This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. Investors employ a lot of macroeconomic indicators for their investment decision, and hence adopting a few macroeconomic indicators is not sufficient in capturing a change in economic states. Moreover, due to aggregation, macroeconomic indicators are not measured precisely. To overcome these problems, we employ a dynamic factor model and extract common factors from a large number of macroeconomic indicators. We find that the common factors are strongly linked to the cryptocurrency expected returns at a quarterly frequency, while we do not observe this relationship using macroeconomic indicators such as inflation and money supply. This suggests that macroeconomic information matters in a longer term, which contrasts with the previous literature that explores a short-term relationship. The cryptocurrency prices are not determined by macroeconomic fundamentals in a short-term period since speculators impact the prices. However, in a long-term period, the prices are more linked to macroeconomic fundamentals.
By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.
Indranil Ghosh, Esteban Alfaro, Matías Gámez, Noelia García
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.
Yang Wan, Yuncheng Song, Xinqian Zhang, Zhichao Yin
We explore the dynamics and determinants of volatility connectedness between cryptocurrencies and energy. We employed a block dynamic equicorrelation model and a group volatility connectedness measurement to measure the cross-equicorrelation and volatility connectedness between cryptocurrencies and energy. We also adopted dynamic model averaging to identify the time-varying drivers. The results suggest that changes in cross-equicorrelation between the two groups were affected by influential global events and increased after the COVID-19 pandemic. Volatilities were transmitted in both directions between cryptocurrencies and energy, but the transmission from energy to cryptocurrencies is by far the strongest. The driver identification implies that the factors related to cryptocurrencies and global financial markets had important roles in explaining the volatility connectedness from cryptocurrencies to energy in some periods after the COVID-19 pandemic, but the effects were marginal. In contrast, factors such as electricity consumption, cryptocurrency turnovers, and VIX were important in affecting the volatility connectedness from energy to cryptocurrencies, and the effects depended on factors and changed over time.
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.
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.
Kate Murray, Andrea Rossi, Diego Carraro, Andrea Visentin
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.
Çalışmanın amacı kripto para piyasasına öncülük eden Bitcoin ile seçilmiş altcoinler arasındaki ilişkinin ve nedenselliğin varlığını tespit ederek yatırımcıların kararlarına ışık tutmaktır. Bu amaç doğrultusunda çalışmada, Bitcoin (BTC) ile Ethereum(ETH), Binance Coin (BNB), Cardano (ADA) , Litecoin (LTC), Tron (TRX), Stellar (XLM), IOTA altcoinlerinin arasındaki eşbütünleşme ve nedensellik ilişkisi araştırılmıştır. Uzun dönemli ilişkinin var olup olmadığı Engle Granger Eşbütünleşme yöntemiyle, kısa dönemli ilişkinin var olup olmadığı Sıradan En Küçük Kareler Yöntemiyle (OLS), nedensellik ilişkisinin var olup olmadığı ise Toda Yamamato Nedensellik yöntemiyle test edilmiştir. Çalışma kapsamında veri seti olarak 14.06.2018-01.12.2021 tarihleri arasında kripto para birimlerinin günlük kapanış fiyatları kullanılmıştır. Araştırma sonucunda Engle Granger Testine göre BTC ile seçilen altcoinler arasında uzun dönemli eşbütünleşme ilişkisi bulunamamıştır. OLS testine göre bağımsız ve bağımlı değişkenler arasında kısa dönemli ilişkinin var olduğu sonucuna ulaşılmıştır. Toda Yamamato Testine göre BTC’den ADA, ETH, IOTA, TRX, XLM alt coinlerine doğru granger nedensellik olduğu, BTC’den BNB ve LTC coinlerine doğru granger nedensellik olmadığı ve BTC’den etkilenmedikleri sonucuna ulaşılmıştır.
We investigate the multifractal properties of daily price returns and trading volume variations in 35 cryptocurrencies by using the method of wavelet leaders prior and during the COVID-19 pandemic. The obtained results from the analysis of scaling exponent functions and multifractal spectrums show that, in general, price returns and trading volume variations exhibit multifractal properties prior to the COVID-19 pandemic and that they tend to exhibit monofractal behavior during the pandemic. As a result, the level of multifractality diminished during the COVID-19 for both price returns and trading volume variations. Since complexity in price returns and trading volume variations decreased during the pandemic, cryptocurrencies may offer an interesting investment during times of serious world economic downturns.
Numerous research have been done to predict cryptocurrency prices since cryptocurrency prices affect global economic and monetary systems. However, investigations using linear connection approaches and technical analysis indicators frequently fall short of providing an explanation for changes in the pattern of BitCoin pricing. This paper is proposed to study time-varying parameters with long short-term memory (LSTM). The study is investigated on a dataset retrieved from Binance from March 2022 to April 2022. The proposed LSTM used a variety of hyperparameter settings, particularly time parameters, to predict the cryptocurrency price (BTC/USDT) on the dataset. Additionally, it is evaluated in terms of mean absolute percentage error (MAPE) in comparison to smooth moving average (SMA), weighted moving average (WMA), and exponential moving averages (EMA). From the investigation, using the previous 3 days for prediction gives the lowest of the MAPE values and the proposed LSTM outperformed the other models. When considering the last three days' value of pricing, the indicated LSTM offers the best accurate prediction, with a MAPE percentage of 0.0927%.
This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student-t distribution, fits the cryptocurrency market’s price dynamics well.
In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. As it is often used in the expectile literature, estimation of the model parameters is based on the asymmetric normal distribution. Maximum likelihood estimates are obtained via an Expectation-Maximization algorithm using efficient M-step update formulas for all parameters. We evaluate the introduced method with both artificial data under several experimental settings and real data investigating the relationship between daily Bitcoin returns and major world market indices.
Blanka Łęt, Konrad Sobański, Wojciech Świder, Katarzyna Włosik
Stablecoins are a relatively recent phenomenon in the cryptocurrency market, gaining prominence particularly since 2018. These digital currencies are usually pegged to nonvolatile underlying assets, offering a solution to the problem of the high price volatility of nonstable cryptocurrencies such as Bitcoin. Therefore, stablecoins seem attractive to participants in the cryptocurrency market, especially in times of turmoil. This paper aims to measure the spillover effect of shocks in volatile cryptocurrencies (Bitcoin, Ethereum, Litecoin and the Cryptocurrencies Index) on the activity of investors in the stablecoin market (Tether, USD Coin, Binance USD, DAI, Paxos, Huobi USD, and Gemini USD). Using the spectral representation of variance decomposition, the paper measures the strength of the spillover effect and investigates whether the cryptocurrency market processes information rapidly or slowly. The results suggest that shocks in the volatile cryptocurrency market moderately drive the popularity of stablecoins. The spillover effects on stablecoin popularity are short-lived, as they are observed mostly within up to 3 days of the shock. The findings indicate that investors use stablecoins as safe haven assets after bad news in the volatile cryptocurrency market; however, they react more strongly to news related to individual cryptocurrencies and not so intensively to the general sentiment in the cryptocurrency market. Investors with larger capital are more resistant to shocks.
Today's discussion centers on whether cryptocurrencies may be used to pay for products and services in developed and underdeveloped nations. The role of cryptocurrencies as investment and speculative trading vehicles is also expanding. The cryptocurrency bitcoin serves as an illustration of such use. The first cryptocurrency to develop value without initially satisfying requirements and carrying any type of collateral in the form of traditional currencies is bitcoin. The analysis of the variables influencing the price of bitcoin is one of the most hotly debated subjects in the financial literature. The purpose of this study is to look into the connection between the price of natural gas and crude oil and the cryptocurrency Bitcoin. In this study, the effect of Brent oil, crude oil and natural gas prices on Bitcoin was examined. For the data containing the weekly time series for the period 05.01.2020-26.12.2021, FMOLS and DOLS tests were conducted, which show the coefficient of cointegration, causality and relationship. According to the findings of the study, according to the FMOLS test, 1% Bitcoin in brent oil price increases 0.000176% (probability values according to DOLS do not confirm the effect). Likewise, when we look at crude oil, according to FMOLS test, 1% Bitcoin in crude oil price increases 0.000180% (the probability values according to DOLS do not confirm the effect). When we look at the changes in the Bitcoin price, according to the DOLS test, a 1% increase in the Bitcoin price increases the Brent oil by 77.86132% (the probability values according to FMOLS do not confirm the effect).
Abstract As a financial innovation of the information age, cryptocurrency is a complex concept with clear advantages and disadvantages and is worthy of discussion. Exploring from a terrorism perspective, this study uses the time-varying parameter/stochastic volatility vector autoregression model to explore the risk hedging and terrorist financing capabilities of Bitcoin. Empirical results show that both terrorist incidents and brutality may explain Bitcoin price, but their effects are slightly different. Compared to terrorist brutality, terrorist incidents have a weaker impact on Bitcoin price, showing that Bitcoin investors are more concerned about the number of deaths than the frequency of terrorist attacks. In turn, the impact of Bitcoin price on terrorist attacks is negligible. Bitcoin is a potential means of financing terrorism, but it does not currently play an important role. Our research findings can help investors analyze and predict Bitcoin prices and help improve the theoretical system of anti-terrorist financing, helping to maintain world peace and security.
As a new type of digital currency, Bitcoin is considered as “future gold” by various scholars. Therefore, this study considers Bitcoin and gold as a group of hedging assets to conduct investment research and it also discusses the investment rules between Bitcoin and gold: prediction of the rise and fall of Bitcoin, comparison of the characteristics of Bitcoin and gold, and the impact of the transaction procedures of Bitcoin and gold on the final trading results, and formulates trading strategies through optimization algorithms. Then, four machine learning algorithms, i.e., LSTM, BP neural network, Adaboost, and Bagging, are introduced to predict the rise and fall of gold and Bitcoin the next day, and then, the entropy weight method is used to synthesize four predicted results to ensure the robustness of the predicted results. To establish the optimal trading strategy, this study considers the maximum expected return as the goal to develop a single-objective optimization model and historical five-day price volatility as a risk factor. In this study, ant colony, simulated annealing, and genetic algorithms are used to solve the single-objective optimization model. Finally, we conclude that Bitcoin, similar to other financial assets, e.g., gold, is sensitive to shocks and volatile and possesses a relatively quiet cycle. When Bitcoin has an asymmetric impact, Bitcoin and gold can equally treat transactions.
Abstract There has been a tremendous growth in cryptocurrencies, which has challenged policy makers around the globe. We obtain millisecond data of some of the most frequently traded cryptocurrencies – bitcoin, ethereum, ripple, litecoin and dash – and two cryptocurrency indices – CRIX and CCI30 – to examine their profitability. Our profitability findings suggest that cryptocurrency traders generate significant profits after considering reasonable transaction costs. We also observe that cryptocurrency market participants can expand and sustain the levels of profitability levels in the subsequent trading activity. Our robustness checks with more recent post‐Covid data are consistent with the initial profitability findings, although we observe lower levels of profits for the two indices and weaker profit persistency for all digital assets.
Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production, increasing network hashrates and electricity consumption. Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity. These changes prompt cryptocurrency miners to become new investors, leading to cryptocurrency price increases. The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified. Hence, this research thus utilizes July 31 2015-July 12 2019 data from 13 cryptocurrencies to investigate the short- and long-run causal effects between cryptocurrency transaction and electricity consumption. Particularly, we consider structural breaks induced by external shocks through stationary analysis and comovement relationships. Over the examined time period, we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks, with prices trending together with hashrates. Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption. Therefore, owing to the computational power deployed to wherever high profit is found, transactions are vital determinants of electricity consumption.
Blockchain can support the food supply chain in several aspects. Particularly, food traceability and trading across pre-existing contracts can make the supply chain fast, error-free, and support in detecting potential fraud. A proper algorithm, keeping in mind specific geographic, demographic, and additional essential parameters, would let the automated market maker (AMM) supply ample liquidity to pre-determined orders. AMMs are usually run by a set of sequential algorithms called a ‘smart contract’ (SM). Appropriate use of SM reduces food waste, contamination, extra or no delivery in due course, and, possibly most significantly, increases traceability. However, SM has definite vulnerabilities, making it less adaptable at times. We are investigating whether they are genuinely vulnerable during stressful periods or not. We considered seven SM platforms, namely, Fabric, Ethereum (ETH), Waves, NEM (XEM), Tezos (XTZ), Algorand (ALGO), and Stellar (XLM), as the proxies for food supply-chain-based smart contracts from 29 August 2021 to 5 October 2022. This period coincides with three stressed events: Delta (Covid II), Omicron (Covid III), and the Russian invasion of Ukraine. We found strong traces of risk transmission, comovement, and interdependence of SM return among the diversified SMs; however, the SMs focused on the food supply chain ended up as net receivers of shocks at both of the extreme tails. All these SMs share a stronger connection in both positive shocks (bullish) and negative shocks (bearish).
Adel Benhamed, Ahlem Selma Messai, Ghassen El Montasser
Since Bitcoin has frequently witnessed price fluctuations and high volatility, the factors influencing its returns and volatility is an important research subject. To accomplish this goal, we applied the Gets reduction method which has a good reputation compared to other competing approaches in terms of the statistical apparatus available for a repeated search to determine the final set of determinants and the consideration of location shifts. We found that the reduced set of explanatory variables that affects Bitcoin returns is composed of Twitter-based economic uncertainty, gold return, the return of the Euro/USD exchange rate, the return of the US Nasdaq stock exchange index, market capitalization, and Bitcoin mining difficulty. In contrast, the volatility of Bitcoin is affected by only lagged terms of the ARCH effect and the volume of this cryptocurrency.
Aims: To determine the investment feasibility of evaluating cryptocurrency opportunities as an investment product under the possibility of crypto price valuation selection. The study analyzes three indicators: asset price returns in unrelated time, selection of cryptocurrency investment price weights, and crypto price forward contract opportunities on ARCH-GARCH probability forecasts in the selection of price valuations by individual cryptocurrency prices. Study Design: Quantitative research. Place and Duration of Study: The period from 10 September 2021 to 4 September 2022 using sample data downloaded from the Yahoo Finance website database with metric data retrieval bound in amount, data quantity, or distance relative to writing opportunities to examine the distribution of the amount of research data. Methodology: This study employed Bitcoin (BTC), Ethereum (ETH), and Tether (USDT) cryptocurrencies as the research objects with used panel and multiple regression analysis methodologies and using forecasting the appropriate ARCH and GARCH methods Results: The results show that the prediction of future crypto price selection in BTC and ETH tokens has a probability of 78.6% and 59.6%, respectively. The study highlights the prediction of future BTC and ETH price selection with 79.21% and 78.64% forecast results as found in the ARCH-GARCH(1, 0, 1) technique. Meanwhile, USDT token has no possibility to be forecasted in the future, leaving a 7.3% possibility of crypto price selection under probability by investors in the form of high (or different) price fluctuation inequalities. Conclusion: Conclusions could state that the partial (combined) selection of crypto coin price assessments and individual crypto assets can reduce the expected return from the selection of the asset price so that this form of investment in crypto assets can reduce the level of observation of return on wealth from crypto assets for investors especially in expecting the chance on that investment.
In this paper, we investigate the co-dependence and portfolio value-at-risk of cryptocurrencies, with the Bitcoin, Ethereum, Litecoin and Ripple price series from January 2016 to December 2021, covering the crypto crash and pandemic period, using the generalized autoregressive score (GAS) model. We find evidence of strong dependence among the virtual currencies with a dynamic structure. The empirical analysis shows that the GAS model smoothly handles volatility and correlation changes, especially during more volatile periods in the markets. We perform a comprehensive comparison of out-of-sample probabilistic forecasts for a range of financial assets and backtests and the GAS model outperforms the classic DCC (dynamic conditional correlation) GARCH model and provides new insights into multivariate risk measures.
Abstract This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries: the developed G7 and the emerging BRICS. The methodology adopts the regular (R)-vine copula and compares it with two benchmark models: the multivariate t copula and the dynamic conditional correlation (DCC) GARCH model. Moreover, this study examines whether the Bitcoin meltdown of 2013, selloff of 2018, COVID-19 pandemic, 2021 crash, and the Russia-Ukraine conflict impact the linkage with conventional currencies. The results indicate that for both currency baskets, R-vine beats the benchmark models. Hence, the dependence is better modeled by providing sufficient information on the shock transmission path. Furthermore, the cross-market linkage slightly increases during the Bitcoin crashes, and reaches significant levels during the 2021 and 2022 crises, which may indicate the end of market isolation of the virtual currency.