolio strategies implemented by risk-averse and risk-seeking investors and managers as we show how Bitcoin futures can hedge investments.

The Relationship Between Safe -haven Property and Cryptocurrency Market: Taking Bitcoin as an example

In this paper, the main research is whether Bitcoin has the property of hedge currency. It also shows how bitcoin and cryptocurrencies have fared in financial crises over the past 20 years. This is despite the fact that the volatility of bitcoin has long been debated around the world, and most people believe that a volatile and highly volatile currency is not a safe haven. But the fact is, although the birth time of bitcoin is very short, but in every financial crisis, it has a strong ability to rise, so to some extent, Bitcoin is actually a very realistic hedging property. And among cryptocurrencies, Bitcoin has the absolute suppression of hedge properties, compared with other altcoins. Bitcoin's rise and fall have been astonishing, but it has been remarkably stable compared to altcoins.

The Relationship Between Bitcoin Returns, Volatility, And Volume Using Asymmetric Garch Modelling

In 2020-2021 where the world experienced a Covid-19 pandemic, the price of Bitcoin increased and there was a fairly high price spike at the turn of the year 2020-2021 and had touched an all-time high (ATH) at the end of 2021. Bitcoin is one of the currencies crypto that is volatile when compared to the exchange rates of widely used currencies. In addition, Bitcoin price movements are difficult to predict. The samples used are Euro, Pound Sterling, Yuan, Yen, Ruble, Franc, and Bitcoin. The data used is in the form of Bitcoin price data and some of these currencies in 2020-2021. This study aims to find the relationship between volatility with Bitcoin trading volume, return with Bitcoin trading volume, and return with Bitcoin volatility for forecasting purposes. The research method used is the Augmented Dickey-Fuller (ADF) stationarity test, then using the ARMA model after that using the EGARCH model to find the relationship. The results of this study indicate that there is a positive relationship between volatility and Bitcoin trading volume. Just like the return and trading volume of Bitcoin there is a positive relationship. However, Bitcoin returns, and volatility have a negative relationship. For further research can use different crypto currency assets with different time periods.
 Keywords: Volatility, Volume, Returns, Bitcoin, GARCH

Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms

The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their predictive power in terms of forecasting accuracy. The predictive capabilities of CSS, NNETAR, and GMDH-NN are compared and evaluated by mean absolute error (MAE) and root-mean-square error (RMSE). Regarding the return volatility of Bitcoin and XRP markets, the forecasted results remarkably suggest that in contrast to rival approaches, the CSS can be an effective model to boost the predicting accuracy in the sense that it has the lowest forecast errors. Considering the Ethereum markets’ volatility, the MAE and RMSE associated with NNETAR are smaller than the MAE and RMSE of CSS and GMDH-NN algorithm, which ensures the effectiveness of NNETAR as compared to competing approaches. Similarly, in case of Tether markets’ volatility, the corresponding MAE and RMSE reveal that the GMDH-NN algorithm is an efficient technique to enhance the forecasting performance. We notice that no single tool performed uniformly for all cryptocurrency markets. The policymakers can adopt the model for forecasting cryptocurrency volatility accordingly.

Forecasting bitcoin volatility: exploring the potential of deep learning

This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold–Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.

Economic Risks Associated with Bit Coin: A Comprehensive Analysis

Bitcoin, the pioneering cryptocurrency, has revolutionized the financial landscape, but its use is not without economic risks. This paper provides a comprehensive analysis of the economic risks stemming from the use of Bitcoin. It explores the volatility of Bitcoin and the factors contributing to its price instability, including regulatory uncertainty, cybersecurity threats, and market manipulation. Additionally, it delves into the implications of illicit activities and money laundering associated with Bitcoin, highlighting the need for robust regulatory frameworks to mitigate these risks. The paper also examines the environmental impact of Bitcoin mining, emphasizing its energy-intensive nature and carbon footprint. The systemic risks posed by Bitcoin, such as market volatility spillover, interconnectedness with traditional financial institutions, counterparty risks, lack of regulation, financial crime, and cybersecurity vulnerabilities, are thoroughly analyzed. Through a comprehensive review of the literature and an in-depth understanding of the subject matter, this paper provides insights into the multifaceted economic risks associated with Bitcoin. It underscores the importance of proactive measures to address these risks, including enhanced regulations, transparency, cybersecurity measures, and sustainability practices. By mitigating these risks, the paper argues that Bitcoin can play a more secure and sustainable role in the future of finance.

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.

Forecasting realized volatility of Bitcoin: The informative role of price duration

AbstractMotivated by the relationship between trading intensity and volatility and the attractiveness of duration‐based volatility estimators, this paper investigates the ability of price duration to forecast realized volatility of Bitcoin. Using high‐frequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique duration‐augmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events.

Macroeconomic news and intraday seasonal volatility in the cryptocurrency markets

ABSTRACT We examine the effects of US, German, and Japanese macroeconomic news surprises and monetary policy decisions on the intraday cyclical volatility of Bitcoin and Ethereum markets. We first document intraday seasonality specific to each day of the week and show that these patterns exhibit a slightly different volatility compared to all-day seasonality. Second, the US monetary policy news and macroeconomic surprises generate the largest effect on the seasonal volatility. Third, Ethereum seasonality is more sensitive to macroeconomic fundamentals compared to Bitcoin. These results suggest that to improve cryptocurrency pricing, portfolio management, and risk management practices associated with cryptocurrency transactions, investors should consider the interactions between day of the week effects, intraday seasonality patterns and the macroeconomics news releases.

Review of: "Unpacking the Complexities of Bitcoin Volatility: A Time Series Data with Long-term Memory or Long-range Dependence"

Review of: "Unpacking the Complexities of Bitcoin Volatility: A Time Series Data with Long-term Memory or Long-range Dependence"

The effect of gold, dollar and Composite Stock Price Index on cryptocurrency

This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.

Price Prediction of Bitcoin, Ethereum and XRP Based on the ARMA Model

The growing potential and high volatility of the cryptocurrency market attract a lot of interest from both businesses and investors. Even though the prices fluctuate, predicting with time serious models such as ARMA and ARIMA would still provide a useful reference for analyzing the market. Recent studies on machine learning methods including RNNs have made new progress in forecasting digital currencies. This study focuses on one of the traditional models ARMA to predict the time serious dataset from 2021-2022 of cryptocurrencies including Bitcoin, Ethereum and Ripple. To be specific, AIC and ADF tests are used to choose the optimal model and suitable dataset. According to the analysis, the ARMA model would be affected by the volatility of Bitcoin. However, the predictions are not precise enough but still a valuable reference for certain businesses and individual investors. More state-of-art machine learning models can be utilized in future study to enhance the performance. Overall, these results shed light on guiding further exploration of crypto currency price prediction.

The COVID-19 pandemic and Bitcoin: Perspective from investor attention.

The response of the Bitcoin market to the novel coronavirus (COVID-19) pandemic is an example of how a global public health crisis can cause drastic market adjustments or even a market crash. Investor attention on the COVID-19 pandemic is likely to play an important role in this response. Focusing on the Bitcoin futures market, this paper aims to investigate whether pandemic attention can explain and forecast the returns and volatility of Bitcoin futures. Using the daily Google search volume index for the "coronavirus" keyword from January 2020 to February 2022 to represent pandemic attention, this paper implements the Granger causality test, Vector Autoregression (VAR) analysis, and several linear effects analyses. The findings suggest that pandemic attention is a granger cause of Bitcoin returns and volatility. It appears that an increase in pandemic attention results in lower returns and excessive volatility in the Bitcoin futures market, even after taking into account the interactive effects and the influence of controlling other financial markets. In addition, this paper carries out the out-of-sample forecasts and finds that the predictive models with pandemic attention do improve the out-of-sample forecast performance, which is enhanced in the prediction of Bitcoin returns while diminished in the prediction of Bitcoin volatility as the forecast horizon is extended. Finally, the predictive models including pandemic attention can generate significant economic benefits by constructing portfolios among Bitcoin futures and risk-free assets. All the results demonstrate that pandemic attention plays an important and non-negligible role in the Bitcoin futures market. This paper can provide enlightens for subsequent research on Bitcoin based on investor attention sparked by public emergencies.

Is Bitcoin a Safe Haven for US Equity Market? --A Comparison with Gold and the Dollar

This paper examines whether Bitcoin is an excellent hedge asset by applying the GARCH model. First, the correlation test finds that bitcoin's returns against the US dollar and gold are not significantly correlated. Also, based on comparisons across events, bitcoin returns perform more neutrally, unlike traditional hedges such as gold, which exhibit a significant negative correlation between performance and hedge in an emergency. In addition, the high volatility of Bitcoin compared to other varieties suggests that investors choosing to invest in Bitcoin will expose to high-risk return volatility. Therefore, bitcoin is more of a speculative asset than a safe haven.

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.

Crytocurrency price prediction using machine learning

Cryptocurrencies are a digital way of money in which all transactions are held electronically. It is a soft currency which doesn’t exist in the form of hard notes physically. Here, we are emphasizing the difference of fiat currency which is decentralized that without any third-party intervention all virtual currency users can get the services. However, getting services of these cryptocurrencies impacts on international relations and trade, due to its high price volatility. There are several virtual currencies such as bit-coin, ripple, ethereum, ethereum classic, lite coin, etc. In our study, we especially focused on a popular cryptocurrency, i.e., bitcoin. From many types of virtual currencies, bitcoin has a great acceptance by different bodies such as investors, researchers, traders, and policy-makers. To the best of our knowledge, our target is to implement the efficient deep learning-based prediction models. Specifically long short-term memory (LSTM) and gated recurrent unit (GRU) to handle the price volatility of bitcoin and to obtain high accuracy. Our study involves comparing these two time series deep learning techniques and proved the efficacy in forecasting the price of bitcoin.

Is there an expiration effect in the bitcoin market?

This paper studies the monthly expiration effect in the bitcoin markets. The emergence of trading in bitcoin futures in regulated markets is an ideal occasion to test this effect on an asset with singular characteristics. Our results with intraday data show that around the time of maturity there are significant changes in the trading volume, volatility and return of bitcoin, an asset that is traded in many exchanges simultaneously. Therefore, there is a clear expiration effect related to bitcoin futures. The closer to the expiration time (shortly beforehand or afterwards), the more intense these effects are. However, in spite of these general results, the expiration effect is not homogeneous across exchanges and depends on the characteristics of the futures contract in question. Robustness tests are also applied to confirm the results. The increasing participation of institutional investors is consistent with our findings, particularly in relation to the expiration effects of cash-settled futures, as these contracts are more appealing for sophisticated investors who could be interested in arbitrage or speculative processes.

Does natural gas volatility affect Bitcoin volatility? Evidence from the HAR-RV model

ABSTRACT While volatility spillover is a vital research area in financial economics (due to its importance for risk valuation and portfolio diversification strategies), the volatility linkage between Bitcoin and electricity/energy markets has not received adequate attention. As the Bitcoin mining cost comes mainly from electricity (which is highly dependent on natural gas), we hypothesize that natural gas is a non-trivial Bitcoin price volatility driver and aim to test if this is the case. Specifically, we employ a widely used model called the HAR-RV model to assess volatility spillover across Bitcoin and natural gas using high-frequency data. We find a spillover effect from natural gas to Bitcoin, and the positive (negative) component of natural gas volatility stabilizes (destabilizes) Bitcoin volatility. The spillover effect is further examined and confirmed using an out-of-sample approach.

The Bitcoin Halving Cycle Volatility Dynamics and Safe Haven-Hedge Properties: A MSGARCH Approach

This paper introduces a unique perspective towards Bitcoin safe haven and hedge properties through the Bitcoin halving cycle. The Bitcoin halving cycle suggests that Bitcoin price movement follows specific sequences, and Bitcoin price movement is independent of other assets. This has significant implications for Bitcoin properties, encompassing its risk profile, volatility dynamics, safe haven properties, and hedge properties. Bitcoin’s institutional and industrial adoption gained traction in 2021, while recent studies suggest that gold lost its safe haven properties against the S&P500 in 2021 amid signs of funds flowing out of gold into Bitcoin. Amid multiple forces at play (COVID-19, halving cycle, institutional adoption), the potential existence of regime changes should be considered when examining volatility dynamics. Therefore, the objective of this study is twofold. The first objective is to examine gold and Bitcoin safe haven and hedge properties against three US stock indices before and after the stock market selloff in March 2020. The second objective is to examine the potential regime changes and the symmetric properties of the Bitcoin volatility profile during the halving cycle. The Markov Switching GARCH model was used in this study to elucidate regime changes in the GARCH volatility dynamics of Bitcoin and its halving cycle. Results show that gold did not exhibit safe haven and hedge properties against three US stock indices after the COVID-19 outbreak, while Bitcoin did not exhibit safe haven or hedge properties against the US stock market indices before or after the COVID-19 pandemic market crash. Furthermore, this study also found that the regime changes are associated with low and high volatility periods rather than specific stages of a Bitcoin halving cycle and are asymmetric. Bitcoin may yet exhibit safe haven and hedge properties as, at the time of writing, these properties may manifest through sustained adoption growth.

The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns

The impact of Bitcoin futures introduction on the underlying Bitcoin volatility has been a controversial topic. Conflicting results had been obtained from different sample periods and methodologies. To address this debate, this study examines the impact of futures trading on volatility and volatility asymmetry of Bitcoin returns in the short and long run. Using exponential GARCH models, we introduce a dummy in the variance equation to capture the changes in the volatility after the introduction of Bitcoin futures. We find that after the introduction, spot return volatility decreases in the short run, but increases in the long run. Besides, in the short run, there exists an inverse leverage effect before and after the introduction; in the long run, the inverse leverage effect before the introduction changes to a usual level effect after the introduction. Finally, we examine whether greater futures trading activity, proxied by trading volume and open interest, is associated with greater Bitcoin volatility. To do so, we decompose each proxy into expected and unexpected components and document that, in the long run, Bitcoin volatility covaries positively with unexpected futures trading volume, but negatively with unexpected futures open interest.