The recent global crises have heightened financial market instability, surging the need for diversification, hedging, and safe haven assets to mitigate stock market risks. This study employs a Quantile Vector Autoregression (Q-VAR) approach to analyze the interconnectedness between gold-backed cryptocurrencies and G7 stock market indices during crises spanning from December 1, 2020, to July 5, 2023. Our findings indicate a robust association between digital gold and financial assets, with a total connectedness index (TCI) of 58.64%. Remarkably, G7 stock indices emerge as significant contributors to market fluctuations compared to digital assets, exerting influence ranging from 24% to 37%, thereby underscoring the potential of gold-backed cryptocurrencies for effective diversification strategies. Dynamic analysis during crises indicates the pivotal role of DGX as a safe haven, alongside identifying NIKKEI as a significant net receiver. Furthermore, the total net directional connectedness examination corroborates the status of gold-backed cryptocurrencies as net receivers, reaffirming their safe-haven abilities. Intriguingly, an in-depth examination across quantiles validates symmetrical dynamic connectedness, with G7 indices predominantly functioning as net transmitters of spillover. Our empirical findings underscore the compelling safe-haven potential in gold-backed cryptocurrencies, offering valuable insights for investors, policymakers, and portfolio optimization during turbulent market conditions.
In this article, we attempt to analyze and compare the safe-haven features of gold, Bitcoin and gold-backed cryptocurrency against the stock and banking indices of G7 countries during the outbreak of adverse events. To do so, we examine dynamic relationships between different assets and we compute optimal hedge ratios for different couples using the corrected Asymmetric Dynamic Conditional Correlation-Exponential Generalized Autoregressive Conditional Heteroscedasticity and corrected Asymmetric Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity models. We clearly show that gold and gold-backed cryptocurrency maintain higher weights in optimal portfolios compared to Bitcoin. We also report that shocks due to unexpected events increasingly affect dynamic correlations, asset weights and hedge ratios. This underscores the need for regular demand for rebalancing the hedge positions and effective risk management. We thereafter show that the relationship between Bitcoin (gold-backed cryptocurrency) and G7 indices is highly affected by the outbreak of COVID-19 pandemic. Such findings highlight the hedging and safe-haven features of different asset classes against stock markets. They could have insightful implications for investors who want to minimize investment risks and policymakers who are worried about the financial consequences of different unexpected events.
This paper investigates the hedging and safe haven capacity of gold and Bitcoin against the G7 stock market indices during the COVID-19 pandemic, the Russia-Ukraine military conflict, and the Silicon Valley Bank collapse. Using a novel Quantile-VAR connectedness approach, the results show that, at the median quantile, both gold and Bitcoin act as effective hedges during normal market conditions and strong safe-haven assets during the three crises. Gold emerges as the most prominent safe haven asset, outperforming Bitcoin, especially during the war and the SVB collapse. Among the G7 stock market indices, the Japanese and the American stocks may be used as risk diversifiers during crises. As for the rest of the G7 stocks, they are regarded as ârisk-onâ investments. Next, we assessed the robustness of our results at various quantiles. We found them to be generally consistent with the outcomes obtained at the median quantile, with one exception related to the S&P500.The results show that the repercussions of the COVID-19 pandemic and the war are much stronger than the American banking crisis.
Abstract In the blockchain world, proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger. The hashrate, a measure of the computational power directed toward securing a blockchain through proof-of-work consensus, is a fundamental measure of preventing various attacks. This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain. We use vector error correction modeling to analyze the endogenous relationships between the hashrate, Bitcoin price, and transaction fee, revealing the need for an additional variable to achieve our aim. Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes. This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.
Sergio Luis Nåñez Alonso, Javier Jorge-VĂĄzquez, Miguel Ăngel Echarte FernĂĄndez, David Sanz Bas
Abstract A number of financial bubbles have occurred throughout history. The objective of this study was to identify the main similarities between Bitcoin price behavior during bubble periods and a number of historical bubbles. Once this had been carried out, we aimed to determine whether the solutions adopted in the past would be effective in the present to reduce investorsâ risk in this digital asset. This study brings a new approach, as studies have previously been conducted analyzing the similarity of Bitcoin bubbles to other bubbles individually, but these were not conducted in such a broad manner, addressing different types of bubbles, and over such a broad time period. Starting from a dataset with 9967 records, a combined methodology was used. This consisted of an analysis of the standard deviations, the growth rates of the prices of the assets involved, the percentage increase in asset prices from the origin of the bubble to its peak and its fundamental value, and, finally, the bubble index. Lastly, correlation statistical analysis was performed. The results obtained from the combination of the above methods reveal the existence of certain similarities between the Bitcoin bubbles (2011, 2013, 2017, and 2021) and the tulip bubble (1634â1637) and the Mississippi bubble (1719â1720). We find that the vast majority of the measures taken to avoid past bubbles will not be effective now; this is due to the digital and decentralized nature of Bitcoin. A limitation of the study is the difficulty in making a comparison between bubbles that occurred at different historical points in time. However, the results obtained shed light and provide guidance on the actions to be taken by regulators to ensure the protection of investors in this digital asset.
Elie Bouri, Mahdi Ghaemi Asl, Sahar Darehshiri, David Gabauer
Abstract This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
Abstract Cryptocurrencies have rapidly become popular as digital assets, and as the market evolves, it is of great importance to understand their volatility and risk behavior. They present specific challenges and opportunities given that are operating within a decentralized and fast-changing ecosystem. Thus, their volatility affects risk management, investment strategies, and market stability. Cryptocurrency volatility can create both opportunities and risks. While it can provide substantial returns, it also presents challenges in terms of investment strategy, regulatory frameworks, business operations, and economic stability. As the cryptocurrency market matures, itâs likely that solutions to manage volatility will evolve, but it remains a key concern for participants in the ecosystem. In this respect, the aim of the paper is to examine the volatility behavior of the main cryptocurrencies (Bitcoin, Ethereum, and Litecoin), for a recent period, i.e. from June 2018 to June 2023. Using both traditional and advanced GARCH models, the results show that these cryptocurrencies experience periods of high and low volatility, but there is no significant asymmetry effect in their responses. This suggests a balanced risk-return profile for investors. Furthermore, there is no evidence for risk premium within the sample, that is no link between risk and return. Additionally, past volatility has a greater impact on current volatility than new information, since GARCH coefficients are significantly higher than the ARCH coefficients. These insights can help investors, policymakers, and researchers to manage the cryptocurrency markets more effectively.
Abstract Research purpose. This study analysed the three cryptocurrencies with the largest market capitalization: Bitcoin, Ether (cryptocurrency built upon the Ethereum project's blockchain technology), and Binance coin, which account for 60% of the total cryptocurrency market capitalization. The purpose of this research was to measure the impact of monetary policy on the price of these cryptocurrencies using an adjusted R squared. Design / Methodology / Approach. As dependent variables, we used interest rates controlled by the European Central Bank and the Federal Reserve and reports from the European Central Bank and the Federal Open Market Committee. A robust Elastic Net Regression with Autoregressive Integrated Moving Average (ARIMA) residuals machine learning approach was applied to obtain robust regression coefficients and corresponding standard errors. To ascertain the robustness of the model, a technique known as rolling window cross-validation was employed. Findings. The results of this study show that monetary policy decisions and announcements significantly impact the price of cryptocurrencies. The impact on cryptocurrencies is likely to be significant both in the period of economic stability (2018-2020) and in the period of economic shocks (2020-2022). This relationship is likely to be indirect, acting through investor sentiment. Originality / Value / Practical implications. The results of this study may be useful to monetary policymakers, as they reveal the link between their actions and the price of cryptocurrencies. Our model will also be useful for mutual fund managers and private investors, as they can anticipate the price dynamics of cryptocurrencies when assessing monetary policy frameworks.
This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis.
Abstract Non-fungible Tokens (NFTs), represent a revolution in the digital ownership paradigm. NFTs are a kind of digital asset built on blockchain technology, most commonly the Ethereum blockchain, that validate the uniqueness and ownership of a unique digital item in question. Each NFT carries specific information or attributes that make it original and non-fungible. Unlike cryptocurrencies like Bitcoin or Ethereum, which are identical to each other, non-fungible tokens cannot be exchanged on a like-for-like basis making them non-fungible. NFTs are traded for cryptocurrencies via online trading platforms. Investment in NFTs can present a risky situation due to the large volatility of the assets in a quite short time. This article focuses on identification of key aspects that influence decision making process of potential investors who are considering buying non-fungible tokens as an investment tool in the Czech Republic. From the point of view of investment decision-making, the primary factors appear to be the expected income from the investment, its payback period, and the risk that the investor undertakes. It has been proven that there is a degree of dependence between gender and the mentioned decision-making factors. The research showed that men are more inclined to make decisions based on expected returns, while women are more likely to make decisions based on perceived risk.
Purpose This study investigates the effect of the day of the week on the volatility of cryptocurrencies. Thus, we reveal investors' perceptions of the day of the week. Design/methodology/approach The EGARCH model consists of the day of the week for 2019â2022 and the volatility of 11 cryptocurrencies. Findings Empirical results show that the weekend harms cryptocurrency volatility. Also, there was positive cryptocurrency volatility at the beginning of the week. Our findings show that weekdays and weekends significantly impact cryptocurrency volatility. Besides, cryptocurrency investors are sensitive to market movements, disclosures, and regulations during the week. Holiday mode and cognitive shortcuts may cause cryptocurrency traders to remain passive on weekends. Research limitations/implications This study has some limitations. We include 11 cryptocurrencies in the analysis by limiting cryptocurrencies according to market capitalizations. Further studies may analyze a larger sample. In addition, further studies may examine the moderator and mediator effects of other financial instruments. Practical implications The empirical results have research, social and practical conclusions from different aspects. Our analysis may contribute to determining trading strategies, risk management, market efficiency, regulatory oversight, and investment decisions in the cryptocurrency market. Originality/value The calendar effect in financial markets has extensive literature. However, cryptocurrencies' weekday and weekend effect needs to be adequately analyzed. Besides, studies analyzing cryptocurrency volatility are limited. We contribute to the literature by investigating the impact of days of the week on cryptocurrency volatility with a large sample and current data.
Disclosure and transparency are two critical components in the green financing sector, especially the green bond segment. Compared to green instruments like green credit, green bond issuances facilitate information dissemination and reduce information asymmetry. Still, concerns stemming from numerous macro-level and firm-level factors impede market advancement. Investors are restrained from green bond financing owing to a fear of potential greenwashing. The nascency of the market, resulting in inadequate disclosure regimes and measurement challenges, exacerbates the problem. Can we find a solution to tackle the dilemma of greenwashing and information asymmetry using emerging, sophisticated technologies? Assessing the major theoretical underpinnings, this chapter presents a comprehensive landscape of how technologies like distributed ledger technologies, blockchain, the internet of things, artificial intelligence, machine learning, and the like fit into the green debt market. While following a theoretical approach, collating research, and the green bond market developments, the authors initiate an investigation into how technology can manage disclosure biases. The assessment signifies the role of technology, specifically FinTech, blockchain, and AI technologies, in spotting greenwashing and information asymmetry.
ÎΔÏÏγία ÎÎżÏ ÏΜαÏÎ¶ÎŻÎŽÎżÏ , Dimitrios Farazakis, Ioannis Mallidis, Christos Floros
This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoinâs susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoinâs volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin.
Abstract We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate stateâofâtheâart machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms econometric benchmarks in terms of forecasting precision for both inâ and outâofâsample forecasts. We find that both deep learning architectures as well as complex layers, such as LSTM, do not increase the precision of daily forecasts. Specifically, a simple recurrent neural network describes a sensible choice for forecasting daily return series.
The bubbles and spikes in cryptocurrency prices increase considerably the risk on investments in these assets. In the traditional time series literature bubbles are viewed as nonstationary and non-estimable components of a process. In this paper, we adopt a different approach and consider the bubbles as inherent features of a strictly stationary causal-noncausal (mixed) Vector Autoregressive (VAR) process. This approach allows us to model and estimate the common bubbles and spikes in cryptocurrency prices. It also provides us linear combinations of cryptocurrencies that eliminate common bubbles analogously to the cointegrating vectors eliminating common trends in unit root processes. They are used to build cryptocurrency portfolios immune to the risk of common bubbles that ensure stable investment strategies. The mixed VAR model is estimated from the US Dollar prices of Bitcoin, Ethereum, Ripple, and Stellar over the period 2017â2019. We document the common bubbles and illustrate the behavior of bubble-free portfolios.
We present tail risk analysis of cryptocurrencies (Bitcoin, Ethereum and Litecoin), non-fungible tokens, stocks (FTSE 100 and S&P 500) and Gold from November 12, 2017 to March 31, 2022 using conditional model-based Value-at-Risk (VaR). We explored which model specification and distributional innovation could best capture the tail risk in these assets. Using the VaR and other risk metrics, we showed that there is no superior model/metric for capturing tail risk. We found that, for all the assets, non-Gaussian distributional assumptions best modelled the asymmetry and fat-tails in the distributions of the returns; though there was more homogeneity in the distributional assumptions for Gold unlike the other assets. Our research is crucial for internal risk modelling and may increase global investor confidence for those who blend conventional and unconventional assets. Also, this study can help investors make informed decisions about asset allocation and risk tolerance in the events of extreme market conditions. Understanding the tail risks in financial assets can help investors hedge and diversify against risk in their portfolios. The theoretical implications also show a trade-off between the different assets as the presence of tail risk reflect the potential of returns, yet possible losses in the presence of extreme events. Last, the findings reinforce the need for risk managers to re-focus their attention to a set of superior models rather than a single best model for risk assessment.
S. Saraswathi, J S Sridhala, A. Elavazhagan, Jasbir Singh Sabharwal · 5 authors
This research proposes an ensemble approach for Bitcoin price prediction, leveraging historical price data and sentiment analysis. The proposed ensemble approach combines the model with Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) to further improve the accuracy in prediction by considering dynamics in the market. The model also addresses the problem of generalization and overfitting, adaption to the changing, dynamic nature of the market. Historical price data and sentiment scores from the preprocessing of the text are combined to the ensemble framework. These data are then fed into GRU and BiLSTM models for training, as the data contain not only complex temporal patterns but also sentiment-driven trends. The ensemble strategy could be beneficial for the strengths of the models and for improving the performances of the predictors. Most importantly, features are engineered in terms of technical indicators, lagged variables, and external factors impacting the price of Bitcoin. Sentiment analysis with the news and on social media complements insight into market sentiment, which adds value to the prediction power of the model.
Purpose This paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the RussiaâUkraine conflict and the Silicon Valley Bank (SVB) collapse. Design/methodology/approach The authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the RussianâUkrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our resultsâ robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities. Findings The results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run. Originality/value This paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the RussiaâUkraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets.
Abstract We aim to identify the determinants of nonâfungible tokens (NFTs) returns. The 10 most popular NFTs based on their price, trading volume, and market capitalisation are examined. Twentyâthree potential drivers of the returns of each NFT are considered. We employ a Bayesian LASSO model which takes into account stochastic volatility and leverage effect. The results indicate that NFTs returns are primarily driven by volatility and ethereum returns. We find a weak connection between NFTs returns and conventional assets, such as stock, oil, and gold markets.
Abstract This study examines the nexus between the good and bad volatilities of three technological revolutionsâfinancial technology (FinTech), the Internet of Things, and artificial intelligence and technologyâas well as the two main conventional and Islamic cryptocurrency platforms, Bitcoin and Stellar, via three approaches: quantile cross-spectral coherence, quantile-VAR connectedness, and quantile-based non-linear causality-in-mean and variance analysis. The results are as follows: (1) under normal market conditions, in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellarâs negative volatilities; (2) Stellarâs negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails, respectively; and (3) the quantile-based causality results indicate that Bitcoinâs good (bad) volatilities can lead to bad (good) volatilities in all three smart technologies operating between normal and bull market conditions. Moreover, the Bitcoin industryâs negative volatilities have a bilateral cause-and-effect relationship with FinTechâs positive volatilities. By analyzing the second moment, we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner. As for Stellar, only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles, whereas good volatilities have no significant effect. Hence, the trade-off between Bitcoin and cutting-edge technologies, especially FinTech-related advancements, appear more broadly and randomly compared with the Stellar-innovative technologies nexus. The findings provide valuable insights for FinTech companies, blockchain developers, crypto-asset regulators, portfolio managers, and high-tech investors.
Purpose This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement. Design/methodology/approach This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015â31 July 2023. Findings The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic. Research limitations/implications The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality. Originality/value Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.
We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018â2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021â2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns.