The aim of the paper is twofold: first, to examine the hedging effectiveness of cryptocurrencies and cryptocurrency portfolios for European equities in bearish and bullish market conditions, and second, to contrast cryptocurrencies with gold as a safe haven asset. To this end, daily data from 2018 to 2022 were employed in a linear and nonlinear Autoregressive Distributed Lag (ARDL) framework. The findings have significant implications for investors, financial intermediaries and regulators.
This paper proposes a nonparametric directional dependence by using the local polynomial regression technique. With data generated from a bivariate copula having a nonmonotone regression structure, we show that our nonparametric directional dependence is superior to the copula directional dependence method in terms of the root-mean-square error. To validate the directional dependence with real data, we use the log returns of daily prices of Bitcoin, Ethereum, Ripple, and Stellar. We conclude that our nonparametric directional dependence, by using the local polynomial regression technique with asymmetric-threshold GARCH models for marginal distributions, detects the directional dependence better than the copula directional dependence method by an asymmetric GARCH model.
This paper investigates the impact of COVID-19 on the cryptocurrency market. It empirically examines the level of volatility and the dynamic conditional correlations among cryptocurrencies pre-COVID-19 and during COVID-19. We find significant dynamic conditional correlations among cryptocurrencies and that the level of volatility is higher during COVID-19 than pre-COVID-19.
The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s— t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.
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.
This paper applies the DCC-MGARCH model to investigate the role of Bitcoin as a hedge for Islamic stocks in Asia during the COVID-19 pandemic. Despite being a highly volatile cryptocurrency, evidence of low dynamic correlation between Bitcoin and Islamic stocks is confirmed across the Asian region. We find that Bitcoin’s diversification benefits improve towards the later stages of the pandemic when countries were transitioning to an endemic phase.
In this article, the MGARCH-DCC model is utilised to compare the usefulness of Bitcoin, gold, and crude oil as a hedge and safe haven for the US Islamic stock index. We utilised daily data from August 2014 to April 2022, which covers the most recent COVID-19 epidemic and the Russia-Ukraine conflict. We find the dynamic correlation between Bitcoin and the US Islamic stock index to be low and often negative during major economic and political events, showing that Bitcoin is a safe haven and hedging instrument, especially during the pandemic period. However, we find that Bitcoin is very volatile, limiting its use as a safe haven and hedging instrument compared to gold. Gold is more stable and negatively correlated with the US Islamic stock index, making it more appropriate as a diversifier and hedging instrument. Adding gold to the US Islamic stock index portfolio reduces the portfolio’s risk.
Maximum extractable value (MEV) has been extensively studied. In most papers, the researchers have worked with the Ethereum blockchain almost exclusively. Even though, Ethereum and other blockchains have dynamic gas prices this is not the case for all blockchains; many of them have fixed gas prices. Extending the research to other blockchains with fixed gas price could broaden the scope of the existing studies on MEV. To our knowledge, there is not a vast understanding of MEV in fixed gas price blockchains. Therefore, we propose to study Terra Classic as an example to understand how MEV activities affect blockchains with fixed gas price. We first analysed the data from Terra Classic before the UST de-peg event in May 2022 and described the nature of the exploited arbitrage opportunities. We found more than 188K successful arbitrages, and most of them used UST as the initial token. The capital to perform the arbitrage was less than 1K UST in 50% of the cases, and 80% of the arbitrages had less than four swaps. Then, we explored the characteristics that attribute to higher MEV. We found that searchers who use more complex mechanisms, i.e. different contracts and accounts, made higher profits. Finally, we concluded that the most profitable searchers used a strategy of running bots in a multi-instance environment, i.e. running bots with different virtual machines. We measured the importance of the geographic distribution of the virtual machines that run the bots. We found that having good geographic coverage makes the difference between winning or losing the arbitrage opportunities. That is because, unlike MEV extraction in Ethereum, bots in fixed gas price blockchains are not battling a gas war; they are fighting in a latency war.
Abstract This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.
Chiang-Ching Tan, Pick-Soon Ling, Siew-Ling Sim, Kelvin Lee Yong Ming
This study examined the capabilities of six cryptocurrencies as a hedge and safe haven against the stock indices and foreign exchange rate in the East Asia-5 markets. According, MGARCH-DCC was adopted and implemented in data collection processes together with Rathner and Chiu regression method, which spanned from April 2013 to December 2019. The results revealed that these cryptocurrencies had dissimilar hedging and safe haven capabilities across various stock indices and exchange rates in the East Asia-5 markets. In particular, Bitcoin, Litecoin, and Ethereum offered strong hedge properties on most of the East Asia-5 equity indices. Moreover, Bitcoin and Litecoin only provided a safe haven for Japanese Yen currency, while Taiwanese equity indices and Chinese Yuan currency can be safely protected via an investment into Stellar.
In recent years, the digital world is fast speeding developed from decentralised concept to blockchain, then to cryptocurrency. Especially, cryptocurrency is a popular trending in recent decades that attracts different experts from various field. Its high volatility has been attracted plenty of investors while also brings the difficulty for realizing the price forecasting. On this basis, this study uses public cryptocurrency dataset and three analytical models to predict the direction of cryptocurrency’s price. To be specific, three underlying assets covering large proportion in cryptocurrency are selected, i.e., Bitcoin, Ethereum and Dogecoin. According to the analysis, the prediction results of different models and approaches will be presented. At the end of study, it gains that the optional model with appropriate hyperparameters based on the judgement of metrics values, which offers relevant suggestions for future works. These results shed light on guiding further exploration of cryptocurrency price prediction in terms the state-of-art machine learning scenarios.
Russia massively invaded Ukraine on February 24, 2022, unavoidably having an effect on the world economy and finance. This paper uses the event study to research the short-term response of the February 2022 top 5 variable-price cryptocurrencies (BTC, ETH, BNB, XRP, SOL) to the Russia-Ukrainian war under the constant mean model. The cryptocurrency volatility was dramatic during the event window, and cryptocurrencies did not show the characteristics of safe haven. Overall, the result of the effect of the Russia-Ukraine war on the cryptocurrency market was negative, with the least negative impact on SOL and the most negative impact on BNB, XRP. Finally, Using the different event window analysis, it shows the cryptocurrency market return volatility rebounded, but it does not sufficiently indicate there is a positive trend in the cryptocurrency market after the event. The analysis of this paper can provide some help for cryptocurrency investors in the event of unforeseen circumstances. And in the data selection, this paper doesn’t consider stablecoins.
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.
The global current situation continues to be turbulent. International crises like the Covid-19 epidemic and the continuing Russian-Ukrainian war have thrown the global economy for a loop. As a result, global economic policy uncertainty has spiked due to the resulting spike in energy prices and economic disruptions. During the outbreak of Covid-19, prices of bitcoin (BTC) have moved higher, but its hedging effect is weakening. Also, combined with rising global inflation expectations and the constant rate hikes by central banks against inflation, bitcoin's hedging effectiveness is waning due to its strong correlation with equities. Furthermore, with the outbreak of the Russian-Ukrainian war, the price of gold continued to rise, and the relationship between gold and the global financial market decreased, confirming gold's diversification ability in a crisis. Simultaneously, the link between gold and bitcoin has weakened marginally. Ultimately, preliminary evidence suggests that gold and bitcoin can be used as complements, rather than substitutes, for diversification purposes during a crisis. This article will construct a portfolio about bitcoin and gold, and examine how individual gold and bitcoin and this portfolio performed as hedging assets throughout the Covid-19 pandemic and the Russian-Ukrainian war.
Price prediction of cryptocurrencies is bound to get more opportunities for investors engaged in digital currency-related industries in order to earn more revenue. In the traditional forecasting methods, the problem of the high volatility of bitcoin price needs to be effectively solved, making the forecasting accuracy become low and ineffective. Due to the rapid development of artificial intelligence technology, more and more relevant algorithms were used for cryptocurrency price research. This study would compare and analyze the prediction effect of the ARIMA time-series model, the Random Forest algorithm of machine learning, and the LSTM algorithm of deep learning algorithm on cryptocurrencies price prediction to assist investors in making investment decisions. In this paper, five years of time-series data of Bitcoin, Ether, and Dogecoin is obtained from 2018 to 2022. Then, the training set and testing set are separated with 0.8:0.2 to test ARIMA, Random Forest, and LSTM algorithms. To evaluate the model, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and decidability coefficient (R2) are chosen as metrics to measure the prediction of each prediction model precision. Comparing the experimental results, the prediction accuracy of LSTM is better than that of Random Forest, and the prediction accuracy of Random Forest is better than ARIMA model. These results shed light on guiding further exploration of significant to cryptocurrency industry practitioners and visitors.
Contemporarily, cryptocurrency has a high market value, and the price of cryptocurrency fluctuates dramatically. This article analyzes the parameters effects of the LSTM model on Bitcoin price prediction accuracy based on Python and modules of Numpy, Pandas, Keras, Tensorflow, and Sklern. The analysis clarifies the relationship between the accuracy of Bitcoin price prediction and different parameters in the LSTM model. It is discovered that when larger batch sizes are supplied at minor epochs, the accuracy of Bitcoin price prediction declines. Meanwhile, the number of neurons affects the accuracy. In addition, compared to lengths of 14, 30, and 60, the prediction error grows greater when a single time sequence is 7 in length. Apart from that, at present, using closing prices from the past two years rather than the past 1 year, 3 years, or 5 years can make predictions more accurate. These findings shed light on recommendations for adjusting various parameters in the development of the LSTM model for Bitcoin price prediction.
Currently, cryptocurrency has become a research and investment topic of great concern and attracted considerable attentions in a wide range of fields. Stocks are well known as a form of investment, and there are countless studies on how its price trends. Cryptocurrencies, however, are not easily predicted due to their extremely high volatility. This paper implements the forecasting results by taking three major cryptocurrencies (i.e., Bitcoin, Ethereum, Dogecoin) as examples for the period starting from January 1st, 2020 to May 31st 2022. Four popular prediction models (XGBoost, LightGBM, GARCH, ARIMA) are applied in Python by training models and testing the prediction results. According to the analysis, XGBoost and LightGBM can forecast the future prices for all three cryptocurrencies exactly apart from the turning points when prices rise and drop suddenly. Although the predicting trends of GARCH, ARIMA have differences from the real price, they can forecast well except the unexpected situations such as COVID-19. Overall, relatively reliable and accurate prediction models can be provided for investors to apply and make wise decisions in investments based on the results of this study.
Contemporarily, blockchains and cryptocurrencies have gained their popularity among investors and hedge funder, where both have bright prospects. On this basis, cryptocurrencies have been used in trading more and more with the development of website and computer. In this case, their prices fluctuations do have great significance to the public. This paper chooses three machine learning model (i.e., XGBoost, LightGBM and Linear Model) to predict the price of three cryptocurrencies (i.e., Bitcoin, Dogecoin and Ethereum). To be specific, this study uses the data from 2020-01-01 to 2022-12-07, including close price, open price, high price, low price, and the volume of trading coins. According to the analysis, Linear Model can predict the price best, with well-fitted trend prediction and accurate price prediction. In addition, other models can also have good predictions but they are not better than Linear model. These results can help others to predict the price of cryptocurrencies and have a deep understanding of cryptocurrency and machine learning.
The following article explores the correlation between bitcoin and both stocks and gold.A Markov regimeswitching 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.
Adrian Moroșan, Oana Oprişan, Eduard Alexandru Stoıca, Cosmin Tileagă
Through our study, we studied the perception of the students of an economic faculty speciality which are at the end of their studies and who will soon become economists, and their attitude towards the cryptocurrencies. Their contacts inside or outside the university led to their professional development because they brought to their attention the widening of the sphere of finance through the prism of a new concept that appeared fifteen years ago, that of cryptocurrency. The main scope of the paper is to understand how students currently relate to cryptocurrencies, after going through all the subjects in the curriculum of their economic specialization. The methodology will involve the use of a structured interview. Important results of our study will be related to the fact that the female students interviewed, who, unlike almost all of the female students, are or say that they will be involved in trading cryptocurrencies in the near future and to the fact that an important part of their information regarding the cryptocurrencies is obtained from outside the faculty. We will recommend, knowing the current situation of the interviewed students, to the teachers who teach various disciplines in the specialization of which the interviewed students are part of that they could try, in the situation where the taught subjects allow it, to offer to the students who will come in the following years additional information about the cryptocurrencies.
Bu çalışmada, 2017M1-2022M1 dönemleri arasındaki veriler kullanılarak Bitcoin (BTC) ile Karbon Emisyonu (CO2) arasındaki ilişki incelenmiştir. Son zamanlarda yapılan çalışmalara istinaden kripto para ve enerji piyasalarının spekülatif ve kırılgan yapıya sahip olduğu ve bundan dolayı değişkenlerin doğrusal olmayan bir forma sahip olabileceği konusuna dikkat çekildiği gözlenmektedir. Dolayısıyla bu bilgiler çerçevesinde çalışmada öncelikle Luukkonen vd. (1988), Harvey vd. (2008) doğrusallık testi ve Kapetanios vd. (2003) doğrusal olmayan birim kök testi ile değişkenlerin doğrusallık sınaması yapılmaktadır. Akabinde değişkenlerin doğrusal olmayan forma sahip olduğu tespit edildiği için çalışmada Kapetanios vd. (2006) Doğrusal Olmayan Eşbütünleşme analizi kullanılmaktadır. Kapetanios vd. (2006) testi bulgularına göre BTC ile CO2 arasında uzun dönemde doğrusal olmayan bir eşbütünleşme ilişkisi olduğu tespit edilmektedir. Bu durum BTC ile CO2 arasındaki ilişkinin uzun dönemde dengeye doğrusal olmayan bir şekilde yakınsadığı sonucunu göstermektedir. Değişkenler arasında doğrusal olmayan eşbütünleşme ilişkisini tespit ettikten sonra bu ilişkinin yönünü belirlemek amacıyla yapılan Granger nedensellik testi sonucuna göre ise Bitcoin’den Karbon Emisyonuna doğru tek yönlü nedensellik olduğu tespit edilmektedir. Bu bulgu, BTC üretiminde kullanılan enerjinin çevre dostu kaynaklardan elde edilmesine yönelik politikaların benimsenmesi gerektiği biçiminde yorumlanabilir.
In this study, the RiskMetrics method is used to estimate Value at Risk for two exchange rates: BitCoin/dollar and the South African Rand/dollar. Value at Risk is used to compare the riskiness of the two currencies. This is to help South Africans and investors understand the risk they are taking by converting their savings/investments to BitCoin instead of the South African currency, the Rand. The Maximum Likelihood Estimation method is used to estimate the parameters of the models. Seven statistical error distributions, namely Normal Distribution, skewed Normal Distribution, Student’s T-Distribution, skewed Student’s T-Distribution, Generalized Error Distribution, skewed Generalized Error Distribution, and the Generalized Hyperbolic Distributions, were considered when modelling and estimating model parameters. Value at Risk estimates suggest that the BitCoin/dollar return averaging 0.035 and 0.055 per dollar invested at 95% and 99%, respectively, is riskier than the Rand/dollar return averaging 0.012 and 0.019 per dollar invested at 95% and 99%, respectively. Using the Kupiec test, RiskMetrics with Generalized Error Distribution (p > 0.07) and skewed Generalized Error Distribution (p > 0.62) gave the best fitting model in the estimation of Value at Risk for BitCoin/dollar and Rand/dollar, respectively. The RiskMetrics approach seems to perform better at higher than lower confidence levels, as evidenced by higher p-values from backtesting using the Kupiec test at 99% than at 95% levels of significance. These findings are also helpful for risk managers in estimating adequate risk-based capital requirements for the two currencies.
Danai Likitratcharoen, Pan Chudasring, Chakrin Pinmanee, Karawan Wiwattanalamphong
In recent years, the cryptocurrency market has been experiencing extreme market stress due to unexpected extreme events such as the COVID-19 pandemic, the Russia and Ukraine war, monetary policy uncertainty, and a collapse in the speculative bubble of the cryptocurrencies market. These events cause cryptocurrencies to exhibit higher market risk. As a result, a risk model can lose its accuracy according to the rapid changes in risk levels. Value-at-risk (VaR) is a widely used risk measurement tool that can be applied to various types of assets. In this study, the efficacy of three value-at-risk (VaR) models—namely, Historical Simulation VaR, Delta Normal VaR, and Monte Carlo Simulation VaR—in predicting market stress in the cryptocurrency market was examined. The sample consisted of popular cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), and Ripple (XRP). Backtesting was performed using Kupiec’s POF test, Kupiec’s TUFF test, Independence test, and Christoffersen’s Interval Forecast test. The results indicate that the Historical Simulation VaR model was the most appropriate model for the cryptocurrency market, as it demonstrated the lowest rejections. Conversely, the Delta Normal VaR and Monte Carlo Simulation VaR models consistently overestimated risk at confidence levels of 95% and 90%, respectively. Despite these results, both models were found to exhibit comparable robustness to the Historical Simulation VaR model.