Bitcoin Volatility Research Articles

Forecasting Bitcoin volatility using machine learning techniques

This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts.

Exploring asymmetries in cryptocurrency intraday returns and implied volatility: New evidence for high-frequency traders

This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders.

Joint Impact of Market Volatility and Cryptocurrency Holdings on Corporate Liquidity: A Comparative Analysis of Cryptocurrency Exchanges and Other Firms

This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges.

Are Indian markets insulated from the impact of cryptocurrencies? Unveiling the volatility linkages through multi‐index dynamic multivariate GARCH analysis

AbstractThis paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be short‐lived, indicated by a relatively low DCC‐GARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCC‐GARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through real‐world examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the short‐lived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.

Heterogeneity, Jumps and Co-Movements in Transmission of Volatility Spillovers Among Cryptocurrencies

Abstract We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.

Comparison of Linear Regression and LSTM (Long Short-Term Memory) in Cryptocurrency Prediction

Abstract Cryptocurrency, particularly Bitcoin, has become a major topic in the financial and digital trading sectors due to its ability to facilitate direct transactions without intermediaries and the transparency offered by blockchain technology. However, the high volatility of Bitcoin prices necessitates accurate prediction methods to support better investment decisions. This research aims to compare the accuracy of Linear Regression and Long Short-Term Memory (LSTM) methods in predicting Bitcoin prices using historical data from Yahoo Finance. The research process begins with the collection of historical Bitcoin price data from September 17, 2014, to July 15, 2024, followed by data processing that includes cleaning and splitting the dataset into training and test data. Linear Regression and LSTM models are applied to the training data and tested to evaluate their performance in price prediction. The research findings show that the LSTM model significantly outperforms the Linear Regression model in terms of prediction accuracy. The LSTM model records much lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), as well as perfect R² scores on both datasets, demonstrating its high precision in prediction. In contrast, the Linear Regression model shows higher errors and lower explanatory power of data variability. These findings indicate that LSTM is more effective in capturing temporal patterns and Bitcoin price fluctuations, offering better accuracy and potentially being more suitable for future cryptocurrency price analysis, providing better guidance for investors in this highly dynamic market.

Fuzzy Clustering Algorithm for Trend Prediction of The Digital Currency Market

Digital currencies, such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT), have been attracting the interest of investors and speculators. Over the last several years, the exponential growth in the value of digital currency has captured the interest of many individuals who see it as an attractive investment opportunity. After all, investors must deal with the expected volatility of Bitcoin prices as part of their investments. The future development of cryptocurrency can be challenging to forecast because of the extreme unpredictability and disorder of external events. In this research, fuzzy models for cryptocurrency price forecasting using a level set-based Fuzzy Clustering Based on Multi-Criteria Decision-Making (FC-MCDM). Compared to linguistic and functional fuzzy clustering, the construction and processing of fuzzy rules in a multi-criteria decision-making-based collection set differ. Based on level sets, the model produces the weighted average of the functions that active fuzzy rules provide as output. In the model's outputs, the activation levels of the fuzzy rules are represented directly by the output functions. Computational experiments are carried out to test the efficacy of the level-set approach for one-step-ahead prediction of cryptocurrency closing prices. Meanwhile, level set-based fuzzy clustering outperforms the other methods when the direction of price change evaluates performance

Interrelations between bitcoin market sentiment, crude oil, gold, and the stock market with bitcoin prices: Vision from the hedging market

PurposeThe purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states.Design/methodology/approachThis study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability.FindingsKey findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones.Originality/valueThis study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.

Penalty shootouts and bitcoin trading

ABSTRACT Soccer is the most popular sport in the world, and bitcoin is a global asset. Penalty shootouts represent an important stage in a soccer match and generate an enormous amount of public interest and attention. Bitcoin trading experiences a significant drop in volume and volatility during penalty shootouts at major international soccer tournaments. After the conclusion of penalty shootouts, the intensity of bitcoin trading exhibits a swift and a strong rebound. Significant fluctuations in trading volume and volatility of bitcoin around penalty shootouts are primarily driven by the variation in number of trades rather than in trade size.

Stochastic Patterns of Bitcoin Volatility: Evidence across Measures

Stochastic Patterns of Bitcoin Volatility: Evidence across Measures

On the robust drivers of cryptocurrency liquidity: the case of Bitcoin

This study aims to identify the factors that robustly contribute to Bitcoin liquidity, employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry, investor attention, macroeconomic fundamentals, and global stress and uncertainty. To construct liquidity metrics, we compile 60-min high-frequency data on the low, high, opening, and closing exchange rates of Bitcoin against the US dollar. Our empirical investigation is based on the extreme bounds analysis (EBA), which can resolve model uncertainty issues. The results of Leamer’s version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity. With the Sala-i-Martin’s variant of EBA, however, four more variables, (viz. Bitcoin’s negative returns, trading volume, hash rates, and Google search volume) are also labeled as robust determinants. Accordingly, our evidence confirms that Bitcoin-specific factors and developments, rather than global macroeconomic and financial variables, matter for explaining its liquidity. The findings are largely insensitive to our proxy of liquidity and to the estimation method used.

The effect of policy uncertainty on the volatility of bitcoin

PurposeThis paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.Design/methodology/approachIn this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.FindingsThe results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.Originality/valueThis study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

Bitcoin Volatility and Intrinsic Time Using Double-Subordinated Lévy Processes

We propose a doubly subordinated Lévy process, the normal double inverse Gaussian (NDIG), to model the time series properties of the cryptocurrency bitcoin. By using two subordinated processes, NDIG captures both the skew and fat-tailed properties of, as well as the intrinsic time driving, bitcoin returns and gives rise to an arbitrage-free option pricing model. In this framework, we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Chicago Board Options Exchange VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both volatility measures are compared to the volatility based on the historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures the observed in-sample volatility.

The Impact of Cryptocurrency Volatility Dynamics on the Islamic Equity Market: The Case of Emerging Asia

This paper investigates the impact of the realized volatility of positive and negative intraday Bitcoin returns on the sensitivity of Shariah-compliant stocks’ orthogonalized returns. We identify the impact in different market states and find that Bitcoin’s upside volatility negatively affects the returns of Islamic equities. The paper contributes to uncovering the properties of a niche Islamic Emerging Asian equity market. The findings offer important implications for investors’ diversification strategies.

Factors affecting the volatility of bitcoin prices

To explore the impact of factors from the traditional financial market, such as economic policy uncertainty, oil prices, the NASDAQ index, and gold prices, to identify factors contributing to Bitcoin volatility. This study uses traditional OLS (ordinary least squares) regression analysis to examine how different external factors affect Bitcoin price volatility from January 2014 to March 2023. By employing a comprehensive approach to recognize the distinctive characteristics of the Bitcoin market, namely, 24-hour trading and the short duration of its existence, we’ve included a wide spectrum of data to ensure a cohesive comparison with other financial datasets. The findings of the statistical analysis indicate that EPU and the NASDAQ index promote positive fluctuations in Bitcoin volatility, whereas gold prices act as a dampener. Conversely, we do not find empirical support for the influence of energy prices, such as oil, on Bitcoin volatility. These findings indicate that we should not undervalue Bitcoin in any financial transaction scenario. It means that all stakeholders should treat the issue of Bitcoin volatility more seriously, even including governments, who should actively regulate the Bitcoin market, and investors, who should recognize the dangers of this volatility, make rational decisions based on individual circumstances, and employ flexible trading strategies.

Dynamic linkages among bitcoin, equity, gold and oil: An implied volatility perspective

Bitcoin Implied Volatility, derived from the recently launched options on Bitcoin, provides a new forward-looking measure of uncertainty in financial markets. This paper explores its long-run and short-run relationships with the implied volatilities of equity, gold, and oil. The NARDL model reveals a long-run relationship among Bitcoin volatility and others. Kyrtsou-Labys Nonlinear Causality tests established pair-wise causality between the implied volatilities under study. Our findings highlight the integration of Bitcoin markets with global financial markets, indicating the coming of age of the Bitcoin market.

Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network

The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.

Evaluating Bitcoin Price Volatility Forecasting Models: A Comparative Analysis

This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's important to emphasise the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme money or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Corrected Triple Correction Method, CNN and Transfer Learning for Prediction the Realized Volatility of Bitcoin and E-Mini S&P500

Corrected Triple Correction Method, CNN and Transfer Learning for Prediction the Realized Volatility of Bitcoin and E-Mini S&P500

Financial Risks of Business Management of Cryptocurrency Operations

Bitcoin is an asset with high risks, and a significant part of its volatility can be explained by the speculative component. Parametric variance-covariance (VaR) methods are not applicable for assessing the risks of bitcoin investment, since log returns are not distributed according to the normal law. Autoregressive risk assessment models (such as ARIMA-GARCH) for bitcoin volatility overestimate risks at times of sharp exchange rate changes and they underestimate them at times of less significant rate changes compared to historical volatility. The grid search for the smoothing parameter in the exponentially weighted moving average method is potentially interesting for modeling the risks of bitcoin investment. This makes it possible to fully take into account the autocorrelation of the bitcoin rate to the levels of previous periods and the volatility of the asset. As a conclusion, there are currently no econometric models that can explain and forecast the volatility of bitcoin in the medium and short term, considering the available factors in the market.